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- Title
融合多特征图及实体影响力的领域实体消歧.
- Authors
单晓欢; 齐鑫傲; 宋宝燕; 张浩林
- Abstract
Entity disambiguation is a key problem in natural language processing, aims to map ambiguous mentions in texts to target entities in the knowledge base. Existing approaches have several problems, such as only realizing single mention disambiguation, ignoring the influence of entity impact and similarity between candidate entities on disambiguation results, and increasing the computational complexity by redundant graph nodes. A domain entity disambiguation method combining multi-feature graph and entity influence is proposed. Taking the financial domain as an example, the financial domain knowledge base is constructed by extracting the keyword triads related to financial categories from CN・DBpedia. Then, it extracts mentions from financial activities, and screens out candidate entities fusing the similar features of string and semantic. It uses triples of the knowledge base to acquire relationship between entities within 2-hop, at the same time calculates similarity between candidate entities as edge w&ghts. The multi-features are fully integrated into the graph model to finish the multi-feature graph construction. Finally, it adopts dynamic decision strategy, PageRank algorithm and entity influence are used to calculate the comprehensive score of candidate entities in the multi-features graph. And then the disambiguation results with high reliability are obtained. Experimental results verify the accuracy and efficiency of the proposed method in the specific domain.
- Subjects
KNOWLEDGE base; ALGORITHMS
- Publication
Journal of Computer Engineering & Applications, 2023, Vol 59, Issue 5, p305
- ISSN
1002-8331
- Publication type
Article
- DOI
10.3778/j.issn.1002-8331.2109-0494