Document Retrieval using Predication Similarity

TL;DR

This paper introduces a novel document retrieval method that uses predication triples and hierarchical ontology information to improve semantic matching, demonstrating competitive performance in biomedical retrieval tasks.

Contribution

The paper presents a new predication-based similarity approach for document retrieval that leverages ontologies for enhanced semantic understanding.

Findings

Predication similarity improves retrieval precision.

The approach is competitive with existing state-of-the-art methods.

Hierarchical ontology information enhances concept similarity measurement.

Abstract

Document retrieval has been an important research problem over many years in the information retrieval community. State-of-the-art techniques utilize various methods in matching documents to a given document including keywords, phrases, and annotations. In this paper, we propose a new approach for document retrieval that utilizes predications (subject-predicate-object triples) extracted from the documents. We represent documents as sets of predications. We measure the similarity between predications to compute the similarity between documents. Our approach utilizes the hierarchical information available in ontologies in computing concept-concept similarity, making the approach flexible. Predication-based document similarity is more precise and forms the basis for a semantically aware document retrieval system. We show that the approach is competitive with an existing state-of-the-art related document retrieval technique in the biomedical domain.