Improving Semantic Search through Entity-Based Document Ranking

Benjamin Großmann, Alexandru Todor, A. Paschke
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Abstract

Traditional keyword-based IR approaches take into account the document context only in a limited manner. In our paper we present a novel document ranking approach based on the semantic relationships between named entities. In the first step we annotate all documents with named entities from a knowledge base (for example people, places and organisations). In the next step these annotations in combination with the relationships from the knowledge base are used to rank documents in order to perform a semantic search. Documents that contain the specific named entity that was searched for as well as other strongly related entities, receive a higher ranking. The inclusion of the document context in the ranking approach achieves a higher precision in the Top-K results.
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通过基于实体的文档排序改进语义搜索
传统的基于关键字的IR方法只以有限的方式考虑文档上下文。在本文中,我们提出了一种基于命名实体之间语义关系的新型文档排序方法。在第一步中,我们用知识库中的命名实体(例如人员、地点和组织)注释所有文档。在下一步中,这些注释与知识库中的关系一起用于对文档进行排序,以便执行语义搜索。包含被搜索的特定命名实体以及其他强相关实体的文档会获得更高的排名。在排序方法中包含文档上下文可以在Top-K结果中获得更高的精度。
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