Schema model construction method and apparatus in knowledge graph, and device and storage medium

Abstract

Provided in the present disclosure are a schema model construction method and apparatus in a knowledge graph, and a device and a storage medium. The method comprises: acquiring a text knowledge base of operation and maintenance fault cases; acquiring, from the text knowledge base, schema modeling domains of operation and maintenance fault knowledge graphs, and on the basis of the text knowledge base, determining representative ontology names corresponding to the schema modeling domains, wherein the schema modeling domains comprise a fault domain, a fault representation domain, a troubleshooting item domain, a troubleshooting content domain, a troubleshooting conclusion domain, a fault root cause domain and a fault solution domain; acquiring, from the text knowledge base, extended ontology names corresponding to the representative ontology names of the schema modeling domains, and entity names corresponding to the extended ontology names; and constructing a schema model of the operation and maintenance fault knowledge graphs by using the representative ontology names corresponding to the schema modeling domains, the extended ontology names, and the entity names corresponding to the extended ontology names.

Description

Method, device, equipment and storage medium for constructing schema model in knowledge graph

[0001] CROSS-REFERENCE TO RELATED APPLICATIONS

[0002] This application claims priority to Chinese patent application CN 202310708629.0, entitled “Method, device, equipment and storage medium for constructing schema model in knowledge graph”, filed on June 14, 2023, the entire contents of which are incorporated herein by reference. Technical Field

[0003] The present invention relates to the field of communication technologies, and in particular to a method, apparatus, device, and storage medium for constructing a schema model in a knowledge graph. Background Art

[0004] Current communication network operations and maintenance (O&M) processes often require field expertise to address numerous faults. While historical databases contain fault cases, these cases are often addressed using a simple search and recommendation matching approach. These retrieved fault cases often consist of isolated case texts, requiring O&M personnel to analyze, identify, and filter the content, a complex task. Furthermore, the accuracy of the content retrieved through search and recommendation often falls short of meeting real-world field requirements.

[0005] With the development of artificial intelligence, especially the rise of knowledge graph technology, knowledge graphs can be used to model massive amounts of fault cases and construct knowledge graphs. This technology can help operations and maintenance personnel quickly and accurately locate fault cases, quickly identify potential solutions or root causes, and perform statistical analysis on all fault cases with similar symptoms. However, network operation and maintenance fault case knowledge is complex, covers a wide range of topics, and represents varying levels of content richness. Manually designed knowledge graph schemas struggle to capture all valuable knowledge.

[0006] Summary of the Invention

[0007] The present disclosure provides a method, apparatus, device and storage medium for constructing a schema model in a knowledge graph.

[0008] The present disclosure provides a method for constructing a schema model in a knowledge graph, comprising: obtaining a text knowledge base of operation and maintenance fault cases; obtaining each schema modeling domain of the operation and maintenance fault knowledge graph from the text knowledge base, and determining a representative ontology name corresponding to each schema modeling domain based on the text knowledge base, wherein the schema modeling domain includes a fault domain, a fault manifestation domain, a troubleshooting project domain, a troubleshooting content domain, a troubleshooting conclusion domain, a fault root cause domain, and a fault solution domain; obtaining an extended ontology name corresponding to the representative ontology name of each schema modeling domain, and an entity name corresponding to the extended ontology name from the text knowledge base; and constructing a schema model of the operation and maintenance fault knowledge graph using the representative ontology name, the extended ontology name, and the entity name corresponding to the extended ontology name corresponding to each schema modeling domain.

[0009] The present disclosure also provides a schema model construction device in a knowledge graph, including: a first acquisition module, used to obtain a text knowledge base of operation and maintenance fault cases; a second acquisition module, used to obtain each schema modeling domain of the operation and maintenance fault knowledge graph from the text knowledge base, and determine the representative ontology name corresponding to each schema modeling domain based on the text knowledge base, wherein the schema modeling domain includes a fault domain, a fault manifestation domain, a troubleshooting project domain, a troubleshooting content domain, a troubleshooting conclusion domain, a fault root cause domain and a fault solution domain; a third acquisition module, used to obtain the extended ontology name corresponding to the representative ontology name of each schema modeling domain, and the entity name corresponding to the extended ontology name from the text knowledge base; a construction module, used to use the representative ontology name, extended ontology name and the entity name corresponding to the extended ontology name corresponding to each schema modeling domain to construct the schema model of the operation and maintenance fault knowledge graph.

[0010] The present disclosure also provides an electronic device, comprising: at least one processor; a memory on which one or more programs are stored, and when the one or more programs are executed by the one or more processors, the one or more processors implement the schema model construction method in the knowledge graph according to the present disclosure.

[0011] The present disclosure also provides a computer-readable medium having a computer program stored thereon. When the program is executed by a processor, the processor implements the method for constructing a schema model in a knowledge graph according to the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0012] FIG1 is a flow chart of a method for constructing a schema model in a knowledge graph provided by an embodiment of the present disclosure;

[0013] FIG2 is a flow chart of a process for discovering and screening representative ontology names based on a schema modeling domain according to an embodiment of the present disclosure;

[0014] FIG3 is a schematic diagram of a process of extracting an ontology from a schema modeling domain according to an embodiment of the present disclosure;

[0015] FIG4 is a schematic diagram of the structure of a schema model construction device in a knowledge graph provided by an embodiment of the present disclosure;

[0016] FIG5 is a schematic structural diagram of an electronic device provided by an embodiment of the present disclosure. DETAILED DESCRIPTION

[0017] The following describes the specific embodiments of the present disclosure in detail with reference to the accompanying drawings. It should be understood that the specific embodiments described herein are only used to illustrate and explain the present disclosure and are not intended to limit the present disclosure.

[0018] As used in this disclosure, the term "and / or" includes any and all combinations of one or more of the associated listed items.

[0019] The terms used in the present disclosure are only used to describe specific embodiments and are not intended to limit the present disclosure.As used in the present disclosure, the singular forms "a", "an" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise.

[0020] When the terms “comprising” and / or “made of…” are used in the present disclosure, it specifies the existence of the stated features, integers, steps, operations, elements and / or components, but does not preclude the existence or addition of one or more other features, integers, steps, operations, elements, components and / or groups thereof.

[0021] Unless otherwise defined, all terms (including technical and scientific terms) used in this disclosure have the same meanings as those commonly understood by those skilled in the art. It will also be understood that terms such as those defined in commonly used dictionaries should be interpreted as having a meaning consistent with their meaning in the context of the relevant art and this disclosure, and will not be interpreted as having an idealized or overly formal meaning unless expressly defined in this disclosure.

[0022] A knowledge graph is a structured, semantic knowledge base that describes entities in the physical world and their relationships through a schema model. The schema defines the format of data to be added to the knowledge graph. Data in the knowledge graph must conform to the ontology and relationship models predefined by the schema.

[0023] Constructing a schema model within the O&M fault knowledge graph involves abstracting the knowledge of O&M fault cases (typically the text of these cases) into a set of business modeling logic. This allows the business model to encompass the most valuable information within the O&M fault knowledge. O&M fault cases can be those in the communications network domain. The constructed schema model can be used to utilize and model fault knowledge within network management and maintenance within communications network operations.

[0024] The present disclosure provides a method for constructing a schema model in a knowledge graph to address the difficulty of manually designing a schema model for an operation and maintenance fault knowledge graph to cover all valuable knowledge. FIG1 shows a flow chart of the method for constructing a schema model in a knowledge graph. The method can be executed by any electronic device. The method includes the following steps 101 to 104.

[0025] In step 101, a text knowledge base of operation and maintenance fault cases is obtained.

[0026] In some embodiments, knowledge text related to communication network operation and maintenance failure cases can be obtained through various methods to construct a text knowledge base of operation and maintenance failure cases, including but not limited to obtaining knowledge text related to operation and maintenance failure cases from company websites, professional books, historical archives, and document records. The communication network operation and maintenance failure cases can be operation and maintenance failure cases of wireless communication networks or wired communication networks, without limitation.

[0027] In step 102, each schema modeling domain of the operation and maintenance fault knowledge graph is obtained from the text knowledge base, and the representative ontology name corresponding to each schema modeling domain is determined based on the text knowledge base, wherein the schema modeling domain includes the fault domain, the fault manifestation domain, the troubleshooting item domain, the troubleshooting content domain, the troubleshooting conclusion domain, the fault root cause domain and the fault solution domain.

[0028] In some embodiments, each schema modeling domain of the operation and maintenance fault knowledge graph is obtained from a text knowledge base, including: extracting a first semantic layer from the text knowledge base, the first semantic layer including fault discovery semantic information, fault analysis semantic information and fault resolution semantic information corresponding to the complete fault handling process; extracting a second semantic layer associated with the first semantic layer from the text knowledge base, the second semantic layer including: a fault domain and a fault manifestation domain associated with the fault discovery semantic information, a troubleshooting project domain, a troubleshooting content domain, a troubleshooting conclusion domain and a fault root cause domain associated with the fault analysis semantic information, and a fault solution domain associated with the fault resolution semantic information.

[0029] For example, a complete fault handling process includes three steps: fault discovery, fault analysis, and fault resolution. This complete fault handling process begins by extracting the first semantic layer from the text knowledge base. The first semantic layer includes semantic information related to fault discovery, fault analysis, and fault resolution. Second, based on the fault case text in the text knowledge base, the first semantic layer is expanded to obtain a second semantic layer. This second semantic layer includes the fault domain and fault manifestation domain, which are expanded based on the fault discovery semantic information, and the fault resolution domain, which is expanded based on the fault analysis semantic information. Finally, the domains in the second semantic layer are aggregated to obtain the schema modeling domains of the operation and maintenance fault knowledge graph.

[0030] The text of operation and maintenance fault cases is recorded by different field personnel, and different personnel have different recording preferences. The language organization form of the text is also different, and the richness of the knowledge content also varies. In the embodiment of the present disclosure, the semantic extraction of the text knowledge base of the operation and maintenance fault cases is used to identify each schema modeling domain, thereby solving the problem of difficulty in extracting core knowledge caused by the diverse narrative structure and large differences in content expression of the operation and maintenance fault case text. In addition, in the embodiment of the present disclosure, semantic extraction is performed through the complete fault handling process, so that the obtained schema modeling domains can cover the core value points related to the entire operation and maintenance fault handling process. Semantic extraction is more efficient than manual design, and can obtain each schema modeling domain based on the text of more operation and maintenance fault cases. Even if the expression content of some operation and maintenance fault cases is not rich enough, a complete schema modeling domain can be obtained.

[0031] In some embodiments, determining a representative ontology name corresponding to each schema modeling domain based on a text knowledge base includes: enumerating at least one ontology expression word corresponding to each schema modeling domain from a portion of operation and maintenance failure cases in the text knowledge base; obtaining the number of occurrences of each ontology expression word in the text knowledge base; determining the similarity between each ontology expression word and the text corresponding to the corresponding schema modeling domain; and, for each schema modeling domain, determining a representative ontology name corresponding to the schema modeling domain based on the number of occurrences and similarity of each ontology expression word corresponding to the schema modeling domain. Obtaining the number of occurrences of each ontology expression word in the text knowledge base may include obtaining the number of occurrences of each ontology expression word in a portion of the operation and maintenance failure cases.

[0032] Representative ontology names are used to express the ontology and / or attributes of the schema modeling domain. FIG2 shows a schematic diagram of the main process of discovering and screening representative ontology names according to the schema modeling domain, including the following steps 201 to 202.

[0033] In step 201, schema modeling domain expansion is performed based on the text of some operation and maintenance fault cases in the text knowledge base.

[0034] In step 202, the final ontology expression words are screened based on the texts of some operation and maintenance failure cases, and the final expression words are used as representative ontology names.

[0035] The method for obtaining a portion of the operation and maintenance failure cases from the text knowledge base includes sampling the text knowledge base of operation and maintenance failure cases, with a sampling ratio of no less than 10%, and using the sampled portion of the operation and maintenance failure cases to extract ontology description terms. The final ontology description terms are representative ontology names obtained by screening based on frequency of occurrence and similarity.

[0036] The ontology expressions of the schema modeling domain include ontology words and / or attribute words. For example, from the text knowledge associated with the [fault domain] in the text knowledge base, the attribute words such as [fault number], [fault location], [fault time], and [fault name] are enumerated; from the text knowledge associated with the [fault manifestation domain] in the text knowledge base, the ontology words such as [fault phenomenon], [fault appearance], [fault impact], and [fault manifestation] are enumerated; from the text knowledge associated with the [fault root cause domain] in the text knowledge base, the ontology words such as [fault root cause], [fault cause], and [fault source] are enumerated; from the text knowledge associated with the [fault solution domain] in the text knowledge base, the ontology words such as [fault phenomenon], [fault appearance], [fault impact], and [fault manifestation] are enumerated. Enumerate the following ontological terms from the knowledge: [solution method], [solution solution], [solution means], [solution measures], etc.; enumerate the following ontological terms from the text knowledge associated with the [investigation project domain] in the text knowledge base: [investigation project], [investigation means], [investigation method], [investigation method], etc.; enumerate the following ontological terms from the text knowledge associated with the [investigation content domain] in the text knowledge base: [investigation content], etc.; enumerate the following ontological terms from the text knowledge associated with the [investigation conclusion domain] in the text knowledge base: [investigation conclusion], [investigation result], [verification conclusion], [verification result], etc.

[0037] In some embodiments, determining the similarity between each ontology expression word and the corresponding text of the corresponding schema modeling domain includes: extracting the title text in the text corresponding to each schema modeling domain from some operation and maintenance failure cases, and determining the first semantic vector corresponding to the title text; determining the second semantic vector corresponding to each ontology expression word; obtaining the average value of the first semantic vectors of each title text corresponding to the same schema modeling domain, and obtaining the third semantic vector of the text corresponding to the schema modeling domain; for each ontology expression word, determining the cosine similarity between the corresponding second semantic vector and the third semantic vector of the corresponding schema modeling domain as the similarity between the ontology expression word and the corresponding schema modeling domain.

[0038] The text of an operation and maintenance fault case has a certain format, which specifies the recording location of the text related to each step in the fault operation and maintenance process. Although different people have different expression habits, the basic format of the text is the same. In the embodiment of the present disclosure, the semantic information embodied in the title text of the operation and maintenance fault case is used to express the content of the corresponding schema modeling domain, thereby ensuring the accuracy of the semantics. Specifically, the first line of each paragraph in the text corresponding to the schema modeling domain can be extracted as the title text, or the paragraph with a specific format in the text corresponding to the schema modeling domain can be extracted as the title text. The specific format may include paragraphs without indentation format, paragraphs marked with bold or underline, paragraphs starting with Arabic numerals, etc.

[0039] Exemplarily, title texts in texts corresponding to each schema modeling domain are extracted from some operation and maintenance fault cases, and a first semantic vector corresponding to the title text is determined, including performing the following process for each schema modeling domain: extracting texts corresponding to the schema modeling domain from some operation and maintenance fault cases, extracting title texts from the extracted texts corresponding to the schema modeling domain, inputting each extracted title text into a pre-trained language model (for example, a bidirectional encoder representation from transformers (BERT) model) to encode the text corresponding to the schema modeling domain, and obtaining a first semantic vector corresponding to the schema modeling domain.

[0040] Assuming that the number of title texts corresponding to a certain schema modeling domain is M, then the schema modeling domain corresponds to M first semantic vectors. The vector average of each first semantic vector corresponding to the schema modeling domain is calculated as the third semantic vector corresponding to the schema modeling domain.

[0041] It should be noted that the semantic vectors used here are derived using the BERT model as an example. The specific acquisition process includes: inputting the title text (e.g., a sentence) into the BERT model and using the vector output by the BERT model, which begins with the [CLS] token, as the sentence vector. Semantic vectors can also be obtained using other methods, such as using a word2vec model to obtain the vector of each word in the sentence, and then adding and averaging all the word vectors to obtain the sentence vector.

[0042] Exemplarily, determining the second semantic vector corresponding to each ontology expression word includes: inputting each ontology expression word into a pre-trained language model (for example, a BERT model) for encoding, and obtaining the second semantic vector of each ontology expression word, wherein the number of ontology expression words corresponding to each schema modeling domain is the same or different. Assuming that the number of ontology expression words corresponding to a certain schema modeling domain is N, then the schema modeling domain corresponds to N second semantic vectors.

[0043] It should be noted that the semantic vectors used here are derived using the BERT model as an example. The specific acquisition process includes: inputting the title text (e.g., a sentence) into the BERT model and using the vector output by the BERT model, which begins with the [CLS] token, as the sentence vector. Semantic vectors can also be obtained using other methods, such as using the word2vec model to obtain the vector of each word in the sentence, and then adding and averaging all the word vectors to obtain the sentence vector.

[0044] Exemplarily, after determining the similarity between each ontology expression word and the corresponding text of the corresponding schema modeling domain, the method further includes: filtering out ontology expression words whose similarity is lower than a preset similarity threshold; if all the ontology expression words of the target schema modeling domain are filtered out, restoring the ontology expression words of the target schema modeling domain before filtering, thereby retaining at least one ontology expression word for each schema modeling domain.

[0045] For example, the similarity between [fault manifestation domain] and [fault phenomenon] is 90%. When retaining ontology expressions with a cosine similarity threshold greater than 0.8, [fault phenomenon] can be retained as the ontology expression word of [fault manifestation domain].

[0046] In some embodiments, for each schema modeling domain, a representative ontology name corresponding to the schema modeling domain is determined based on the number of occurrences and similarities of each ontology expression word corresponding to the schema modeling domain, including performing the following processing for each schema modeling domain: calculating the sum of the number of occurrences of each ontology expression word corresponding to the schema modeling domain, and calculating the ratio of the number of occurrences of each ontology expression word corresponding to the schema modeling domain to the sum; obtaining a score value for each ontology expression word in the schema modeling domain, the score value of the ontology expression word being the larger value of the ratio corresponding to the ontology expression word and the corresponding similarity; and determining the ontology expression word with the highest score value as the representative ontology name corresponding to the schema modeling domain.

[0047] In the embodiment, a ratio is used to represent the frequency of occurrence of ontology expression words, and similarity is used to represent the degree of representation of the extracted content. By comprehensively considering these two dimensions to screen ontology expression words, the screened ontology expression words can have wide applicability and representativeness.

[0048] In an exemplary embodiment, taking the [Troubleshooting Conclusion Domain] as an example, assuming that the [Troubleshooting Conclusion Domain] corresponds to four ontology expressions, the number of times each of these four ontology expressions occurs is obtained, the sum of the number of times for these four ontology expressions is calculated, and the ratio of the number of times for each ontology expression to the sum is calculated as the frequency of occurrence of the ontology expression. Assume that the frequency of occurrence of [Troubleshooting Conclusion] in the [Troubleshooting Conclusion Domain] accounts for 10%, the frequency of occurrence of [Troubleshooting Results] in the [Troubleshooting Conclusion Domain] accounts for 80%, and the frequency of occurrence of [Verification Results] in the [Troubleshooting Conclusion Domain] accounts for 5%. Taking the [Fault Manifestation Domain] as an example, the frequency of occurrence of [Fault Phenomenon] accounts for 99%.

[0049] In an exemplary embodiment, in the ratio of the frequency of occurrence corresponding to the ontology expression word and the corresponding similarity, the larger value is taken as the scoring value of the ontology expression word. For example, the frequency of occurrence of [fault phenomenon] in [fault manifestation domain] accounts for 99%; the frequency of occurrence of [fault manifestation] in [fault manifestation domain] accounts for 1%; the similarity between [fault manifestation domain] and [fault phenomenon] is 90%; the similarity between [fault manifestation domain] and [fault manifestation] is 80%; then: the score of [fault manifestation] is max(0.1,0.8)=0.8; the score of [fault phenomenon] is: max(0.99,0.9)=0.99.

[0050] In step 103, the extended ontology name corresponding to the representative ontology name of each schema modeling domain and the entity name corresponding to the extended ontology name are obtained from the text knowledge base.

[0051] In some embodiments, the extended ontology name corresponding to the representative ontology name of each schema modeling domain and the entity name corresponding to the extended ontology name are obtained from a text knowledge base, including: using a pre-trained entity extraction model to extract entity description information from the text corresponding to each schema modeling domain in the text knowledge base; based on a pre-configured proper noun library, extracting proper nouns from the entity description information, and using the extracted proper nouns as the extended ontology name corresponding to the corresponding schema modeling domain, and as the extended ontology name corresponding to the corresponding entity description information.

[0052] In an exemplary embodiment, FIG3 is a schematic diagram showing a process of extracting an extended ontology from a schema modeling domain, including the following steps 301 to 302 .

[0053] In step 301, predefined entities are used to pre-extract ontology expressions of the schema modeling domain, and the pre-extracted information is entity description information in the text corresponding to the modeling domain.

[0054] In step 302, ontology expansion is performed in combination with communication operation and maintenance domain knowledge, that is, proper nouns in the communication operation and maintenance domain are extracted from entity description information.

[0055] For example, during the ontology pre-extraction process, the following situations occurred in the pre-extraction results of the schema modeling domain [Troubleshooting Content] in the text of some operation and maintenance failure cases:

[0056] ① Check whether the Radio Resource Control (RRC) connection success rate exceeds the threshold;

[0057] ② Check whether the remote radio unit (RRU) alarm of the cell has a link disconnection alarm;

[0058] ③ Check the basic configuration of the network element baseband processing unit (BBU);

[0059] ④ Check the trace start (TraceStart) signaling process;

[0060] ⑤Analyze the success rate of Evolved Radio Access Bearer (ERAB) connection establishment.

[0061] During the ontology expansion process, the extraction results of [Troubleshooting Content] mostly contain technical terms in the field of communication operation and maintenance, such as performance indicators, alarms, configurations, etc. These more specific technical terms in the field are used to concretize the modeling domain, so that the representative ontology name can be expanded. The descriptive information of ontologies such as performance indicators, alarms, and configurations in the pre-extraction results of the troubleshooting content can be used to expand the representative ontology name of the troubleshooting content. Therefore, the representative ontology name [Troubleshooting Content] is concretized by 5 extended ontology names: [Performance Indicators], [Alarms], [Configuration], [Parameters], and [Signaling].

[0062] In step 104, a schema model of the operation and maintenance fault knowledge graph is constructed using the representative ontology names, extended ontology names, and entity names corresponding to the extended ontology names corresponding to each schema modeling domain.

[0063] In some embodiments, a schema model of an operation and maintenance fault knowledge graph is constructed using representative ontology names, extended ontology names, and entity names corresponding to the extended ontology names corresponding to each schema modeling domain, including: constructing an ontology model of the relationship between the representative ontology names, extended ontology names, and entity description information using representative ontology names, extended ontology names, and entity description information corresponding to each schema modeling domain; determining a head entity based on the extended ontology name corresponding to the schema fault domain, determining a relationship type and a tail entity based on the extended ontology name and entity description information corresponding to the fault manifestation domain, troubleshooting item domain, troubleshooting content domain, troubleshooting conclusion domain, fault root cause domain, and fault solution domain, and constructing a relationship model between the head entity, relationship type, and tail entity; and constructing a schema model of an operation and maintenance fault knowledge graph based on the ontology model and the relationship model.

[0064] Table 1 shows an example of an ontology model. The ontology expression words in Table 1 are used as the ontology in the ontology model, and the entity names in the entity description information corresponding to the ontology expression words are used as entities.

[0065] Table 1 Ontology model

[0066] The ontology of the fault domain in the ontology model is taken as the head entity, the ontologies of the fault manifestation domain, troubleshooting project domain, troubleshooting content domain, troubleshooting conclusion domain, fault root cause domain and fault solution domain in the ontology model are taken as the relationship type, and the corresponding entities of the fault manifestation domain, troubleshooting project domain, troubleshooting content domain, troubleshooting conclusion domain, fault root cause domain and fault solution domain in the ontology model are taken as the tail entity, and a relationship model is constructed in which the relationship type, the head entity and the tail entity are associated.

[0067] Table 2 shows an example of a relational model, where relations (i.e., relation types) refer to the ontologies of other schema modeling domains except the fault domain.

[0068] Table 2 Relationship model

[0069] After building the schema model, you can build a knowledge graph based on specific knowledge graph design requirements, that is, the construction of the {ontology (attribute) - relationship - ontology (attribute, here refers to the entity corresponding to the ontology)} triple schema. The knowledge graph example is as follows:

[0070] Fault 34245——>Fault phenomenon is——>Call freeze;

[0071] Fault 34245: The root cause is: BBU failure.

[0072] Fault 34245 -> Solution is -> Replace the BBU;

[0073] Fault 34245 -> Troubleshooting method is -> Alarm verification;

[0074] Fault 34245 -> Troubleshooting method is -> Signaling verification;

[0075] Fault 34245 ——> Troubleshooting method is ——> Parameter verification;

[0076] Fault 34245 -> Alarm Troubleshooting Item -> BBU Link Disconnect Alarm;

[0077] Fault 34245 -> Signaling Troubleshooting Item -> DeactiveTrace;

[0078] Fault 34245 -> Parameter troubleshooting -> BBU activation parameters;

[0079] Fault 34245 -> Troubleshooting conclusion is -> Alarm is normal;

[0080] Fault 34245 -> Troubleshooting conclusion is -> Signaling is normal;

[0081] Fault 34245——>The troubleshooting conclusion is——>Parameter error.

[0082] In an embodiment of the present disclosure, each schema modeling domain of the operation and maintenance fault knowledge graph, the representative ontology name corresponding to each schema modeling domain, the extended ontology name corresponding to the representative ontology name, and the entity name corresponding to the extended ontology name are obtained from a text knowledge base of operation and maintenance fault cases, and the schema model of the operation and maintenance fault knowledge graph is constructed using the representative ontology name, extended ontology name, and entity name corresponding to the extended ontology name corresponding to each schema modeling domain, thereby realizing the automated construction of the schema model of the operation and maintenance fault knowledge graph. In addition, by identifying the fault domain, fault manifestation domain, troubleshooting item domain, troubleshooting content domain, troubleshooting conclusion domain, fault root cause domain and fault solution domain, a complete operation and maintenance fault handling process is covered. Even if the fault knowledge content is complex and covers a wide range, it can still fully cover the valuable content in the entire fault handling process, greatly simplifying the abstraction process of operation and maintenance fault knowledge, and laying the foundation for the realization of an operation and maintenance system based on a knowledge graph. Moreover, the schema model is constructed based on actual operation and maintenance fault cases, which can cover important and valuable information in operation and maintenance fault cases, and can maximize the refinement of operation and maintenance fault knowledge, thereby improving the use value of the knowledge graph constructed based on the schema model in operation and maintenance.

[0083] The steps of the various methods above are divided only for clear description. When implemented, they can be combined into one step or some steps can be split and decomposed into multiple steps. As long as they include the same logical relationship, they are all within the scope of protection of this disclosure; adding modifications or introducing designs into the algorithm or process without changing the core design of the algorithm and process are all within the scope of protection of this disclosure.

[0084] The present disclosure also provides a method for constructing a schema model in a knowledge graph. The specific implementation of the method can be found in the relevant description of the method embodiment and will not be repeated here. FIG4 shows a schematic diagram of the structure of the method, including: a first acquisition module 401, a second acquisition module 402, a third acquisition module 403, and a construction module 404.

[0085] The first acquisition module 401 is used to acquire a text knowledge base of operation and maintenance fault cases.

[0086] The second acquisition module 402 is used to obtain each schema modeling domain of the operation and maintenance fault knowledge graph from the text knowledge base, and determine the representative ontology name corresponding to each schema modeling domain based on the text knowledge base, wherein the schema modeling domain includes a fault domain, a fault manifestation domain, a troubleshooting item domain, a troubleshooting content domain, a troubleshooting conclusion domain, a fault root cause domain and a fault solution domain.

[0087] The third acquisition module 403 is used to acquire, from the text knowledge base, the extended ontology name corresponding to the representative ontology name of each schema modeling domain, and the entity name corresponding to the extended ontology name.

[0088] The construction module 404 is used to construct a schema model of the operation and maintenance fault knowledge graph by using the representative ontology name, extended ontology name and entity name corresponding to each schema modeling domain.

[0089] The functions or modules included in the apparatus provided in the embodiments of the present disclosure can be used to execute the method described in the method embodiments. The specific implementation and technical effects thereof can be referred to the description of the above method embodiments, and will not be repeated here for the sake of brevity.

[0090] It should be noted that all modules involved in this embodiment are logical modules. In actual applications, a logical unit can be a physical unit, a part of a physical unit, or a combination of multiple physical units. In addition, to highlight the innovations of this disclosure, this embodiment does not include units that are not closely related to solving the technical problems proposed by this disclosure. However, this does not mean that other units do not exist in this embodiment.

[0091] 5 , an embodiment of the present disclosure provides an electronic device, comprising: at least one processor 501; and a memory 502 on which at least one program is stored. When the at least one program is executed by the at least one processor 501, the at least one processor 501 implements the schema model construction method in the knowledge graph according to each embodiment of the present disclosure.

[0092] In addition, as shown in FIG5 , the electronic device may further include at least one I / O interface (read-write interface) 503 , which is connected between the processor 501 and the memory 502 and configured to implement information interaction between the processor 501 and the memory 502 .

[0093] The processor 501 is a device with data processing capabilities, including but not limited to a central processing unit (CPU); the memory 502 is a device with data storage capabilities, including but not limited to random access memory (RAM, more specifically SDRAM, DDR, etc.), read-only memory (ROM), electrically erasable programmable read-only memory (EEPROM), and flash memory (FLASH); the I / O interface 503 is connected between the processor 501 and the memory 502, and can realize information exchange between the processor 501 and the memory 502, including but not limited to a data bus (Bus), etc.

[0094] In some embodiments, the processor 501 , the memory 502 , and the I / O interface 503 are connected to each other via a bus, and further connected to other components of the computing device.

[0095] This embodiment also provides a computer-readable medium having a computer program stored thereon, which, when executed by a processor, causes the processor to implement the method for constructing a schema model in a knowledge graph according to various embodiments of the present disclosure. To avoid repetitive description, the specific steps of this method are not repeated here.

[0096] It will be appreciated by those skilled in the art that all or some of the steps, systems, and functional modules / units in the methods applied for above may be implemented as software, firmware, hardware, and appropriate combinations thereof. In hardware implementations, the division between the functional modules / units mentioned in the above description does not necessarily correspond to the division of physical components; for example, a physical component may have multiple functions, or a function or step may be performed by several physical components in cooperation. Some or all physical components may be implemented as software executed by a processor, such as a central processing unit, a digital signal processor, or a microprocessor, or implemented as hardware, or implemented as an integrated circuit, such as an application-specific integrated circuit. Such software may be distributed on a computer-readable medium, which may include a computer storage medium (or non-transitory medium) and a communication medium (or temporary medium). As is well known to those skilled in the art, the term computer storage medium includes volatile and non-volatile, removable, and non-removable media implemented in any method or technology for storing information (such as computer-readable instructions, data structures, program modules, or other data). Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium that can be used to store the desired information and can be accessed by a computer. In addition, it is well known to those skilled in the art that communication media typically embodies computer-readable instructions, data structures, program modules, or other data in a modulated data signal such as a carrier wave or other transport mechanism, and may include any information delivery media.

[0097] It should be noted that, in this document, the terms "comprises," "includes," or any other variations thereof are intended to encompass non-exclusive inclusion, such that a process, method, article, or apparatus comprising a series of elements includes not only those elements but also other elements not explicitly listed, or elements inherent to such process, method, article, or apparatus. In the absence of further limitations, an element defined by the phrase "comprising a ..." does not exclude the presence of other identical elements in the process, method, article, or apparatus comprising the element.

[0098] Those skilled in the art will understand that although some embodiments described herein include certain features included in other embodiments but not other features, the combination of features from different embodiments is meant to be within the scope of the present embodiment and to form different embodiments.

[0099] It is understood that the above embodiments are merely exemplary embodiments for illustrating the principles of the present disclosure, and the present disclosure is not limited thereto. Those skilled in the art may make various modifications and improvements without departing from the spirit and substance of the present disclosure, and such modifications and improvements are also considered to be within the scope of protection of the present disclosure.

Claims

1. A method for constructing a schema model in a knowledge graph, comprising: Obtain a text knowledge base of operation and maintenance failure cases; Acquire each schema modeling domain of the operation and maintenance fault knowledge graph from the text knowledge base, and determine the representative ontology name corresponding to each schema modeling domain based on the text knowledge base, wherein the schema modeling domain includes a fault domain, a fault manifestation domain, a troubleshooting item domain, a troubleshooting content domain, a troubleshooting conclusion domain, a fault root cause domain, and a fault solution domain; Acquire, from the text knowledge base, an extended ontology name corresponding to a representative ontology name of each schema modeling domain, and an entity name corresponding to the extended ontology name; The schema model of the operation and maintenance fault knowledge graph is constructed by using the representative ontology names, extended ontology names and entity names corresponding to the extended ontology names corresponding to the schema modeling domains.

2. The method according to claim 1, wherein: The schema modeling domains of the operation and maintenance fault knowledge graph are obtained from the text knowledge base, including: Extracting a first semantic layer from the text knowledge base, wherein the first semantic layer includes semantic information of fault discovery, semantic information of fault analysis, and semantic information of fault resolution corresponding to a complete fault handling process; The second semantic layer associated with the first semantic layer is extracted from the text knowledge base, and the second semantic layer includes: the fault domain and the fault manifestation domain associated with the fault discovery semantic information, the troubleshooting project domain, the troubleshooting content domain, the troubleshooting conclusion domain and the fault root cause domain associated with the fault analysis semantic information, and the fault solution domain associated with the fault resolution semantic information.

3. The method according to claim 1, wherein: Determining representative ontology names corresponding to each schema modeling domain based on the text knowledge base includes: Enumerate at least one ontology expression word corresponding to each of the schema modeling domains from some operation and maintenance failure cases in the text knowledge base; Obtaining the number of occurrences of each ontology expression word in the text knowledge base; Determining the similarity between each ontology expression word and the corresponding text of the schema modeling domain; For each of the schema modeling domains, a representative ontology name corresponding to the schema modeling domain is determined according to the number of occurrences and similarities of each ontology expression word corresponding to the schema modeling domain.

4. The method according to claim 3, wherein: Determining the similarity between each ontology expression word and the corresponding text of the schema modeling domain includes: Extracting title texts in texts corresponding to each of the schema modeling domains from the partial operation and maintenance fault cases, and determining a first semantic vector corresponding to the title text; Determine the second semantic vector corresponding to each of the ontology expression words; Obtaining an average value of the first semantic vectors of the title texts corresponding to the same schema modeling domain, and obtaining a third semantic vector of the text corresponding to the schema modeling domain; For each ontology expression word, the cosine similarity between the corresponding second semantic vector and the third semantic vector of the corresponding schema modeling domain is determined as the similarity between the ontology expression word and the corresponding schema modeling domain.

5. The method according to claim 3, wherein: For each of the schema modeling domains, according to the number of occurrences and similarities of each of the ontology expressions corresponding to the schema modeling domain, a representative ontology name corresponding to the schema modeling domain is determined, including performing the following processing for each of the schema modeling domains: Calculating the sum of the number of occurrences of each ontology expression word corresponding to the schema modeling domain, and calculating the ratio of the number of occurrences of each ontology expression word corresponding to the schema modeling domain to the sum; Obtaining a score value of each ontology expression word of the schema modeling domain, wherein the score value of the ontology expression word is a larger value of the ratio corresponding to the ontology expression word and the corresponding similarity; The ontology expression word with the highest score is determined as the representative ontology name corresponding to the schema modeling domain.

6. The method according to claim 1, wherein: Acquiring, from the text knowledge base, an extended ontology name corresponding to a representative ontology name of each schema modeling domain, and an entity name corresponding to the extended ontology name, including: Using a pre-trained entity extraction model, extracting entity description information from the text corresponding to each schema modeling domain in the text knowledge base; Based on a pre-configured proper noun library, proper nouns are extracted from the entity description information, and the extracted proper nouns are used as the extended ontology names corresponding to the schema modeling domain and as the extended ontology names corresponding to the entity description information.

7. The method according to claim 1, wherein: The schema model of the operation and maintenance fault knowledge graph is constructed by using the representative ontology name, the extended ontology name and the entity name corresponding to each of the schema modeling domains, including: Using the representative ontology name corresponding to each of the schema modeling domains, the extended ontology name, and the entity description information corresponding to the extended ontology name, an ontology model of the relationship among the representative ontology name, the extended ontology name, and the entity description information is constructed; Determine the head entity based on the extended ontology name corresponding to the schema fault domain, determine the relationship type and the tail entity based on the extended ontology names and entity description information corresponding to the fault manifestation domain, troubleshooting item domain, troubleshooting content domain, troubleshooting conclusion domain, fault root cause domain and fault solution domain, and construct a relationship model among the head entity, the relationship type and the tail entity; A schema model of the operation and maintenance fault knowledge graph is constructed based on the ontology model and the relationship model.

8. A device for constructing a schema model in a knowledge graph, comprising: The first acquisition module is used to acquire a text knowledge base of operation and maintenance failure cases; The second acquisition module is used to obtain each schema modeling domain of the operation and maintenance fault knowledge graph from the text knowledge base, and determine the representative ontology name corresponding to each schema modeling domain based on the text knowledge base, wherein the schema modeling domain includes the fault domain, the fault manifestation domain, the troubleshooting project domain, the troubleshooting content domain, the troubleshooting conclusion domain, the fault root cause domain, the fault domain and troubleshooting solution domain; A third acquisition module is used to acquire, from the text knowledge base, the extended ontology name corresponding to the representative ontology name of each schema modeling domain, and the entity name corresponding to the extended ontology name; A construction module is used to construct a schema model of the operation and maintenance fault knowledge graph by using representative ontology names, extended ontology names and entity names corresponding to the extended ontology names corresponding to each of the schema modeling domains.

9. An electronic device, comprising: at least one processor; a memory having one or more programs stored therein, When the one or more programs are executed by the one or more processors, the one or more processors implement the method for constructing a schema model in a knowledge graph according to any one of claims 1-7.

10. A computer-readable medium having a computer program stored thereon, wherein when the program is executed by a processor, the processor implements the method for constructing a schema model in a knowledge graph according to any one of claims 1-7.

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