Knowledge graph generation method for online resource related information extraction

A technology of knowledge graph and related information, applied in the field of information extraction in natural language processing, can solve the problems of limited extraction accuracy, time-consuming and laborious online resource knowledge graph, insufficient description of online resource attribute description, etc., so as to improve construction efficiency and reduce The effect of labor costs

Active Publication Date: 2021-01-29
BEIJING INSTITUTE OF TECHNOLOGYGY
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0005] The present invention aims to solve the problem of limited accuracy in extracting online resource-related information in scientific and technological literature by using existing entity and relationship extraction technologies, and the description of online

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  • Knowledge graph generation method for online resource related information extraction
  • Knowledge graph generation method for online resource related information extraction
  • Knowledge graph generation method for online resource related information extraction

Examples

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Example Embodiment

[0040]Example 1

[0041]First of all, it needs to be explained that the present invention is a method for generating knowledge graphs for extracting information related to online resources. The purpose of this invention is to extract fine-grained information related to resources in the context sentences of online resource citations in scientific literature, and generate knowledge based on the extracted information. Atlas. Fine-grained information extraction includes the extraction of online resource-related technology entities and the extraction of the relationship between target resource entities and online resource-related technology entities. This problem has the following formal definition: Given a context sentence containing online resource references s={w1,w2,...,wN}, for example, the context sentence "We selectedour vocabulary from terms(words and phrases)in WordNet lexicon", N is the length of the sentence word sequence (in the example given above, the context sentence consists...

Example Embodiment

[0097]Example 2

[0098](1) Experimental data settings

[0099]Before introducing the experimental data, firstly, the definition of the online resource-related scientific and technological entities and the resource-entity relationship as the object of fine-grained information extraction in the present invention is given.

[0100]For online resource-related technology entities related to target resource entities, there are six different entity categories, namely: Task, Method, Data, Metric, and Generic Term ). The detailed definition and examples of each technology entity category are as follows:

[0101]1) Task: includes the description of the scientific and technological entities that need to be solved, the system to be built, and specific application scenarios that can be completed as the goal.

[0102]For example.: Language modeling,relation classification,transductiveinference,tree parsing...

[0103]2) Method: Including algorithms, strategies, models, tools, software, code bases, frameworks, sys...

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Abstract

The invention provides a knowledge graph generation method for online resource related information extraction, and belongs to the technical field of natural language processing information extraction.The method comprises the following steps: enumerating an input online resource reference sentence to generate candidate spans, and learning token representation in the sentence based on a BERT encoder to obtain representation of each candidate span, so that two tasks of entity extraction and relationship extraction are converted into a classification calculation problem based on the span representation; weighting the target functions of the two tasks to obtain a joint target function, and performing joint training by using a multi-task learning strategy; a trained information extraction modelis applied to a large-scale scientific and technological literature corpus to generate a knowledge graph of online resources. According to the method, the problem of insufficient description of online resource attribute description by entity and relationship extraction is solved, the labor cost for constructing the online resource knowledge graph is reduced, and the knowledge graph generation efficiency is improved.

Description

technical field [0001] The invention relates to a method for generating a knowledge map for extracting related information of online resources, and relates to the technical field of information extraction in natural language processing. Background technique [0002] At present, the problem of metadata information extraction in scientific and technological literature has received more and more attention. However, in addition to common keywords, literature citations, scientific and technological entities, and entity relationships, online resources in scientific and technological literature are another important metadata. information, so far not received enough attention. [0003] With the continuous expansion of the scale of scientific and technological literature, the number of online resources cited in the literature is also increasing rapidly. How to discover, track and understand these online resources from the massive existing literature and the latest literature has beco...

Claims

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Application Information

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IPC IPC(8): G06F16/36
CPCG06F16/367
Inventor 冯冲赵赫唐雨馨
Owner BEIJING INSTITUTE OF TECHNOLOGYGY
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