Social Semantic Search: A Case Study on Web 2.0 for Science

Social Semantic Search: A Case Study on Web 2.0 for Science

Laurens De Vocht, Selver Softic, Ruben Verborgh, Erik Mannens, Martin Ebner
Copyright: © 2017 |Pages: 26
DOI: 10.4018/IJSWIS.2017100108
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When researchers formulate search queries to find relevant content on the Web, those queries typically consist of keywords that can only be matched in the content or its metadata. The Web of Data extends this functionality by bringing structure and giving well-defined meaning to the content and it enables humans and machines to work together using controlled vocabularies. Due the high degree of mismatches between the structure of the content and the vocabularies in different sources, searching over multiple heterogeneous repositories of structured data is considered challenging. Therefore, the authors present a semantic search engine for researchers facilitating search in research related Linked Data. To facilitate high-precision interactive search, they annotated and interlinked structured research data with ontologies from various repositories in an effective semantic model. Furthermore, the authors' system is adaptive as researchers can synchronize using new social media accounts and efficiently explore new datasets.
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1. Introduction

The evolution of Web 2.0 enabled many users via wikis, blogs and other content publishing platforms to become the main content providers on the web. The Web 2.0 for Science, also known as Science 2.0 or Research 2.0 aims to adapt the Web 2.0 for researchers. It entails a set of tools and services which researchers use to discover resources, such as academic publications or events they might be interested in, as an alternative to traditional search engines (De Vocht et al., 2011). The tools and services are typically API’s, publishing feeds, search and discovery services and interfaces designed based on social profiles (Parra & Duval, 2010; Ullmann et al., 2010). Research 2.0 comprises interacting with information published on Social Media, online collaboration platforms and other Web 2.0 tools. These platforms find more and more uptake (Van Noorden, 2014). The data is available under the form of posts, threads, tags and user information is transferable into semantic form, since widely used and accepted vocabularies for these domains exist. Weaving microblogs into the Web of Data is interesting from a researcher centric semantic search perspective. Twitter1, as exemplary microblog Social Media platform, can help resolving scientific citations (Weller et al., 2011).

Studies on the use of microblogs like Twitter during conferences within the science community showed that researchers were using Twitter to discuss and asynchronously communicated on topics during conferences (Ebner et al., 2010) and in their everyday work (Reinhardt et al., 2009). A survey on Twitter use for scientific purposes (Letierce et al., 2010) showed that Twitter is not only a communication medium but also reliable source of data for scientific analysis, profiling tasks and trends detection (Tao et al., 2011; Mathioudakis & Koudas, 2010; Softic, Ebner et al., 2010). Twitter hashtags have an influence on the structuring of communication within Twitter as well as for community building (Laniado & Mika, 2010; Bakshy et al., 2011).

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