Aspect-based Academic Search using Domain-specific KB

TL;DR

This paper introduces a novel aspect-based academic search method that leverages domain-specific knowledge bases to improve relevance in search results, outperforming traditional keyword expansion techniques.

Contribution

The paper proposes a new aspect-based retrieval approach using domain-specific knowledge bases to better estimate relevance, advancing academic search capabilities.

Findings

Outperforms keyword-based query expansion in relevance

Uses a mixture of language models for query and aspect

Effective on the Open Research Corpus dataset

Abstract

Academic search engines allow scientists to explore related work relevant to a given query. Often, the user is also aware of the "aspect" to retrieve a relevant document. In such cases, existing search engines can be used by expanding the query with terms describing that aspect. However, this approach does not guarantee good results since plain keyword matches do not always imply relevance. To address this issue, we define and solve a novel academic search task, called "aspect-based retrieval", which allows the user to specify the aspect along with the query to retrieve a ranked list of relevant documents. The primary idea is to estimate a language model for the aspect as well as the query using a domain-specific knowledge base and use a mixture of the two to determine the relevance of the article. Our evaluation of the results over the Open Research Corpus dataset shows that our method outperforms keyword-based expansion of query with aspect with and without relevance feedback.