Boosting Document Retrieval with Knowledge Extraction and Linked Data

Given a document collection, Document Retrieval is the task of returning the most relevant documents for a specified user query. In this paper, we assess a document retrieval approach exploiting Linked Open Data and Knowledge Extraction techniques. Based on Natural Language Processing methods (e.g., Entity Linking, Frame Detection), knowledge extraction allows disambiguating the semantic content of queries and documents, linking it to established Linked Open Data resources (e.g., DBpedia, YAGO) from which additional semantic terms (entities, types, frames, temporal information) are imported to realize a semantic-based expansion of queries and documents. The approach, implemented in the KE4IR system, has been evaluated on different state-of-the-art datasets, on a total of 555 queries and with document collections spanning from few hundreds to more than a million of documents. The results show that the expansion with semantic content extracted from queries and documents enables consistently outperforming retrieval performances when only textual information is exploited; on a specific dataset for semantic search, KE4IR outperforms a reference ontology-based search system. The experiments also validate the feasibility of applying knowledge extraction techniques for document retrieval — i.e., processing the document collection, building the expanded index, and searching over it — on large collections (e.g., TREC WT10g).