Scientific and technological innovation project data retrieval recommendation method, system, equipment and medium

Abstract

The invention discloses a scientific and technological innovation project data retrieval recommendation method, system and device and a medium, and particularly relates to the technical field of data association, and the technical key points are as follows: taking each entity in scientific and technological innovation project data as a graph node to construct a knowledge graph; determining a mapping entity of the retrieval entity in the knowledge graph, calculating the association strength between the mapping entity and the entity having the relationship, constructing a neighborhood representation of the mapping entity based on the association strength, and performing feature expansion on the mapping entity according to the neighborhood representation; spreading the historical retrieval interaction entities in the knowledge graph according to the entity connection relationship to obtain neighbor entities of the historical retrieval interaction entities in each layer of the knowledge graph, calculating the correlation between the historical retrieval interaction entities and the neighbor entities in each layer, and aggregating all the calculated correlations to obtain retrieval interest features; and performing weighted calculation on the first prediction result and the second prediction result, and outputting an item data retrieval recommendation result based on a predicted value after weighted calculation.

Description

Technical Field

[0001] The present invention relates to the field of data association technology, and in particular to a method, system, device and medium for retrieval and recommendation of scientific and technological innovation project data. Background Art

[0002] The science and technology innovation management system is a centralized management system for enterprise-level scientific research projects, including project management, high-tech enterprise management, scientific and technological achievement management, achievement transformation, knowledge base, etc. Through a unified platform, unified planning, and unified construction model, data channels are opened up, information islands are eliminated, and vertical data penetration and horizontal integration of functions are achieved, which also supports the subsequent construction and management of data centers and enterprise service buses.

[0003] In the existing science and technology innovation management system, when an enterprise needs to form some certification materials or project reports and needs to retrieve or query data of the same subject or type in multiple sections, since the information retrieval and data recommendation of each section are relatively independent and each section has a lot of data, the traditional method is to retrieve relevant data in each section separately and then fill in the report. Therefore, it is difficult to match data of the same subject under different sections, making it difficult to generate complete data reports or certification materials, and it is impossible to provide data support efficiently and accurately.

[0004] Therefore, the present invention aims to provide a method, system, device and medium for retrieving and recommending scientific and technological innovation project data to solve the above-mentioned related problems. Summary of the invention

[0005] The technical problem to be solved by the present invention is that data matching of the same subject under different sections is difficult, it is difficult to generate complete data reports or identification materials, and data support cannot be provided efficiently and accurately. The purpose is to provide a method, system, device and medium for data retrieval and recommendation of scientific and technological innovation projects. By constructing a knowledge graph with scientific and technological innovation project data, it is convenient to integrate the data with relevance in each section, and to facilitate the subsequent call of data recommendation; at the same time, the mapping entity corresponding to the retrieval entity is determined in the knowledge graph, and the association strength between the mapping entity and the entity with relationship in the knowledge graph is calculated, and then the neighborhood representation of the mapping entity is constructed based on the association strength, and the mapping entity is feature expanded according to the neighborhood representation, which can increase the feature representation of the entity in the data retrieval process, enrich the semantic information of the recommended data, and avoid recommendation deviation and data recommendation missing caused by insufficient information. The technical problem that data matching of the same subject under different sections is difficult, it is difficult to generate complete data reports or identification materials, and data support cannot be provided efficiently and accurately is solved.

[0006] The present invention is achieved through the following technical solutions: A method for retrieving and recommending scientific and technological innovation project data, the method comprising: Obtain scientific and technological innovation project data, and use each entity in the scientific and technological innovation project data as a graph node to construct a knowledge graph; Extract the search entity in the search information, determine the mapping entity corresponding to the search entity in the knowledge graph, calculate the association strength between the mapping entity and the entity with relationship in the knowledge graph, construct a neighborhood representation of the mapping entity based on the association strength, and perform feature expansion on the mapping entity according to the neighborhood representation, input the mapping entity after feature expansion into the pre-constructed first prediction model, and calculate and obtain the first prediction result; The historical retrieval interaction entity is propagated in the knowledge graph according to the entity connection relationship to obtain the neighbor entity of the historical retrieval interaction entity in each layer of the knowledge graph, the correlation between the historical retrieval interaction entity and the neighbor entity of each layer is calculated, and all the calculated correlations are aggregated to obtain the retrieval interest feature, and the retrieval interest feature is input into the pre-built second prediction model to calculate the second prediction result; The relative entropy between the first prediction result and the second prediction result is calculated to obtain the prediction result deviation value, and the first prediction result and the second prediction result are weightedly calculated, the prediction result deviation value is fused with the weighted calculation result, and the final project data retrieval recommendation result is output according to the fused prediction value.

[0007] Furthermore, each entity in the knowledge graph includes indicator entities and project entities.

[0008] Furthermore, the search entity in the search information is extracted, the mapping entity corresponding to the search entity is determined in the knowledge graph, the association strength between the mapping entity and the entity with a relationship in the knowledge graph is calculated, the neighborhood representation of the mapping entity is constructed based on the association strength, and the features of the mapping entity are expanded according to the neighborhood representation, specifically: Extract the search entity in the search information and determine the mapping entity corresponding to the search entity in the knowledge graph; Calculating a first association strength between a mapping entity and a project entity having a relationship, and calculating a second association strength between the mapping entity and an indicator entity having a relationship, and constructing a neighborhood representation of the mapping entity based on the first association strength and the second association strength; The neighborhood representation and the mapping entity are aggregated using the SUM aggregation method to obtain the mapping entity after feature expansion.

[0009] Furthermore, the second association strength between the mapping entity and the indicator entity having the relationship is calculated, specifically: Filter indicator entities that have relationships with mapping entities in the knowledge graph, and find all relationship paths from mapping entities to indicator entities; The pre-built knowledge representation model is used to calculate the semantic similarity of all relationship paths, specifically: ,in, Indicates the number of hops in the relationship path; Indicates The weight of the jump; Indicates Semantic similarity of jumps; Represents an indicator entity; Represents a relationship path; Representation mapping entity, the pre-built knowledge representation model is obtained by training the TransE model; The average pooling algorithm is used to aggregate the semantic similarities of all relationship paths to obtain the second association strength, wherein the path aggregation weight of each relationship path is dynamically adjusted through the attention mechanism, specifically: ,in, represents the second association strength; Indicates the path aggregation weight.

[0010] Furthermore, the historical retrieval interaction entities are propagated in the knowledge graph according to the entity connection relationship to obtain the neighbor entities of the historical retrieval interaction entities in each layer of the knowledge graph, and the correlation between the historical retrieval interaction entities and the neighbor entities in each layer is calculated. All the calculated correlations are aggregated to obtain the retrieval interest features, which are specifically: Taking the historical retrieval interaction entity as the starting point, propagate it in the knowledge graph according to the entity connection relationship to obtain the neighbor entities of the historical retrieval interaction entity at each layer of the knowledge graph; Calculate the correlation between the historically retrieved interaction entities and the neighbor entities at each layer, and multiply the correlation with the neighbor entities at the previous layer to obtain the entity response at each layer; The hierarchical neighborhood summation is performed on the entity responses of each layer to obtain the retrieval interest features.

[0011] Furthermore, after weighted calculation is performed on the first prediction result and the second prediction result, the prediction result deviation value is fused with the weighted calculation result, and the final project data retrieval recommendation result is output according to the fused prediction value, specifically: ,in, represents the predicted value after fusion; represents the weight value, ; Respectively represent the first prediction result and the second prediction result; Indicates the deviation value of the prediction result.

[0012] The present invention also provides a scientific and technological innovation project data retrieval and recommendation system, which is used in any one of the above-mentioned scientific and technological innovation project data retrieval and recommendation methods, and the system includes: The knowledge graph construction module is used to obtain scientific and technological innovation project data and construct a knowledge graph using each entity in the scientific and technological innovation project data as a graph node; A first prediction module is used to extract the search entity in the search information, determine the mapping entity corresponding to the search entity in the knowledge graph, calculate the association strength between the mapping entity and the entity having a relationship in the knowledge graph, construct a neighborhood representation of the mapping entity based on the association strength, and perform feature expansion on the mapping entity according to the neighborhood representation, input the mapping entity after feature expansion into a pre-constructed first prediction model, and calculate and obtain a first prediction result; The second prediction module is used to propagate the historical retrieval interaction entity in the knowledge graph according to the entity connection relationship, obtain the neighbor entity of the historical retrieval interaction entity in each layer of the knowledge graph, calculate the correlation between the historical retrieval interaction entity and the neighbor entity of each layer, and aggregate all the calculated correlations to obtain the retrieval interest feature, input the retrieval interest feature into the pre-built second prediction model, and calculate the second prediction result; The retrieval recommendation module is used to calculate the relative entropy between the first prediction result and the second prediction result, obtain the prediction result deviation value, perform weighted calculation on the first prediction result and the second prediction result, fuse the prediction result deviation value with the weighted calculation result, and output the final project data retrieval recommendation result according to the fused prediction value.

[0013] The present invention also provides a computer device, comprising a system memory and a processor, wherein the system memory stores a computer program, and the processor implements the steps of any one of the above-mentioned methods when executing the computer program.

[0014] The present invention also provides a computer-readable storage medium having a computer program stored thereon, and when the computer program is executed by a processor, the steps of any one of the methods described above are implemented.

[0015] The present invention also provides a computer program product comprising instructions, and when the instructions are executed by a computer device cluster, the computer device cluster executes any one of the above methods.

[0016] Compared with the prior art, the present invention has the following advantages and beneficial effects: In the present invention, by constructing a knowledge graph with scientific and technological innovation project data, it is convenient to integrate the data with relevance in each section, and to facilitate the call of subsequent data recommendation; at the same time, the mapping entity corresponding to the retrieval entity is determined in the knowledge graph, and the association strength between the mapping entity and the entity with relationship in the knowledge graph is calculated, and then the neighborhood representation of the mapping entity is constructed based on the association strength, and the feature of the mapping entity is expanded according to the neighborhood representation, which can increase the feature representation of the entity in the data retrieval process, enrich the semantic information of the recommended data, avoid recommendation deviation and lack of data recommendation due to insufficient information, and solve the technical problems that data matching of the same topic under different sections is difficult, it is difficult to generate complete data reports or identification materials, and it is impossible to provide data support efficiently and accurately. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] In order to more clearly illustrate the technical solutions of the exemplary embodiments of the present invention, the following briefly introduces the drawings required for use in the embodiments. It should be understood that the following drawings only illustrate certain embodiments of the present invention and should not be regarded as limiting the scope. For those of ordinary skill in the art, other relevant drawings can be obtained based on these drawings without creative work. In the drawings: Figure 1 This is a schematic diagram of a method flow of a method for retrieving and recommending scientific and technological innovation project data in this embodiment; Figure 2 This is a schematic diagram of system modules of a scientific and technological innovation project data retrieval and recommendation system in this embodiment; Figure 3 It is a structural schematic diagram of a computer device in this embodiment. DETAILED DESCRIPTION

[0018] The following is a description of exemplary embodiments of the present disclosure in conjunction with the accompanying drawings, including various details of the embodiments of the present disclosure to facilitate understanding, which should be considered as merely exemplary. Therefore, it should be recognized by those of ordinary skill in the art that various changes and modifications may be made to the embodiments described herein without departing from the scope of the present disclosure. Similarly, for the sake of clarity and conciseness, the description of well-known functions and structures is omitted in the following description.

[0019] In the present disclosure, unless otherwise specified, the use of the terms "first", "second", etc. to describe various elements is not intended to limit the positional relationship, timing relationship, or importance relationship of these elements, and such terms are only used to distinguish one element from another element. In some examples, the first element and the second element may refer to the same instance of the element, and in some cases, based on the description of the context, they may also refer to different instances.

[0020] The terms used in the description of various examples in this disclosure are only for the purpose of describing specific examples and are not intended to be limiting. Unless the context clearly indicates otherwise, if the number of elements is not specifically limited, the element can be one or more. In addition, the term "and / or" used in this disclosure covers any one of the listed items and all possible combinations.

[0021] Example 1

[0022] See also Figure 1 As shown, this embodiment provides a method for retrieving and recommending scientific and technological innovation project data, the method comprising: S1: Obtain scientific and technological innovation project data, and use each entity in the scientific and technological innovation project data as a graph node to construct a knowledge graph; It should be noted that, in this embodiment, the scientific and technological innovation project data is stored in the database of the scientific and technological innovation management system, and the scientific and technological innovation project data is extracted from the database, wherein the scientific and technological innovation project data includes R&D activity data, technical product data and intellectual property data, wherein the R&D activity data includes R&D activity number information, R&D activity name information, technical field information, technical source information and technical purpose, etc.; the technical product data includes product number information, technical source information, key technology information and technical neighborhood information, etc.; the intellectual property data includes intellectual property name information, intellectual property category information, application year information, intellectual property number information, authorized patent number information and authorization date information, etc.; in other embodiments, other information data may also be included, which will not be described in detail here; At the same time, the entities in the knowledge graph include project entities and indicator entities. The indicator entity is composed of various data information indicators in the scientific and technological innovation project data. Therefore, the entities in the knowledge graph correspond one-to-one to the scientific and technological innovation project data; at the same time, the knowledge graph is composed of head entity, tail entity and relationship, which can be described by the triple <head entity, relationship, tail entity>.

[0023] S2: extracting the search entity in the search information, determining the mapping entity corresponding to the search entity in the knowledge graph, calculating the association strength between the mapping entity and the entity having a relationship in the knowledge graph, constructing a neighborhood representation of the mapping entity based on the association strength, and performing feature expansion on the mapping entity according to the neighborhood representation, inputting the mapping entity after feature expansion into the pre-constructed first prediction model, and calculating and obtaining a first prediction result; Specifically, in this embodiment, the search entity in the search information is first extracted, and the mapping entity corresponding to the search entity is determined in the knowledge graph; It should be noted that, in this embodiment, the search information can be obtained by the user inputting the search formula, or automatically obtained by the system according to the form generation requirement. This technical means is a conventional technical means and will not be described in detail here. At the same time, the search entities extracted can be 1, 2, 3 or more numbers, depending on the actual search information. At the same time, it should be noted that in this embodiment, the mapping entity represents the entity found in the knowledge graph that corresponds to the retrieval entity; for example, the retrieval entity is: "transportation", "rotating device", etc., and the corresponding entity found in the knowledge graph based on these retrieval entities is the mapping entity.

[0024] Calculating a first association strength between a mapping entity and a project entity having a relationship, and calculating a second association strength between the mapping entity and an indicator entity having a relationship, and constructing a neighborhood representation of the mapping entity based on the first association strength and the second association strength; It should be noted that, in this embodiment, the project entity or indicator entity having a relationship with the mapping entity means that the mapping entity is a head entity or a tail entity in a triple, and the other head entity or the tail entity of the same triple is the project entity or the indicator entity having a relationship with the mapping entity; Calculate the first association strength between the mapping entity and the project entity with the relationship: The inner product method is needed to calculate the number of associations between the mapping entity and the project entity under the relationship, and then normalize all the association numbers with the mapping entity to calculate the first association strength between the mapping entity and the project entity with the relationship , ,in, Represents mapping entities and project entities The number of associations between Represents an entity set, Represents a mapping entity, An entity set representing a mapping entity connection; Calculate the second association strength between the mapping entity and the indicator entity with a relationship: filter the indicator entity with a relationship with the mapping entity in the knowledge graph, and find all the relationship paths from the mapping entity to the indicator entity; use the pre-built knowledge representation model to calculate the semantic similarity of all relationship paths, specifically: ,in, Indicates the number of hops in the relationship path; Indicates The weight of the jump; Indicates Semantic similarity of jumps; Represents an indicator entity; Represents a relationship path; Represents the mapping entity; the semantic similarity of all relationship paths is aggregated using the average pooling algorithm to obtain the second association strength, wherein the path aggregation weight of each relationship path is dynamically adjusted through the attention mechanism, specifically: ,in, represents the second association strength; represents the path aggregation weight; It should be noted that, in this embodiment, the pre-built knowledge representation model is obtained by training the TransE model.

[0025] Construct a neighborhood representation of the mapping entity based on the first association strength and the second association strength: Multiply to get the vector of triplets, and for entities with the same head The vector sum of the triples is used as the outer neighborhood representation , .

[0026] The neighborhood representation and the mapping entity are aggregated using the SUM aggregation method to obtain the mapping entity after feature expansion. , ,in, represents a nonlinear function; denote weight and bias respectively.

[0027] The mapping entity after feature expansion is input into the pre-built first prediction model, and the first prediction result is calculated. It should be noted that in this embodiment, the first prediction model is a KGCN model, which is a knowledge graph convolutional neural network model. In this retrieval recommendation method, it is used to predict the recommended data based on the mapping entity after feature expansion; it improves the accuracy and diversity of recommendations by combining the structural information of the knowledge graph and the personalized preferences of users; this technical solution is a conventional technical means and will not be elaborated here.

[0028] S3: Propagate the historical retrieval interaction entity in the knowledge graph according to the entity connection relationship, obtain the neighbor entity of the historical retrieval interaction entity in each layer of the knowledge graph, calculate the correlation between the historical retrieval interaction entity and the neighbor entity in each layer, and aggregate all the calculated correlations to obtain the retrieval interest feature, input the retrieval interest feature into the pre-built second prediction model, and calculate the second prediction result; Specifically, in this embodiment, the historical retrieval interaction entity is taken as the starting point, and propagated in the knowledge graph according to the entity connection relationship to obtain the neighbor entities of the historical retrieval interaction entity at each layer of the knowledge graph; Calculate the correlation between historical retrieval interaction entities and neighbor entities at each layer , ,in, The transpose of the vector representation representing the historical retrieval interaction entity, Indicates relationship vector, Indicates The entity vector of each layer is obtained by multiplying the correlation with the neighbor entity of the previous layer. The hierarchical neighborhood summation of the entity response of each layer is performed to obtain the retrieval interest feature. The retrieval interest features are input into the pre-built second prediction model, and the second prediction result is calculated. It should be noted that, in this embodiment, the second prediction model adopts the RippleNet model, which is a recommendation model based on the knowledge graph. It expands the user's interest features by the user's historical retrieval features. This technical solution is a conventional technical solution in this field and will not be elaborated here.

[0029] S4: Calculate the relative entropy between the first prediction result and the second prediction result to obtain a prediction result deviation value, perform weighted calculation on the first prediction result and the second prediction result, fuse the prediction result deviation value with the weighted calculation result, and output the final project data retrieval recommendation result according to the fused prediction value.

[0030] Specifically, in this embodiment, by constructing a knowledge graph from the data of scientific and technological innovation projects, it is convenient to integrate the data with relevance in each section and facilitate the subsequent call of data recommendation; at the same time, the mapping entity corresponding to the retrieval entity is determined in the knowledge graph, and the association strength between the mapping entity and the entity with relationship in the knowledge graph is calculated, and then the neighborhood representation of the mapping entity is constructed based on the association strength, and the feature of the mapping entity is expanded according to the neighborhood representation. This can increase the feature representation of the entity during the data retrieval process, enrich the semantic information of the recommended data, avoid recommendation bias and missing data recommendation due to insufficient information, and solve the technical problems that data matching of the same topic under different sections is difficult, it is difficult to generate complete data reports or certification materials, and it is impossible to provide data support efficiently and accurately.

[0031] As a possible implementation, the method further includes: Calculate the relative entropy between the first prediction result and the second prediction result to obtain a prediction result deviation value; After weighted calculation of the first prediction result and the second prediction result, the prediction result deviation value is fused with the weighted calculation result, and the final project data retrieval recommendation result is output according to the fused prediction value, specifically: ,in, represents the predicted value after fusion; represents the weight value, ; Respectively represent the first prediction result and the second prediction result; Indicates the deviation value of the prediction result.

[0032] Specifically, in this embodiment, the prediction result deviation value obtained by calculating the relative entropy between the first prediction result and the second prediction result is used to balance the deviation of the two prediction results, so that the data recommendation result is more consistent with the search information.

[0033] Example 2

[0034] See also Figure 2 As shown, the present invention also provides a scientific and technological innovation project data retrieval and recommendation system, which is used in any one of the above-mentioned scientific and technological innovation project data retrieval and recommendation methods, and the system includes: The knowledge graph construction module 100 is used to obtain scientific and technological innovation project data and construct a knowledge graph using each entity in the scientific and technological innovation project data as a graph node; The first prediction module 200 is used to extract the search entity in the search information, determine the mapping entity corresponding to the search entity in the knowledge graph, calculate the association strength between the mapping entity and the entity having a relationship in the knowledge graph, construct a neighborhood representation of the mapping entity based on the association strength, and perform feature expansion on the mapping entity according to the neighborhood representation, input the mapping entity after feature expansion into the pre-constructed first prediction model, and calculate and obtain a first prediction result; The second prediction module 300 is used to propagate the historical retrieval interaction entity in the knowledge graph according to the entity connection relationship, obtain the neighbor entity of the historical retrieval interaction entity in each layer of the knowledge graph, calculate the correlation between the historical retrieval interaction entity and the neighbor entity of each layer, and aggregate all the calculated correlations to obtain the retrieval interest feature, input the retrieval interest feature into the pre-constructed second prediction model, and calculate the second prediction result; The retrieval recommendation module 400 is used to calculate the relative entropy between the first prediction result and the second prediction result, obtain the prediction result deviation value, perform weighted calculation on the first prediction result and the second prediction result, fuse the prediction result deviation value with the weighted calculation result, and output the final project data retrieval recommendation result according to the fused prediction value.

[0035] It should be noted that the modules in the system of Example 2 correspond to the steps in the method of Example 1. The steps in the method of Example 1 have been described in detail in Example 1. In this Example 2, the contents of the modules in the system will not be described in detail.

[0036] Example 3

[0037] See also Figure 3As shown, this embodiment further provides a computer device, including a system memory 1005 and a processor 1001, wherein the system memory 1005 stores a computer program, and the processor 1001 implements the steps of any of the above methods when executing the computer program.

[0038] It should be noted that the processor 1001 is used to execute the steps in the above method embodiments according to the instructions in the program code. Alternatively, the processor 1001 implements the functions of each module / unit in the above system / device embodiments when executing the computer program.

[0039] Specifically, in this embodiment, the computer program may be divided into one or more modules / units, one or more modules / units are stored in the system memory 1005, and are executed by the processor 1001 to complete the present application. One or more modules / units may be a series of computer program instruction segments that can complete specific functions, and the instruction segments are used to describe the execution process of the computer program in the terminal device.

[0040] The terminal device may be a computing device such as a desktop computer, a notebook, a PDA, a cloud server, etc. The terminal device may include, but is not limited to, a processor 1001 and a system memory 1005. Those skilled in the art will appreciate that this does not constitute a limitation on the terminal device, and may include more or less components than shown in the figure, or combine certain components, or different components. For example, the terminal device may also include an input / output device 1003, a network access device 1002, a bus 1006, etc.

[0041] The processor 1001 may be a central processing unit (CPU), or other general-purpose processors, digital signal processors (DSP), application-specific integrated circuits (ASIC), field-programmable gate arrays (FPGA), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. A general-purpose processor may be a microprocessor or any conventional processor, etc.

[0042] The system memory 1005 may be an internal storage unit of the terminal device, such as a hard disk or memory of the terminal device. The system memory 1005 may also be a storage device 1004 of the terminal device, such as a plug-in hard disk, a smart memory card (SmartMedia Card, SMC), a secure digital (Secure Digital, SD) card, a flash card (FlashCard), etc. equipped on the terminal device. Further, the system memory 1005 may also include both the internal storage unit of the terminal device and the storage device 1004. The system memory 1005 is used to store computer programs and other programs and data required by the terminal device. The system memory 1005 may also be used to temporarily store data that has been output or is to be output.

[0043] Those skilled in the art can clearly understand that, for the convenience and brevity of description, the specific working processes of the above-described systems, systems and units can refer to the corresponding processes in the aforementioned method embodiments and will not be repeated here.

[0044] Example 4

[0045] This embodiment provides a computer-readable storage medium on which a computer program is stored. When the computer program is executed by a processor, the steps of any one of the above methods are implemented.

[0046] Among them, the computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared or semiconductor system, system or device, or any combination of the above. More specific examples of computer-readable storage media (a non-exhaustive list) include: an electrical connection with one or more wires, a portable computer disk, a hard disk. Random Access Memory (RAM), Read-Only Memory (ROM), Erasable Programmable Read Only Memory (EPROM), registers, hard disks, optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the above, or any other form of computer-readable storage medium known in the art.

[0047] An exemplary storage medium is coupled to a processor so that the processor can read information from the storage medium and write information to the storage medium. Of course, the storage medium can also be a component of the processor. The processor and the storage medium can be located in an application-specific integrated circuit (ASIC). In an embodiment of the present invention, a computer-readable storage medium can be any tangible medium containing or storing a program that can be used by or in combination with an instruction execution system, system, or device.

[0048] Example 5

[0049] This embodiment also provides a computer program product including instructions. When the instructions are executed by a computer device cluster, the computer device cluster executes the method described in Embodiment 1.

[0050] The specific implementation methods described above further illustrate the objectives, technical solutions and beneficial effects of the present invention in detail. It should be understood that the above description is only a specific implementation method of the present invention and is not intended to limit the scope of protection of the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the scope of protection of the present invention.

Claims

1. A method for retrieving and recommending scientific and technological innovation project data, characterized in that: include: Obtain scientific and technological innovation project data, and use each entity in the scientific and technological innovation project data as a graph node to construct a knowledge graph; Extract the search entity in the search information, determine the mapping entity corresponding to the search entity in the knowledge graph, calculate the association strength between the mapping entity and the entity with relationship in the knowledge graph, construct a neighborhood representation of the mapping entity based on the association strength, and perform feature expansion on the mapping entity according to the neighborhood representation, input the mapping entity after feature expansion into the pre-constructed first prediction model, and calculate and obtain the first prediction result; The historical retrieval interaction entity is propagated in the knowledge graph according to the entity connection relationship to obtain the neighbor entity of the historical retrieval interaction entity in each layer of the knowledge graph, the correlation between the historical retrieval interaction entity and the neighbor entity of each layer is calculated, and all the calculated correlations are aggregated to obtain the retrieval interest feature, and the retrieval interest feature is input into the pre-built second prediction model to calculate the second prediction result; The relative entropy between the first prediction result and the second prediction result is calculated to obtain the prediction result deviation value, and the first prediction result and the second prediction result are weightedly calculated, the prediction result deviation value is fused with the weighted calculation result, and the final project data retrieval recommendation result is output according to the fused prediction value.

2. A method for retrieving and recommending scientific and technological innovation project data according to claim 1, characterized in that: The entities in the knowledge graph include indicator entities and project entities.

3. A method for retrieving and recommending scientific and technological innovation project data according to claim 2, characterized in that: Extract the search entity in the search information, determine the mapping entity corresponding to the search entity in the knowledge graph, calculate the association strength between the mapping entity and the entity with relationship in the knowledge graph, build a neighborhood representation of the mapping entity based on the association strength, and expand the features of the mapping entity according to the neighborhood representation, specifically: Extract the search entity in the search information and determine the mapping entity corresponding to the search entity in the knowledge graph; Calculating a first association strength between a mapping entity and a project entity having a relationship, and calculating a second association strength between the mapping entity and an indicator entity having a relationship, and constructing a neighborhood representation of the mapping entity based on the first association strength and the second association strength; The neighborhood representation and the mapping entity are aggregated using the SUM aggregation method to obtain the mapping entity after feature expansion.

4. A method for retrieving and recommending scientific and technological innovation project data according to claim 3, characterized in that: The second association strength between the mapping entity and the indicator entity having a relationship is calculated, specifically: Filter indicator entities that have relationships with mapping entities in the knowledge graph, and find all relationship paths from mapping entities to indicator entities; The pre-built knowledge representation model is used to calculate the semantic similarity of all relationship paths, specifically: ,in, Indicates the number of hops in the relationship path; Indicates The weight of the jump; Indicates Semantic similarity of jumps; Represents an indicator entity; Represents a relationship path; Representation mapping entity, the pre-built knowledge representation model is obtained by training the TransE model; The average pooling algorithm is used to aggregate the semantic similarities of all relationship paths to obtain the second association strength, wherein the path aggregation weight of each relationship path is dynamically adjusted through the attention mechanism, specifically: ,in, represents the second association strength; Indicates the path aggregation weight.

5. The method for retrieving and recommending scientific and technological innovation project data according to claim 1, characterized in that: The historical retrieval interaction entities are propagated in the knowledge graph according to the entity connection relationship to obtain the neighbor entities of the historical retrieval interaction entities in each layer of the knowledge graph, and the correlation between the historical retrieval interaction entities and the neighbor entities in each layer is calculated. All the calculated correlations are aggregated to obtain the retrieval interest features, which are as follows: Taking the historical retrieval interaction entity as the starting point, propagate it in the knowledge graph according to the entity connection relationship to obtain the neighbor entities of the historical retrieval interaction entity at each layer of the knowledge graph; Calculate the correlation between the historically retrieved interaction entities and the neighbor entities at each layer, and multiply the correlation with the neighbor entities at the previous layer to obtain the entity response at each layer; The hierarchical neighborhood summation is performed on the entity responses of each layer to obtain the retrieval interest features.

6. A method for retrieving and recommending scientific and technological innovation project data according to claim 2, characterized in that: After weighted calculation of the first prediction result and the second prediction result, the prediction result deviation value is fused with the weighted calculation result, and the final project data retrieval recommendation result is output according to the fused prediction value, specifically: ,in, represents the predicted value after fusion; represents the weight value, ; Respectively represent the first prediction result and the second prediction result; Indicates the deviation value of the prediction result.

7. A scientific and technological innovation project data retrieval and recommendation system, characterized in that: The system is used in a method for retrieving and recommending scientific and technological innovation project data as described in any one of claims 1 to 6, and the system comprises: The knowledge graph construction module is used to obtain scientific and technological innovation project data and construct a knowledge graph using each entity in the scientific and technological innovation project data as a graph node; A first prediction module is used to extract the search entity in the search information, determine the mapping entity corresponding to the search entity in the knowledge graph, calculate the association strength between the mapping entity and the entity having a relationship in the knowledge graph, construct a neighborhood representation of the mapping entity based on the association strength, and perform feature expansion on the mapping entity according to the neighborhood representation, input the mapping entity after feature expansion into a pre-constructed first prediction model, and calculate and obtain a first prediction result; The second prediction module is used to propagate the historical retrieval interaction entity in the knowledge graph according to the entity connection relationship, obtain the neighbor entity of the historical retrieval interaction entity in each layer of the knowledge graph, calculate the correlation between the historical retrieval interaction entity and the neighbor entity of each layer, and aggregate all the calculated correlations to obtain the retrieval interest feature, input the retrieval interest feature into the pre-built second prediction model, and calculate the second prediction result; The retrieval recommendation module is used to calculate the relative entropy between the first prediction result and the second prediction result, obtain the prediction result deviation value, perform weighted calculation on the first prediction result and the second prediction result, fuse the prediction result deviation value with the weighted calculation result, and output the final project data retrieval recommendation result according to the fused prediction value.

8. A computer device comprising a system memory and a processor, wherein the system memory stores a computer program, wherein: When the processor executes the computer program, the steps of the method according to any one of claims 1 to 6 are implemented.

9. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the steps of the method according to any one of claims 1 to 6 are implemented.

10. A computer program product comprising instructions, characterized in that When the instructions are executed by a computer device cluster, the computer device cluster executes the method according to any one of claims 1 to 6.

Images