Exploiting semantic annotation of content with Linked Data to improve searching performance in web repositories
Year: 2015
Type: conference-paper
Abstract: Searching for relevant information in web repositories of multi-disciplinary scientific research data is becoming a challenge for research communities such as the Social Sciences. Researchers use the available keywords-based online search which often fall short of producing the desired search results due to known issues of content heterogeneity, volume of data and terminological obsolescence. This leads to a number of problems including insufficient content exposure, unsatisfied researchers and lack of trust in such repositories of valuable knowledge. This research explores the appropriateness of alternative searching based on Linked Open Data (LoD)-based semantic annotation and indexing in online repositories such as the ReStore repository (www.restore.ac.uk) containing content from multiple Social Science research methods projects . We explore websites content annotations using LoD to generate contemporary semantic annotations. We investigate whether we can improve accuracy and relevance in search results affected by concepts and terms obsolescence in repositories of scientific content.
Authors: Arshad Khan, Thanassis Tiropanis, David Martín
Language: English
Topic: Semantic Web and Ontologies
Subfield: Artificial Intelligence
Field: Computer Science
Domain: Physical Sciences
Open Access status: green
OpenAlex ID: W2603699296