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JP-2026076035-A - Information processing device, information processing method, and program

JP2026076035AJP 2026076035 AJP2026076035 AJP 2026076035AJP-2026076035-A

Abstract

[Problem] To provide link prediction technology that enables lower-cost and more accurate predictions. [Solution] The information processing device includes an acquisition unit that acquires a query, a first generation unit that generates one or more triples by referring to the query, a second generation unit that generates a text from each of the one or more triples, a first calculation unit that acquires text embedding obtained using a trained natural language model for each of the generated texts and calculates a score for each of the text embeddings, a second calculation unit that calculates a score for the knowledge graph embedding of each of the one or more triples, and an aggregation unit that aggregates the scores calculated by the first calculation unit and the scores calculated by the second calculation unit. [Selection Diagram] Figure 1

Inventors

  • 赤木 健一朗

Assignees

  • 日本電気株式会社

Dates

Publication Date
20260511
Application Date
20241023

Claims (10)

  1. The means of obtaining the query, A first generation means that generates one or more triples by referring to the query, A second generation means for generating text from each of the one or more triples, For each generated sentence, a first calculation means obtains a sentence embedding using a trained natural language model and calculates a score for each of the sentence embeddings. A second calculation means for calculating the score of each of the one or more triples of the knowledge graph embedding, An information processing device comprising an aggregation means for aggregating the score calculated by the first calculation means and the score calculated by the second calculation means.
  2. The first calculation means is, The information processing apparatus according to claim 1, which calculates a score for each of the aforementioned text embeddings by inputting them into a machine learning-based score calculation model.
  3. The second calculation means is, The knowledge graph embedding for each of the one or more triples is calculated. The information processing device according to claim 2, which calculates the score by performing link predictions for each of the one or more triples with reference to the calculated knowledge graph embedding.
  4. The information processing apparatus according to any one of claims 1 to 3, wherein the query includes information relating to at least one of regimens, drugs, genes, and diseases.
  5. The information processing apparatus according to any one of claims 1 to 3, further comprising an output information generation means that generates output information to support decision-making by medical professionals by referring to the scores aggregated by the aggregation means.
  6. A first generation means that generates a group of triples including positive example triples and negative example triples by referring to a knowledge graph, A second generation means for generating a sentence from each of the triples included in the triple group, An information processing device comprising: a first learning means for obtaining a text embedding for each generated text using a trained natural language model, and training a scoring model that calculates a score for each of the text embeddings by referring to each of the text embeddings.
  7. Retrieving the query, To generate one or more triples by referring to the aforementioned query, To generate a text from each of the one or more triples, For each generated sentence, obtain a sentence embedding using a trained natural language model, and calculate a score for each of those sentence embeddings. Calculate the score for each of the one or more triples of the knowledge graph embedding, An information processing method that includes aggregating the score of the aforementioned text embedding and the score of the aforementioned knowledge graph embedding.
  8. By referring to the knowledge graph, a group of triples including positive and negative example triples is generated, To generate a sentence from each of the triples included in the aforementioned group of triples, An information processing method comprising obtaining a text embedding for each generated text using a trained natural language model, and training a scoring model that calculates a score for each of the text embeddings by referring to each of the text embeddings.
  9. A program for causing a computer to function as an information processing device according to claim 1, wherein the program causes the computer to function as the first generation means, the second generation means, the first calculation means, the second calculation means, and the aggregation means.
  10. A program for causing a computer to function as an information processing device according to claim 6, wherein the program causes the computer to function as the first generation means, the second generation means, and the first learning means.

Description

This invention relates to an information processing device, an information processing method, and a program. Various knowledge graph-based link prediction technologies are available that predict genes (proteins) related to diseases such as cancer. For example, Patent Document 1 discloses a technology for training a natural language model from automatically extracted triples and then achieving link prediction. Patent Document 2 discloses a technology for training nodes and documents in a knowledge graph. Japanese Patent Publication No. 2024-513293Japanese Patent Publication No. 2023-522822 This is a block diagram showing the configuration of the information processing device related to this disclosure.This is a flowchart showing the flow of the information processing method related to this disclosure.This diagram illustrates the basic structure of a knowledge graph.This is a block diagram showing the configuration of the information processing device related to this disclosure.This is a flowchart showing the flow of the information processing method related to this disclosure.This is a block diagram showing the configuration of the information processing system related to this disclosure.This is a flowchart showing the flow of the information processing method related to this disclosure.This is a flowchart showing the flow of the information processing method related to this disclosure.This is a diagram illustrating the information processing related to this disclosure.This is a diagram illustrating the information processing related to this disclosure.This is a diagram illustrating the information processing related to this disclosure.This is a block diagram showing the configuration of the information processing device related to this disclosure.This is a block diagram showing the hardware configuration of the information processing device related to this disclosure. The following are examples of embodiments of the present invention. However, the present invention is not limited to the exemplary embodiments shown below, and various modifications are possible within the scope of the claims. For example, embodiments obtained by appropriately combining some or all of the techniques (products or methods) employed in the exemplary embodiments shown below may also be included in the scope of the present invention. Furthermore, embodiments obtained by appropriately omitting some of the techniques employed in the exemplary embodiments shown below may also be included in the scope of the present invention. In addition, the effects mentioned in the exemplary embodiments shown below are examples of effects expected in those exemplary embodiments and do not define the scope of the present invention. That is, embodiments that do not produce the effects mentioned in the exemplary embodiments shown below may also be included in the scope of the present invention. [First Embodiment] A first exemplary embodiment, which is an example of an embodiment of the present invention, will be described in detail with reference to the drawings. This exemplary embodiment is the basic form for each of the exemplary embodiments described later. The scope of application of each technology adopted in this exemplary embodiment is not limited to this exemplary embodiment. That is, each technology adopted in this exemplary embodiment can also be adopted in other exemplary embodiments included in this disclosure, to the extent that no particular technical problems occur. Furthermore, each technology shown in the drawings referenced to explain this exemplary embodiment can also be adopted in other exemplary embodiments included in this disclosure, to the extent that no particular technical problems occur. (Configuration of Information Processing Device 1) The configuration of the information processing device 1 according to this exemplary embodiment will be described with reference to Figure 1. Figure 2 is a block diagram showing the configuration of the information processing device 1. As shown in Figure 1, the information processing device 1 includes an acquisition unit (means) 11, a first generation unit (means) 12, a second generation unit 13 (means), a first calculation unit (means) 14, a second calculation unit (means) 15, and an aggregation unit (means) 16. (Acquisition unit 11) The acquisition unit 11 acquires queries entered by the user. Here, the query may include, for example, multiple information fragments, each representing one or more targets, and each representing a relationship with any of those targets. The acquisition unit 11 extracts these information fragments from the acquired query. For example, if the acquisition unit 11 acquires the query "Which proteins are highly associated with disease 1?", it extracts "disease 1" as an information fragment representing a target and "highly associated" as an information fragment representing a relationship with that target. The acquisition unit may also further extract "protein" as a