JOURNAL ARTICLE

Improving Semantic Search through Entity-Based Document Ranking

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.

Keywords:
Computer science Information retrieval Ranking (information retrieval) Context (archaeology) Rank (graph theory) Knowledge base Learning to rank Semantic search Entity linking Base (topology) World Wide Web Search engine

Metrics

0
Cited By
0.00
FWCI (Field Weighted Citation Impact)
15
Refs
0.04
Citation Normalized Percentile
Is in top 1%
Is in top 10%

Topics

Semantic Web and Ontologies
Physical Sciences →  Computer Science →  Artificial Intelligence
Advanced Text Analysis Techniques
Physical Sciences →  Computer Science →  Artificial Intelligence
Topic Modeling
Physical Sciences →  Computer Science →  Artificial Intelligence

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