Enhancing information retrieval and resource discovery from data using the Semantic Web

Jordanous, Anna (2015) Enhancing information retrieval and resource discovery from data using the Semantic Web. In: 4th International Symposium on Emerging Trends and Technologies in Libraries and Information Services, 6-8 Jan 2015, Noida, UP, India. (Full text available)

Abstract

Data are everywhere. Often the sheer quantities of data that we store or archive in repositories or digital libraries make it difficult to navigate data; information in the data is obscured, particularly where we have Big Data. Traditionally, we record metadata on our data items to assist data classification and some information retrieval. The Semantic Web enables us to further unlock and enrich our data, by exploring how different data are related or connected. Using ontologies and Linked Data we can declare, navigate and discover semantic relationships. Relationships exist both locally, within the data, and globally, such that we can enhance our data with information retrieved from a wider context. To illustrate how the application of Semantic Web technologies aids data discovery and information retrieval, I discuss two case studies: (1) Sharing Ancient Wisdoms (SAWS), a Dynamic Library of information on selected ancient wisdom literature; and (2) the DEFRA DTC archive, a repository of data about freshwater quality in the UK.

Item Type: Conference or workshop item (Keynote)
Uncontrolled keywords: data repositories; digital libraries; information retrieval and discovery; semantic web; linked data; ontologies;
Subjects: Q Science > QA Mathematics (inc Computing science) > QA 75 Electronic computers. Computer science
Q Science > QA Mathematics (inc Computing science) > QA 76 Software, computer programming, > QA76.76 Computer software > QA76.76.I59 Interactive media, hypermedia
Z Bibliography. Library Science. Information Resources > Z665 Library Science. Information Science
Z Bibliography. Library Science. Information Resources > ZA Information resources
Divisions: Faculties > Sciences > School of Computing > Computational Intelligence Group
Faculties > Sciences > School of Computing > Data Science
Depositing User: Anna Jordanous
Date Deposited: 07 Jan 2015 07:25 UTC
Last Modified: 27 Apr 2016 16:05 UTC
Resource URI: https://kar.kent.ac.uk/id/eprint/46568 (The current URI for this page, for reference purposes)
Jordanous, Anna: https://orcid.org/0000-0003-2076-8642
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