Exploiting inference from semantic annotations for information retrieval

Zuccon, Guido, Koopman, Bevan, & Bruza, Peter D. (2014) Exploiting inference from semantic annotations for information retrieval. In Proceedings of the 7th International Workshop on Exploiting Semantic Annotations in Information Retrieval ( ESAIR '14), ACM, Shanghai, China, pp. 43-45.

View at publisher


The increasing amount of information that is annotated against standardised semantic resources offers opportunities to incorporate sophisticated levels of reasoning, or inference, into the retrieval process. In this position paper, we reflect on the need to incorporate semantic inference into retrieval (in particular for medical information retrieval) as well as previous attempts that have been made so far with mixed success. Medical information retrieval is a fertile ground for testing inference mechanisms to augment retrieval. The medical domain offers a plethora of carefully curated, structured, semantic resources, along with well established entity extraction and linking tools, and search topics that intuitively require a number of different inferential processes (e.g., conceptual similarity, conceptual implication, etc.). We argue that integrating semantic inference in information retrieval has the potential to uncover a large amount of information that otherwise would be inaccessible; but inference is also risky and, if not used cautiously, can harm retrieval.

Impact and interest:

0 citations in Scopus
Search Google Scholar™

Citation counts are sourced monthly from Scopus and Web of Science® citation databases.

These databases contain citations from different subsets of available publications and different time periods and thus the citation count from each is usually different. Some works are not in either database and no count is displayed. Scopus includes citations from articles published in 1996 onwards, and Web of Science® generally from 1980 onwards.

Citations counts from the Google Scholar™ indexing service can be viewed at the linked Google Scholar™ search.

ID Code: 84099
Item Type: Conference Paper
Refereed: Yes
DOI: 10.1145/2663712.2666197
ISBN: 9781450313650
Divisions: Current > Schools > School of Information Systems
Current > QUT Faculties and Divisions > Science & Engineering Faculty
Copyright Owner: Copyright 2014 ACM
Deposited On: 12 May 2015 22:23
Last Modified: 13 May 2015 23:18

Export: EndNote | Dublin Core | BibTeX

Repository Staff Only: item control page