Information Processing Method, Apparatus, Device, and Medium

Abstract

The present disclosure provides an information processing method, which can be applied to the field of big data technology. The information processing method includes: constructing a first relationship information matrix according to the entity information and relationship information stored in the ontology model, wherein the first relationship information in the first relationship information matrix has an entity information identifier of the entity information associated with the first relationship information; determining, from the first relationship information matrix, target first relationship information corresponding to the target entity information according to the entity information identifier of the target entity information, to obtain a target first relationship information set, wherein the target entity information has an associated relationship with a target ontology model in the ontology model; and constructing a target knowledge graph according to the target entity information and the target first relationship information set. The present disclosure also provides an information processing device, equipment, storage medium and program product.

Description

Technical Field

[0001] The present disclosure relates to the field of big data, and particularly to an information processing method, apparatus, device, medium, and program product. Background Art

[0002] With the rapid development of technology, more and more people can obtain relevant information through terminal devices such as computers and mobile phones. For example, users can conduct online learning by operating terminal devices, thereby conveniently obtaining the required knowledge. With the surge in the number of users of online learning, relevant online education platforms have correspondingly launched a large number of learning materials such as educational courses and e-books to meet the actual needs of users for obtaining knowledge.

[0003] In the process of implementing the inventive concept of the present disclosure, the inventors found that in the relevant learning materials launched by relevant platforms, the information context is relatively messy, it is difficult to clearly display relevant important information, and it is difficult to meet the personalized learning needs or information acquisition needs of users. Summary of the Invention

[0004] In view of the above problems, the present disclosure provides an information processing method, apparatus, device, medium, and program product.

[0005] According to a first aspect of the present disclosure, there is provided an information processing method, including:

[0006] Construct a first relationship information matrix according to the entity information and relationship information stored in the ontology model, wherein the first relationship information in the first relationship information matrix has an entity information identifier of the entity information associated with the first relationship information;

[0007] Determine, from the first relationship information matrix, target first relationship information corresponding to the target entity information according to the entity information identifier of the target entity information, to obtain a target first relationship information set, wherein the target entity information is associated with a target ontology model in the ontology model; and

[0008] Construct a target knowledge graph according to the target entity information and the target first relationship information set.

[0009] According to an embodiment of the present disclosure, there are N ontology models, N≥2;

[0010] Constructing a first relationship information matrix according to the entity information and relationship information stored in the ontology model includes:

[0011] Add an entity information identifier corresponding to the relationship information to the relationship information in the ontology model according to the correspondence between the entity information and relationship information stored in each of the N ontology models, to obtain updated first relationship information;

[0012] Construct the first relationship information matrix according to the first relationship information of each of the N above-mentioned ontology models.

[0013] According to an embodiment of the present disclosure, the entity information associated with the above-mentioned first relationship information includes first entity information and second entity information, and the above-mentioned first entity information and the above-mentioned second entity information establish an association relationship through the above-mentioned first relationship information;

[0014] Constructing the first relationship information matrix according to the first relationship information of each of the N above-mentioned ontology models includes:

[0015] Construct an initial first relationship information matrix according to the first entity information identifier and the second entity information identifier of each of the N above-mentioned ontology models. The above-mentioned initial first relationship information matrix includes a first identifier row array composed of the above-mentioned first entity information identifiers, and a first identifier column array composed of the above-mentioned second entity information identifiers;

[0016] According to the matching result of the above-mentioned first relationship information with the first entity information identifier and the second entity information identifier in the above-mentioned initial first relationship information matrix, store the above-mentioned first relationship information into the above-mentioned initial first relationship information matrix to obtain the above-mentioned first relationship information matrix.

[0017] According to an embodiment of the present disclosure, the above-mentioned information processing method further includes:

[0018] Perform information extraction on the basic information to obtain entity information and relationship information corresponding to the above-mentioned entity information, wherein the above-mentioned basic information has an ontology identifier for characterizing an association relationship with the above-mentioned ontology model;

[0019] Construct an information triple according to the above-mentioned entity information and the above-mentioned relationship information, wherein the above-mentioned information triple includes relationship information and two entity information associated through the above-mentioned relationship information; and

[0020] Construct an ontology model with the above-mentioned ontology identifier according to the above-mentioned information triple and the above-mentioned ontology identifier.

[0021] According to an embodiment of the present disclosure, constructing an information triple according to the above-mentioned entity information and the above-mentioned relationship information includes:

[0022] Determine two initial entity information associated through the above-mentioned relationship information according to the above-mentioned relationship information;

[0023] Construct an initial information triple according to the two above-mentioned initial entity information and the above-mentioned relationship information; and

[0024] In the case where the above-mentioned initial information triples include multiple, merge the multiple above-mentioned initial information triples to obtain the merged above-mentioned information triple.

[0025] According to an embodiment of the present disclosure, the above-mentioned target entity information includes target first entity information and target second entity information, and the target first entity information and the target second entity information are associated through the target first relationship information in the above-mentioned target first relationship information set;

[0026] The above information processing method further includes:

[0027] Display the above-mentioned target knowledge graph on an interaction page, where the above-mentioned target knowledge graph includes a first node object representing the above-mentioned target first entity information, a first edge relationship object representing the above-mentioned target first relationship information, and a second node object representing the above-mentioned target second entity information;

[0028] In response to a query request for the above-mentioned second node object, determine the target second entity information corresponding to the above-mentioned second node object as the new target first entity information; and

[0029] Construct a new target knowledge graph according to the new target first entity information.

[0030] According to an embodiment of the present disclosure, the above-mentioned target knowledge graph is applied to an education learning platform;

[0031] The above information processing method further includes:

[0032] Extract information from basic education information to obtain the above-mentioned entity information and the above-mentioned relationship information;

[0033] Construct an education learning ontology model based on the above-mentioned entity information and the above-mentioned relationship information, where the above-mentioned target knowledge graph is constructed according to the above-mentioned education learning ontology model.

[0034] According to an embodiment of the present disclosure, the above-mentioned basic education information includes at least one of the following:

[0035] Educational curriculum information, learner interaction information, learner attribute information.

[0036] A second aspect of the present disclosure provides an information processing device, including:

[0037] A first construction module, configured to construct a first relationship information matrix according to the entity information and relationship information stored in the ontology model, where the first relationship information in the above-mentioned first relationship information matrix has an entity information identifier of the entity information associated with the above-mentioned first relationship information;

[0038] A first determination module, configured to determine, according to the entity information identifier of the target entity information, target first relationship information corresponding to the target entity information from the first relationship information matrix, so as to obtain a set of target first relationship information, where the target entity information has an association relationship with a target ontology model in the ontology model; and

[0039] A second construction module, configured to construct a target knowledge graph according to the target entity information and the set of target first relationship information.

[0040] A third aspect of the present disclosure provides an electronic device, including: one or more processors; a memory for storing one or more programs, where, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the above information processing method.

[0041] A fourth aspect of the present disclosure further provides a computer-readable storage medium, on which executable instructions are stored, and when the instructions are executed by a processor, the processor is caused to execute the above information processing method.

[0042] A fifth aspect of the present disclosure further provides a computer program product, including a computer program, and when the computer program is executed by a processor, the above information processing method is implemented. Description of the Drawings

[0043] Through the following description of the embodiments of the present disclosure with reference to the drawings, the above content and other objects, features and advantages of the present disclosure will become clearer. In the drawings:

[0044] Figure 1 Schematically shows an application scenario diagram of the information processing method and device according to an embodiment of the present disclosure;

[0045] Figure 2 Schematically shows a flowchart of the information processing method according to an embodiment of the present disclosure;

[0046] Figure 3 Schematically shows a flowchart of the information processing method according to another embodiment of the present disclosure;

[0047] Figure 4 Schematically shows an application scenario diagram of the ontology association relationship between ontology models according to an embodiment of the present disclosure;

[0048] Figure 5 Schematically shows a flowchart of the information processing method according to another embodiment of the present disclosure;

[0049] Figure 6 Schematically shows an application scenario diagram of the information processing method according to another embodiment of the present disclosure;

[0050] Figure 7A Schematically shows an application scenario diagram of an information processing method according to another embodiment of the present disclosure;

[0051] Figure 7B Schematically shows an application scenario diagram of an information processing method according to still another embodiment of the present disclosure;

[0052] Figure 8 Schematically shows a structural block diagram of an information processing apparatus according to an embodiment of the present disclosure; and

[0053] Figure 9 Schematically shows a block diagram of an electronic device suitable for implementing the information processing method according to an embodiment of the present disclosure. Detailed implementation manners

[0054] Hereinafter, embodiments of the present disclosure will be described with reference to the accompanying drawings. However, it should be understood that these descriptions are merely exemplary and are not intended to limit the scope of the present disclosure. In the following detailed description, for the sake of explanation, many specific details are set forth to provide a thorough understanding of the embodiments of the present disclosure. However, it is obvious that one or more embodiments can also be implemented without these specific details. In addition, in the following description, descriptions of well-known structures and technologies are omitted to avoid unnecessarily confusing the concepts of the present disclosure.

[0055] The terms used herein are merely for describing specific embodiments and are not intended to limit the present disclosure. The terms "including", "comprising", etc. used herein indicate the presence of the described features, steps, operations, and / or components, but do not exclude the presence or addition of one or more other features, steps, operations, or components.

[0056] All terms used herein (including technical and scientific terms) have the meanings commonly understood by those skilled in the art, unless otherwise defined. It should be noted that the terms used herein should be interpreted as having a meaning consistent with the context of this specification and should not be interpreted in an idealized or overly rigid manner.

[0057] In the case of using expressions such as "at least one of A, B, and C", generally, it should be interpreted according to the meaning commonly understood by those skilled in the art (for example, "a system having at least one of A, B, and C" should include, but is not limited to, a system having only A, only B, only C, having A and B, having A and C, having B and C, and / or having A, B, and C).

[0058] In the technical solution of the present disclosure, the processing of the collection, storage, use, processing, transmission, provision, disclosure, and application of the user's personal information involved all comply with the provisions of relevant laws and regulations, take necessary confidentiality measures, and do not violate public order and good customs.

[0059] In the technical solution of the present disclosure, the authorization or consent of the user is obtained before obtaining or collecting the user's personal information.

[0060] Embodiments of the present disclosure provide an information processing method, apparatus, device, medium, and program product. The information processing method includes: constructing a first relationship information matrix according to the entity information and relationship information stored in the ontology model, where the first relationship information in the first relationship information matrix has an entity information identifier of the entity information associated with the first relationship information; determining, from the first relationship information matrix, target first relationship information corresponding to the target entity information according to the entity information identifier of the target entity information, to obtain a target first relationship information set, where the target entity information has an association relationship with the target ontology model in the ontology model; and constructing a target knowledge graph according to the target entity information and the target first relationship information set.

[0061] According to the embodiments of the present disclosure, by constructing the first relationship information matrix, the entity information and relationship information stored in the ontology model can be structurally stored according to the association relationship between the entity information and the relationship information, and the entity information is represented by the entity information identifier, so that the storage space for storing the entity information can be saved. According to the entity information identifier of the target entity information, the target first relationship information corresponding to the target entity information is determined from the first relationship information matrix, and then, on the basis of meeting the user's personalized selection of the target entity information or the target first relationship information, a structured information display method can be quickly constructed through the constructed target knowledge graph, so as to clearly display relevant important information, save storage space, and at the same time take into account the user's personalized needs and improve the query efficiency of relevant information.

[0062] Figure 1 Schematically shows an application scenario diagram of the information processing method and apparatus according to the embodiments of the present disclosure.

[0063] As Figure 1 shown, the application scenario 100 according to this embodiment may include terminal devices 101, 102, 103, a network 104, and a server 105. The network 104 is used to provide a medium for communication links between the terminal devices 101, 102, 103 and the server 105. The network 104 may include various connection types, such as wired, wireless communication links, or fiber optic cables, etc.

[0064] Users can use the terminal devices 101, 102, 103 to interact with the server 105 through the network 104 to receive or send messages, etc. Various communication client applications may be installed on the terminal devices 101, 102, 103, such as shopping applications, web browser applications, search applications, instant messaging tools, email clients, social platform software, etc. (only as examples).

[0065] The terminal devices 101, 102, and 103 can be various electronic devices with a display screen and supporting web browsing, including but not limited to smartphones, tablets, laptop computers, desktop computers, and so on.

[0066] The server 105 can be a server that provides various services, such as a background management server (only for example) that supports the websites browsed by users using the terminal devices 101, 102, and 103. The background management server can analyze and process data such as user requests received, and feedback the processing results (such as web pages, information, or data obtained or generated according to user requests) to the terminal devices.

[0067] It should be noted that the information processing method provided by the embodiments of the present disclosure can generally be executed by the server 105. Correspondingly, the information processing device provided by the embodiments of the present disclosure can generally be set in the server 105. The information processing method provided by the embodiments of the present disclosure can also be executed by a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and / or the server 105. Correspondingly, the information processing device provided by the embodiments of the present disclosure can also be set in a server or a server cluster different from the server 105 and capable of communicating with the terminal devices 101, 102, 103 and / or the server 105.

[0068] It should be understood that Figure 1 the numbers of the terminal devices, the network, and the server in

[0069] are merely illustrative. According to the implementation requirements, there can be any number of terminal devices, networks, and servers. Figure 1 The following will be based on Figures 2 to 7B the described scenario, and will describe in detail the information processing method of the disclosed embodiments through

[0070] Figure 2 FIG. schematically shows a flowchart of the information processing method according to an embodiment of the present disclosure.

[0071] As Figure 2 shown, the information processing method of this embodiment includes operation S210 to operation S230.

[0072] In operation S210, according to the entity information and relationship information stored in the ontology model, a first relationship information matrix is constructed, where the first relationship information in the first relationship information matrix has the entity information identifier of the entity information associated with the first relationship information.

[0073] According to an embodiment of the present disclosure, an ontology model may include a model for describing general concepts of knowledge in a certain domain. The ontology model may contain a set of abstract concepts for constructing a knowledge graph, as well as the relationships between the abstract concepts. The entity information in the ontology model may include specific instances of the abstract concepts of the ontology model, and the relationship information in the ontology model may be used to characterize the relationship characteristics between different entity information in the ontology model.

[0074] For example, the entity information contained in the ontology model is "Java" and "programming language", and the relationship information "belongs to" can be used to characterize the relationship characteristics between the entity information "Java" and the entity information "programming language".

[0075] According to an embodiment of the present disclosure, the matrix elements of the first relationship information matrix (i.e., the first relationship information) may include the relationship information stored in the ontology model. Since the relationship information can characterize the relationship characteristics between different entity information, the first relationship information in the first relationship information matrix can characterize the relationship characteristics between the entity information in the ontology model through the entity information identifiers of the different entity information represented.

[0076] For example, the first relationship information "belongs to" may have the entity information identifiers of the entity information "Java" and "programming language".

[0077] In operation S220, according to the entity information identifier of the target entity information, determine the target first relationship information corresponding to the target entity information from the first relationship information matrix, and obtain a set of target first relationship information, where the target entity information has an association relationship with the target ontology model in the ontology model.

[0078] In operation S230, construct a target knowledge graph according to the target entity information and the set of target first relationship information.

[0079] According to an embodiment of the present disclosure, the entity information identifier of the target entity information can be used to determine the target first relationship information having the entity information identifier of the target entity information from the first relationship information matrix, and obtain a set of target first relationship information composed of the target first relationship information. A pointer can be assigned to the target entity information, and the pointer points to the set of target first relationship information. Thus, the other entity information associated with the target entity information through the target first relationship information can be determined by using the target entity information and the set of target first relationship information, and then the target knowledge graph for the target entity information can be constructed.

[0080] Since the target entity has an associated relationship with the target ontology model, the target knowledge graph for the target entity information can be a knowledge graph within the knowledge domain framework of the target ontology model. It can combine the target ontology model to structurally display relevant important information, thereby clearly showing the relevant important information to meet the personalized needs of users and improve the query efficiency.

[0081] According to an embodiment of the present disclosure, by constructing a first relationship information matrix, the entity information and relationship information stored in the ontology model can be structurally stored according to the association relationship between the entity information and the relationship information, and the entity information is represented by an entity information identifier, thereby saving the storage space for storing entity information. According to the entity information identifier of the target entity information, the target first relationship information corresponding to the target entity information is determined from the first relationship information matrix. Furthermore, on the basis of satisfying the user's personalized selection of the target entity information or the selection of the target first relationship information, a structured information display method can be quickly constructed through the constructed target knowledge graph, thereby clearly showing the relevant important information. While saving storage space, it also takes into account the personalized needs of users and improves the query efficiency of relevant information.

[0082] Figure 3 Schematically shows a flowchart of an information processing method according to another embodiment of the present disclosure.

[0083] As Figure 3 shown, the information processing method of this embodiment may further include operation S310 to operation S330.

[0084] In operation S310, information extraction is performed on the basic information to obtain entity information and relationship information corresponding to the entity information, where the basic information has an ontology identifier for characterizing an associated relationship with the ontology model.

[0085] According to an embodiment of the present disclosure, the basic information may include information corresponding to the knowledge framework of the ontology model, and the basic information may include information in any format such as text information, voice information, image information, video information, etc.

[0086] According to an embodiment of the present disclosure, relevant information extraction methods can be used to perform information extraction on the basic information. For example, operations such as word segmentation, named entity recognition, dependency syntactic analysis, or part-of-speech tagging can be performed on the basic information to achieve information extraction.

[0087] In operation S320, an information triple is constructed according to the entity information and the relationship information, where the information triple includes the relationship information and two entity information associated by the relationship information.

[0088] According to an embodiment of the present disclosure, the information triple may include the RDF (Resource Description Framework) triple in the related art, and the information triple may have an information structure form of "entity - relationship - entity". For example, in the case where the information triple is ("Java" - "belongs to" - "programming language"), the entity information "Java" and "programming language" can establish an association relationship through the relationship information "belongs to".

[0089] It should be noted that the relationship information in the related information triple can represent the association relationship of any attribute. For example, it can represent the "subject - predicate - object" grammatical structure relationship, but not limited to this, and it can also represent other forms of grammatical structure relationships, which can be designed by those skilled in the art according to the actual situation.

[0090] In operation S330, an ontology model with an ontology identifier is constructed according to the information triple and the ontology identifier.

[0091] According to an embodiment of the present disclosure, since the basic information has an ontology identifier, the ontology identifier associated with the information triple can be determined according to the ontology identifier of the basic information. Furthermore, the information triple extracted from the basic information can be stored in the same ontology model as the ontology identifier of the basic information, thereby completing the construction process of the ontology model.

[0092] It should be noted that the basic information may have one or more ontology identifiers, that is, the basic information can belong to the knowledge frameworks corresponding to different ontology models. Therefore, the knowledge frameworks corresponding to the ontology models can be enriched according to the extracted entity information and relationship information, laying a foundation for constructing the target knowledge graph subsequently.

[0093] According to an embodiment of the present disclosure, the basic information may include structured information, unstructured information, and semi - structured information. The structured information can be stored in a relational database, and thus the entity information and relationship information can be extracted. For the unstructured information first, operations such as word segmentation, part - of - speech tagging, and named entity recognition can be performed on the unstructured information, and the entity relationship extraction method based on dependency syntactic analysis can be used to complete the extraction task of the entity information and relationship information. At the same time, the construction of the ontology model can also be realized based on the related ontology model construction technology. For example, the Protege4.3 software can be used to construct the ontology model and visually display the ontology model.

[0094] According to embodiments of the present disclosure, for the structured characteristics of a knowledge domain, multiple ontology models can be constructed, and ontology association relationships between the multiple ontology models can be constructed, so that the knowledge scope of the knowledge domain can be structurally characterized by using the ontology association relationships between the ontology models. The ontology association relationships between the ontology models can include inheritance relationships, instance relationships, whole-part relationships, equivalence relationships, but are not limited thereto. They can also include location relationships for describing the relationship between an event and the location where the event occurs, time relationships for describing the relationship between an event and the time when the event occurs, cause relationships for describing the relationship between the cause of an event and the event, etc. Those skilled in the art can establish corresponding ontology association relationships according to actual needs.

[0095] The extraction of ontology association relationships can adopt any one or a combination of multiple of the structured table method, the statistical method, or the linguistic rule method. Since the ontology vocabulary in the ontology model has good structural characteristics, the structured table method can be directly used to extract ontology association relationships to improve the speed of constructing ontology association relationships.

[0096] According to embodiments of the present disclosure, for operation S320, constructing an information triple according to entity information and relationship information may include the following operations.

[0097] Determine two initial entity information associated by the relationship information according to the relationship information; construct an initial information triple according to the two initial entity information and the relationship information; and when there are multiple initial information triples, merge the multiple initial information triples to obtain a merged information triple.

[0098] According to embodiments of the present disclosure, for multiple initial information triples with the same "entity-relationship-entity", by merging the multiple initial information triples into the same information triple, redundant information triples can be deleted to save the storage space for storing information triples.

[0099] According to embodiments of the present disclosure, the target knowledge graph can be applied to an education learning platform.

[0100] The information processing method may further include the following operations.

[0101] Perform information extraction on basic education information to obtain entity information and relationship information; construct an education learning ontology model based on the entity information and the relationship information, wherein the target knowledge graph is constructed according to the education learning ontology model.

[0102] According to embodiments of the present disclosure, the basic education information may include basic information related to the education field in related education fields. For example, when the education field is the safety knowledge education field, the basic education information may include safety training course information, safety law and regulation text information, safety accident case training information, and so on.

[0103] According to an embodiment of the present disclosure, by extracting information from basic education information, the obtained entity information and relationship information can enrich the knowledge framework corresponding to the educational learning ontology model in the education field, laying a foundation for constructing a target knowledge graph subsequently.

[0104] According to an embodiment of the present disclosure, the basic education information includes at least one of the following: educational curriculum information, learner interaction information, and learner attribute information.

[0105] According to an embodiment of the present disclosure, the educational curriculum information may include course names, course classifications, course content information, etc. The learner interaction information may include feedback information of learners participating in the educational curriculum, such as learner like behavior information, learner favorite behavior information, learner rating behavior information, learner learning record information, etc.

[0106] According to an embodiment of the present disclosure, the learner attribute information may include attribute information of learners participating in the educational curriculum, such as learner gender information, learner age information, learner occupational attribute information, learner job level information, etc.

[0107] Figure 4 A schematic application scenario diagram showing the ontology association relationship between ontology models according to an embodiment of the present disclosure is shown.

[0108] As Figure 4 shown, in the application scenario of this embodiment, the ontology model may include the educational learning ontology model in the above embodiment. The ontology association relationship between ontology models may include a parallel relationship and a subordinate relationship.

[0109] For example, the ontology association relationship between the ontology model "Safety Matters 421" and the ontology model "Safety Protection Learning Course 410" is a subordinate relationship, and the ontology association relationships among the ontology models "Safety Matters 421", "Safety Incident Location 422", "Safety Incident Structure 423", "Safety Incident Handling Result 424", "Learner Position Classification 425", and "Learner Position Hierarchy 426" are parallel relationships.

[0110] Correspondingly, the ontology association relationships between the ontology models "Fire Protection 4311", "Food Safety 4312" and the ontology model "Safety Matters 421" are subordinate relationships. The ontology association relationship between the ontology model "Fire Protection 4311" and "Food Safety 4312" is a subordinate relationship.

[0111] By constructing the ontological association relationships between ontological models, the ontological association relationships between ontological models can be used to structurally represent the knowledge scope of the knowledge domain, and the ontological association relationships, as well as the entity information and relationship information stored in each ontological model, are used to construct a target knowledge graph. Furthermore, through the target knowledge graph, the knowledge graphs corresponding to different ontological models can be clearly displayed, thereby improving the query efficiency of information in related fields and further meeting the personalized needs of users.

[0112] It should be understood that Figure 4 the ontological model in is the ontological name of this ontological model, and the entity information and relationship information corresponding to this ontological model can be stored in the ontological model with the corresponding ontological name. Figure 4 the ontological model in only schematically shows the ontological association relationships between ontological models and the names of ontological models. However, in practical applications, the ontological association relationships between ontological models and the ontological names can be designed. The embodiments of the present disclosure do not limit the ontological association relationships and the names of ontological models.

[0113] It should be noted that before obtaining relevant basic information for constructing an ontological model, the consent or authorization of relevant users can be obtained first. For example, when the basic information is a list of students, the consent or authorization of the corresponding students in the list of students can be obtained, so as to construct an ontological model according to the list of students.

[0114] According to the embodiments of the present disclosure, there can be N ontological models, where N≥2.

[0115] Figure 5 Schematically shows a flowchart of an information processing method according to another embodiment of the present disclosure.

[0116] As Figure 5 shown, for operation S210, constructing the first relationship information matrix according to the entity information and relationship information stored in the ontological model can include operations S510 to S520.

[0117] In operation S510, according to the corresponding relationships between the entity information and relationship information respectively stored in the N ontological models, entity information identifiers corresponding to the relationship information are added to the relationship information in the ontological model to obtain the updated first relationship information.

[0118] In operation S520, according to the first relationship information of each of the N ontological models, a first relationship information matrix is constructed.

[0119] According to an embodiment of the present disclosure, for example, in the case where entity information and relationship information are stored in the form of information triples in an ontology model, two entity information in the information triples can be respectively set with entity information identifiers, and the two entity information identifiers can be added to the relationship information in the information triples, so as to obtain updated first relationship information. Furthermore, the first relationship information of each of the N ontology models can be used, and the first relationship information is used as an element of the first relationship information matrix, so as to construct the first relationship information matrix.

[0120] According to an embodiment of the present disclosure, the entity information associated with the first relationship information includes first entity information and second entity information, and the first entity information and the second entity information are associated through the first relationship information.

[0121] Operation S520, constructing the first relationship information matrix according to the first relationship information of each of the N ontology models may include the following operations.

[0122] According to the first entity information identifier and the second entity information identifier of the first relationship information of each of the N ontology models, construct an initial first relationship information matrix, the initial first relationship information matrix includes a first identifier row array composed of the first entity information identifiers, and a first identifier column array composed of the second entity information identifiers; according to the matching result of the first relationship information with the first entity information identifier and the second entity information identifier in the initial first relationship information matrix, store the first relationship information into the initial first relationship information matrix to obtain the first relationship information matrix.

[0123] Figure 6 Schematically shows an application scenario diagram of an information processing method according to another embodiment of the present disclosure.

[0124] As Figure 6 shown, in this embodiment, according to the correspondence between the entity information and the relationship information stored in each of the N ontology models, add the first entity information identifier of the first entity information and the second entity information identifier of the second entity information to the relationship information in each information triple stored in each ontology model, so as to obtain updated first relationship information.

[0125] The first entity information identifiers can be arranged in a preset order to form a first identifier row array 611. In the first identifier row array 611, n first identifier array elements can be respectively deployed according to the sorting of 1, 2, 3, 4,... n-1 to n, and each first identifier array element respectively represents the corresponding first entity information.

[0126] Correspondingly, the second entity information identifiers can be arranged in a preset order to form the first identifier column array 612. In the first identifier column array 612, n second identifier array elements can also be deployed respectively according to the sorting of 1, 2, 3, 4, … n-1 to n, and each second identifier array element represents the corresponding second entity information respectively. Thus, an initial first relationship information matrix with storage positions for storing the first relationship information can be constructed based on the first identifier row array 611 and the first identifier column array 612.

[0127] After constructing the first identifier row array 611 and the first identifier column array 612, match the first identifier array elements of the first identifier row array 611 according to the first entity information identifier of the first relationship information, and match the second identifier array elements in the first identifier column array 612 according to the second entity information identifier of the first relationship information. After obtaining the matching result, store the relationship information content of the first relationship information into the initial first relationship information matrix, and then the first relationship information matrix 610 storing the first relationship information can be obtained.

[0128] Then, according to the entity information identifier 20218 of the target first entity information 621, the target first relationship information corresponding to the target first entity information 621 can be determined from the first relationship information matrix 610, so as to obtain the target first relationship information set 622. Furthermore, the second entity information identifier set 623 of the target second entity information corresponding to the target first entity information 621 can be determined from the first relationship information matrix 610 by using the target first relationship information set 622. Thus, a target knowledge graph can be constructed according to the target first entity information 621, the target first relationship information set 622, and the second entity information identifier set 623 of the target second entity information.

[0129] In an embodiment of the present disclosure, the target first relationship information set 622 may include that the target first relationship information is "located in", "caused by", "range", "caused", and "belongs to". The second entity information identifier set 623 of the target second entity information may include that the second entity information identifier is "3028", "4838", "5739", "6318", and "11132".

[0130] According to the embodiment of the present disclosure, the target entity information includes the target first entity information and the target second entity information, and the target first entity information and the target second entity information are associated through the target first relationship information in the target first relationship information set.

[0131] The information processing method may further include the following operations.

[0132] Display a target knowledge graph on an interactive page, where the target knowledge graph includes a first node object representing target first entity information, a first edge relationship object representing target first relationship information, and a second node object representing target second entity information; in response to a query request for the second node object, determine the target second entity information corresponding to the second node object as the new target first entity information; and construct a new target knowledge graph according to the new target first entity information.

[0133] According to an embodiment of the present disclosure, by using the target second entity information corresponding to the target second node object as the new target first entity information, a new target knowledge graph can be constructed for the new target first entity information, thereby expanding the knowledge context of the target knowledge graph, meeting the personalized query needs of users, and clearly displaying the knowledge structure of entity information and relationship information through the new target knowledge graph for the convenience of users' learning or query.

[0134] According to an embodiment of the present disclosure, it is also possible to construct a new target knowledge graph when detecting the target first edge relationship represented by the first edge relationship information, so as to display the knowledge structure of entity information and relationship information from another perspective and enhance the readability and clarity of the target knowledge graph.

[0135] Figure 7A Schematically shows an application scenario diagram of an information processing method according to another embodiment of the present disclosure.

[0136] Figure 7B Schematically shows an application scenario diagram of an information processing method according to still another embodiment of the present disclosure.

[0137] Combined Figure 7A with Figure 7B As shown, in the first display state Y710 of the interactive page, a target knowledge graph for which the target first entity information is "fire" can be displayed. The target knowledge graph may include a first node object 711 representing the target first entity information as "fire", and first edge relationship objects 721, 722, 723, 724, 725 representing target first relationship information. Further, the target knowledge graph may display second node objects 731, 732, 733, 734, 735 respectively connected to the first edge relationship objects 721, 722, 723, 724, 725.

[0138] Through the target knowledge graph displayed in the first display state Y710 of the interactive page, the knowledge structure for the target first entity information "fire" can be clearly obtained, that is, it is obtained that the fire belongs to a fire accident, the fire is located in Area B, City A, the cause of the fire is electrical short - circuit, the affected area of the fire is Apartment C in Area B, and the direct property loss caused by this fire is xyz ten thousand yuan.

[0139] In the case of detecting a query request for the second node object 733 corresponding to the target second entity information "Apartment C in Area B", the target second entity information "Apartment C in Area B" can be used as the new target first entity information, and based on the new target first entity information "Apartment C in Area B", it is determined that the set of target first relationship information of the target first entity information "Apartment C in Area B" includes the target first relationship "belongs to". Using the target first relationship "belongs to" and the target first entity information "Apartment C in Area B", the new target second entity information is determined to be "Type E fire".

[0140] Then, in the second display state Y720 of the interaction page, the new target knowledge graph is displayed, and the new target knowledge graph includes a new first edge relationship object 743 and a second node object 753.

[0141] Based on the same or similar method, in response to the query request for the second node object 753 and according to the corresponding display rules, a corresponding new target knowledge graph can be generated in the interaction page, thereby expanding the knowledge context of the target knowledge graph, meeting the user's personalized query needs, and clearly displaying the knowledge structure of entity information and relationship information through the new target knowledge graph for the user to learn or query.

[0142] It should be noted that the entity information identifiers corresponding to the target first entity information and / or the target second entity information can also be displayed on the target interaction page, thereby providing effective support for relevant personnel to modify and test the process of constructing the knowledge graph.

[0143] According to the embodiments of the present disclosure, by using the information processing method provided by the embodiments of the present disclosure, on the basis of establishing the ontology association relationship of multiple ontology models, by displaying the ontology objects representing different ontology models in the interaction page and responding to the query request for the ontology object to display the corresponding target knowledge graph of the ontology model, it is convenient for users to obtain information with a clear knowledge structure from the ontology model level.

[0144] Based on the above information processing method, the present disclosure also provides an information processing device. The following will be combined with Figure 8 Describe this device in detail.

[0145] Figure 8 Schematically shows a structural block diagram of an information processing device according to an embodiment of the present disclosure.

[0146] As Figure 8 shown, the information processing device 800 of this embodiment includes a first construction module 810, a first determination module 820, and a second construction module 830.

[0147] The first construction module 810 is used to construct a first relationship information matrix according to the entity information and relationship information stored in the ontology model. Among them, the first relationship information in the first relationship information matrix has the entity information identifier of the entity information associated with the first relationship information.

[0148] The first determination module 820 is used to determine the target first relationship information corresponding to the target entity information from the first relationship information matrix according to the entity information identifier of the target entity information, and obtain a set of target first relationship information. Among them, the target entity information has an associated relationship with the target ontology model in the ontology model.

[0149] The second construction module 830 is used to construct a target knowledge graph according to the target entity information and the set of target first relationship information.

[0150] According to an embodiment of the present disclosure, there are N ontology models, where N≥2.

[0151] The first determination module includes: an addition unit and a first construction unit.

[0152] The addition unit is used to add the entity information identifier corresponding to the relationship information to the relationship information in the ontology model according to the corresponding relationship between the entity information and the relationship information stored in each of the N ontology models, and obtain the updated first relationship information.

[0153] The first construction unit is used to construct a first relationship information matrix according to the first relationship information of each of the N ontology models.

[0154] According to an embodiment of the present disclosure, the entity information associated with the first relationship information includes first entity information and second entity information, and the first entity information and the second entity information are associated through the first relationship information.

[0155] The first construction unit includes: a first construction subunit and a first storage subunit.

[0156] The first construction subunit is used to construct an initial first relationship information matrix according to the first entity information identifier and the second entity information identifier of the first relationship information of each of the N ontology models. The initial first relationship information matrix includes a first identifier row array composed of first entity information identifiers and a first identifier column array composed of second entity information identifiers.

[0157] The first storage subunit is used to store the first relationship information into the initial first relationship information matrix according to the matching result of the first relationship information with the first entity information identifier and the second entity information identifier in the initial first relationship information matrix, and obtain the first relationship information matrix.

[0158] According to an embodiment of the present disclosure, the information processing apparatus further includes: a first extraction module, a second construction module, and a third construction module.

[0159] The first extraction module is configured to perform information extraction on the basic information to obtain entity information and relationship information corresponding to the entity information, wherein the basic information has an ontology identifier for characterizing an association relationship with the ontology model.

[0160] The second construction module is configured to construct an information triple according to the entity information and the relationship information, wherein the information triple includes the relationship information and two entity information items associated by the relationship information.

[0161] The third construction module is configured to construct an ontology model with an ontology identifier according to the information triple and the ontology identifier.

[0162] According to an embodiment of the present disclosure, the second construction module includes: a first determination unit, a second construction unit, and a merging unit.

[0163] The first determination unit is configured to determine two initial entity information items associated by the relationship information according to the relationship information.

[0164] The second construction unit is configured to construct an initial information triple according to the two initial entity information items and the relationship information.

[0165] The merging unit is configured to merge multiple initial information triples to obtain a merged information triple when there are multiple initial information triples.

[0166] According to an embodiment of the present disclosure, the target entity information includes target first entity information and target second entity information, and the target first entity information and the target second entity information are associated through the target first relationship information in the target first relationship information set.

[0167] The information processing apparatus further includes: a display module, a second determination module, and a fourth construction module.

[0168] The display module is configured to display a target knowledge graph on an interactive page, wherein the target knowledge graph includes a first node object representing the target first entity information, a first edge relationship object representing the target first relationship information, and a second node object representing the target second entity information.

[0169] The second determination module is configured to, in response to a query request for the second node object, determine the target second entity information corresponding to the second node object as new target first entity information.

[0170] The fourth construction module is configured to construct a new target knowledge graph according to the new target first entity information.

[0171] According to an embodiment of the present disclosure, the target knowledge graph is applied to an education learning platform;

[0172] The information processing apparatus further includes: a second extraction module and a fifth construction module.

[0173] The second extraction module is configured to extract information from basic education information to obtain entity information and relationship information.

[0174] The fifth construction module is configured to construct an education learning ontology model based on the entity information and the relationship information, wherein the target knowledge graph is constructed according to the education learning ontology model.

[0175] According to an embodiment of the present disclosure, the basic education information includes at least one of the following: education curriculum information, learner interaction information, learner attribute information.

[0176] According to an embodiment of the present disclosure, any plurality of the first construction module 810, the first determination module 820, and the second construction module 830 may be combined and implemented in one module, or any one of them may be split into multiple modules. Alternatively, at least part of the functions of one or more of these modules may be combined with at least part of the functions of other modules and implemented in one module. According to an embodiment of the present disclosure, at least one of the first construction module 810, the first determination module 820, and the second construction module 830 may be at least partially implemented as a hardware circuit, such as a field programmable gate array (FPGA), a programmable logic array (PLA), a system on chip, a system on a substrate, a system on a package, an application specific integrated circuit (ASIC), or may be implemented by any other reasonable means of integrating or packaging circuits, etc., in hardware or firmware, or implemented in any one of the three implementation manners of software, hardware, and firmware, or in any appropriate combination of several of them. Alternatively, at least one of the first construction module 810, the first determination module 820, and the second construction module 830 may be at least partially implemented as a computer program module, and when the computer program module is run, the corresponding functions may be executed.

[0177] Figure 9 A block diagram of an electronic device suitable for implementing the information processing method according to an embodiment of the present disclosure is schematically shown.

[0178] As Figure 9As shown, the electronic device 900 according to an embodiment of the present disclosure includes a processor 901, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 902 or a program loaded from a storage section 908 into a random access memory (RAM) 903. The processor 901 can include, for example, a general microprocessor (such as a CPU), an instruction set processor, and / or a related chipset, and / or a dedicated microprocessor (such as an application specific integrated circuit (ASIC)), etc. The processor 901 can also include on-board memory for caching purposes. The processor 901 can include a single processing unit or multiple processing units for performing different actions of the method flow according to an embodiment of the present disclosure.

[0179] In the RAM 903, various programs and data required for the operation of the electronic device 900 are stored. The processor 901, the ROM 902, and the RAM 903 are connected to each other via a bus 904. The processor 901 performs various operations of the method flow according to an embodiment of the present disclosure by executing the program in the ROM 902 and / or the RAM 903. It should be noted that the program can also be stored in one or more memories other than the ROM 902 and the RAM 903. The processor 901 can also perform various operations of the method flow according to an embodiment of the present disclosure by executing the program stored in the one or more memories.

[0180] According to an embodiment of the present disclosure, the electronic device 900 may further include an input / output (I / O) interface 905, and the input / output (I / O) interface 905 is also connected to the bus 904. The electronic device 900 may further include one or more of the following components connected to the I / O interface 905: an input section 906 including a keyboard, a mouse, etc.; an output section 907 including a cathode ray tube (CRT), a liquid crystal display (LCD), etc. and a speaker, etc.; a storage section 908 including a hard disk, etc.; and a communication section 909 including a network interface card such as a LAN card, a modem, etc. The communication section 909 performs communication processing via a network such as the Internet. A drive 910 is also connected to the I / O interface 905 as needed. A removable medium 911, such as a magnetic disk, an optical disk, a magneto-optical disk, a semiconductor memory, etc., is installed on the drive 910 as needed so that a computer program read from it can be installed into the storage section 908 as needed.

[0181] The present disclosure also provides a computer-readable storage medium, which may be included in the device / apparatus / system described in the above embodiments; or may exist separately without being assembled into the device / apparatus / system. The above computer-readable storage medium carries one or more programs, and when the one or more programs are executed, the method according to an embodiment of the present disclosure is implemented.

[0182] According to an embodiment of the present disclosure, the computer-readable storage medium may be a non-volatile computer-readable storage medium, which may include, for example, but is not limited to: portable computer disks, hard disks, random access memories (RAMs), read-only memories (ROMs), erasable programmable read-only memories (EPROMs or flash memories), portable compact disk read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the above. In the present disclosure, the computer-readable storage medium may be any tangible medium that contains or stores a program, and this program may be used by or in combination with an instruction execution system, apparatus, or device. For example, according to an embodiment of the present disclosure, the computer-readable storage medium may include one or more memories other than the ROM 902 and / or RAM 903 and / or ROM 902 and RAM 903 described above.

[0183] An embodiment of the present disclosure further includes a computer program product, which includes a computer program that contains program code for executing the method shown in the flowchart. When the computer program product runs in a computer system, the program code is used to cause the computer system to implement the information processing method provided by the embodiment of the present disclosure.

[0184] When the computer program is executed by the processor 901, it executes the above functions defined in the system / apparatus of the embodiment of the present disclosure. According to an embodiment of the present disclosure, the above-described systems, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0185] In one embodiment, the computer program may rely on tangible storage media such as optical storage devices and magnetic storage devices. In another embodiment, the computer program may also be transmitted and distributed in the form of a signal on a network medium, and is downloaded and installed through the communication part 909, and / or installed from the removable medium 911. The program code included in the computer program may be transmitted by any suitable network medium, including but not limited to: wireless, wired, etc., or any suitable combination of the above.

[0186] In such an embodiment, the computer program may be downloaded and installed from the network through the communication part 909, and / or installed from the removable medium 911. When the computer program is executed by the processor 901, it executes the above functions defined in the system of the embodiment of the present disclosure. According to an embodiment of the present disclosure, the above-described systems, devices, apparatuses, modules, units, etc. may be implemented by computer program modules.

[0187] In accordance with embodiments of the present disclosure, program code for executing the computer programs provided by the embodiments of the present disclosure can be written in any combination of one or more programming languages. Specifically, these computing programs can be implemented using high-level procedural and / or object-oriented programming languages, and / or assembly / machine languages. Programming languages include, but are not limited to, such as Java, C++, Python, the "C" language, or similar programming languages. The program code can be executed entirely on the user's computing device, partially on the user's device, partially on a remote computing device, or entirely on a remote computing device or server. In cases involving a remote computing device, the remote computing device can be connected to the user's computing device through any type of network, including a local area network (LAN) or a wide area network (WAN), or it can be connected to an external computing device (e.g., by using an Internet service provider to connect through the Internet).

[0188] The flowcharts and block diagrams in the accompanying drawings illustrate the possible architectures, functions, and operations of systems, methods, and computer program products according to various embodiments of the present disclosure. In this regard, each block in the flowchart or block diagram can represent a module, a program segment, or a part of code that contains one or more executable instructions for implementing the specified logical function. It should also be noted that in some alternative implementations, the functions marked in the blocks can occur in a different order than that marked in the accompanying drawings. For example, two consecutive blocks shown can actually be executed substantially in parallel, and they can sometimes be executed in the reverse order, depending on the functions involved. It should also be noted that each block in the block diagram or flowchart, and the combination of blocks in the block diagram or flowchart, can be implemented by a dedicated hardware-based system for performing the specified functions or operations, or can be implemented by a combination of dedicated hardware and computer instructions.

[0189] Those skilled in the art can understand that the features recited in the various embodiments and / or claims of the present disclosure can be combined or combined in various ways, even if such combinations or combinations are not explicitly recited in the present disclosure. In particular, without departing from the spirit and teachings of the present disclosure, the features recited in the various embodiments and / or claims of the present disclosure can be combined and combined in various ways. All such combinations and / or combinations fall within the scope of the present disclosure.

[0190] The embodiments of the present disclosure have been described above. However, these embodiments are for illustrative purposes only and are not intended to limit the scope of the present disclosure. Although the embodiments have been described separately above, this does not mean that the measures in each embodiment cannot be used advantageously in combination. The scope of the present disclosure is defined by the appended claims and their equivalents. Without departing from the scope of the present disclosure, those skilled in the art can make various substitutions and modifications, and all such substitutions and modifications should fall within the scope of the present disclosure.

Claims

1. An information processing method, comprising: Adding an entity information identifier corresponding to the relationship information to the relationship information in the ontology model according to the correspondence between the entity information and the relationship information stored in each of the N ontology models, to obtain updated first relationship information, wherein the entity information and the relationship information are determined by information extraction from basic education information, and the basic education information includes at least one of education curriculum information, learner interaction information, and learner attribute information; Constructing an initial first relationship information matrix according to the first entity information identifier and the second entity information identifier of the first relationship information of each of the N ontology models, the initial first relationship information matrix including a first identifier row array composed of the first entity information identifiers and a first identifier column array composed of the second entity information identifiers; Storing the first relationship information into the initial first relationship information matrix according to the matching result between the first relationship information and the first entity information identifier and the second entity information identifier in the initial first relationship information matrix, to obtain the first relationship information matrix, wherein the first relationship information in the first relationship information matrix has an entity information identifier of the entity information associated with the first relationship information; Determining, from the first relationship information matrix, target first relationship information corresponding to the target entity information according to the entity information identifier of the target entity information, to obtain a target first relationship information set, wherein the target entity information has an association relationship with a target ontology model in the ontology model, and a target first entity information and a target second entity information in the target entity information are associated through the target first relationship information in the target first relationship information set; and Constructing a target knowledge graph applied to an education learning platform according to the target entity information and the target first relationship information set, the target knowledge graph including a first node object representing the target first entity information, and the first node object being a root node object of the target knowledge graph.

2. The method according to claim 1, further comprising: Performing information extraction on basic information to obtain entity information and relationship information corresponding to the entity information, wherein the basic information has an ontology identifier for representing an association relationship with the ontology model; Constructing an information triple according to the entity information and the relationship information, wherein the information triple includes relationship information and two entity information associated through the relationship information; and Constructing an ontology model with the ontology identifier according to the information triple and the ontology identifier.

3. The method according to claim 2, wherein Constructing an information triple according to the entity information and the relationship information includes: Determining two initial entity information associated through the relationship information according to the relationship information; Constructing an initial information triple according to the two initial entity information and the relationship information; and In the case where there are multiple initial information triples, merging the multiple initial information triples to obtain the merged information triple.

4. The method according to claim 1, wherein, The information processing method further comprises: Display the target knowledge graph on an interactive page, where the target knowledge graph includes a first edge relationship object representing the target first relationship information and a second node object representing the target second entity information; In response to a query request for the second node object, determine the target second entity information corresponding to the second node object as the new target first entity information; and Construct a new target knowledge graph based on the new target first entity information.

5. The method according to claim 1, wherein The information processing method further includes: Perform information extraction on basic education information to obtain the entity information and the relationship information; Based on the entity information and the relationship information, construct an education learning ontology model, wherein the target knowledge graph is constructed according to the education learning ontology model.

6. An information processing apparatus, comprising: A first construction module for constructing a first relationship information matrix according to the entity information and relationship information stored in the ontology model, wherein the first relationship information in the first relationship information matrix has an entity information identifier of the entity information associated with the first relationship information; A first determination module for determining, from the first relationship information matrix, the target first relationship information corresponding to the target entity information according to the entity information identifier of the target entity information, to obtain a target first relationship information set, wherein the target entity information has an association relationship with the target ontology model in the ontology model, and the target first entity information and the target second entity information in the target entity information are associated through the target first relationship information in the target first relationship information set; and A second construction module for constructing a target knowledge graph applied to an education learning platform according to the target entity information and the target first relationship information set, the target knowledge graph including a first node object representing the target first entity information, and the first node object being the root node object of the target knowledge graph; The first construction module is configured to: According to the correspondence between the entity information and the relationship information stored in each of the N ontology models, add an entity information identifier corresponding to the relationship information to the relationship information in the ontology model to obtain updated first relationship information, wherein the entity information and the relationship information are determined by performing information extraction on basic education information, and the basic education information includes at least one of education curriculum information, learner interaction information, and learner attribute information; Construct an initial first relationship information matrix according to the first entity information identifier and the second entity information identifier of the first relationship information of each of the N ontology models, the initial first relationship information matrix including a first identifier row array composed of the first entity information identifiers and a first identifier column array composed of the second entity information identifiers; Store the first relationship information in the initial first relationship information matrix according to the matching result of the first relationship information with the first entity information identifier and the second entity information identifier in the initial first relationship information matrix to obtain the first relationship information matrix.

7. An electronic device, comprising: One or more processors; A storage device for storing one or more programs, wherein, when the one or more programs are executed by the one or more processors, the one or more processors are caused to execute the method according to any one of claims 1 to 5.

8. A computer-readable storage medium having stored thereon executable instructions that, when executed by a processor, cause the processor to execute the method according to any one of claims 1 to 5.

9. A computer program product comprising a computer program that, when executed by a processor, implements the method according to any one of claims 1 to 5.

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