Aspect-based Academic Search using Domain-specific KB
This addresses the need for more precise document retrieval in academic search when users specify aspects, offering an incremental improvement over existing keyword-based approaches.
The paper tackles the problem of aspect-based academic search by proposing a method that uses a domain-specific knowledge base to estimate language models for both the query and aspect, then mixes them to rank documents. Evaluation on the Open Research Corpus dataset shows it outperforms keyword-based expansion methods, including those with relevance feedback.
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.