Crowd-annotation and LoD-based semantic indexing of content in multi-disciplinary web repositories to improve search results

Khan, Arshad and Tiropanis, Thanassis and Martin, David (2017) Crowd-annotation and LoD-based semantic indexing of content in multi-disciplinary web repositories to improve search results. In: ACSW '17 Proceedings of the Australasian Computer Science Week Multiconference, January 30 - February 03, 2017, Geelong, Australia.

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Official URL: http://dl.acm.org/citation.cfm?id=3014867

Abstract

Searching for relevant information in multi-disciplinary web repositories is becoming a topic of increasing interest among the computer science research community. To date, methods and techniques to extract useful and relevant information from online repositories of research data have largely been based on static full text indexing which entails a ‘produce once and use forever’ kind of strategy. That strategy is fast becoming insufficient due to increasing data volume, concept obsolescence, and complexity and heterogeneity of content types in web repositories. We propose that by automatic semantic annotation of content in web repositories (using Linked Open Data or LoD sources) without using domain-specific ontologies, we can sustain the performance of searching by retrieving highly relevant search results. Secondly, we claim that by expert crowd-annotation of content on top of automatic semantic annotation, we can enrich the semantic index over time to augment the contextual value of content in web repositories so that they remain findable despite changes in language, terminology and scientific concepts. We deployed a custom- built annotation, indexing and searching environment in a web repository website that has been used by expert annotators to annotate webpages using free text and vocabulary terms. We present our findings based on the annotation and tagging data on top of LoD-based annotations and the overall modus operandi. We also analyze and demonstrate that by adding expert annotations to the existing semantic index, we can improve the relationship between query and documents using Cosine Similarity Measures (CSM).

Item Type:Conference or Workshop Item (Paper)
Subjects:2. Data Collection > 2.6 Observation
2. Data Collection > 2.11 Online Data Collection
7. ICT and Software > 7.3 Technology
7. ICT and Software > 7.4 ICT and Software (other)
ID Code:4004
Deposited By: Mr. Arshad Khan
Deposited On:10 Feb 2017 14:43
Last Modified:10 Feb 2017 14:43

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