Transforming Wikipedia into a Search Engine for Local Experts

2012

Abstract: We propose a dynamic map of knowledge generated from Wikipedia pages and the Web URLs contained therein. GalaxySearch provides answers to the questions we don't know how to ask, by constructing a semantic network of the most relevant pages in Wikipedia related to a search term.

2018

Semantic Web (SW) is an emerging research field that has application in different domains such as e-government services, richly interlinked library systems, Web search engines, enterprise knowledge stores, and other. The term “Semantic Web” refers to the World Wide Web Consortium’s (W3C) vision of the Web of linked data (called also the Web of Data) as “...an extension of the current Web in which information is given a well-defined meaning, better enabling computers and people to work in cooperation” (Berners-Lee, Hendler, & Lassila, 2001). Since then, many specifications, guidelines, languages, and tools have been developed that facilitate software development, improve performance and create new business opportunities.

The Semantic Web: Research and Applications, 2010

There are many opportunities to improve the interactivity of information retrieval systems beyond the ubiquitous search box. One idea is to use knowledge bases—e.g. controlled vocabularies, classification schemes, thesauri and ontologies—to organize, describe and navigate the information space. These resources are popular in libraries and specialist collections, but have proven too expensive and narrow to be applied to everyday webscale search. Wikipedia has the potential to bring structured knowledge into more widespread use. This online, collaboratively generated encyclopaedia is one of the largest and most consulted reference works in existence. It is broader, deeper and more agile than the knowledge bases put forward to assist retrieval in the past. Rendering this resource machine-readable is a challenging task that has captured the interest of many researchers. Many see it as a key step required to break the knowledge acquisition bottleneck that crippled previous efforts. This ...

Expert Systems with Applications, 2015

The profiling of background knowledge is essential in scholar's recommender systems. Existing ontologybased profiling approaches employ a pre-built reference ontology as a backbone structure for representing the scholar's preferences. However, such singular reference ontologies lack sufficient ontological concepts and are unable to represent the hierarchical structure of scholars' knowledge. They rather encompass general-purpose topics of the domain and are inaccurate in representing the scholars' knowledge. This paper proposes a method for integrating of multiple domain taxonomies to build a reference ontology, and exploits this reference ontology for profiling scholars' background knowledge. In our approach, various topics of Computer Science domain from Web taxonomies are selected, transformed by DBpedia, and merged to construct a reference ontology. We demonstrate the effectiveness of our approach by measuring five quality-based metrics as well as application-based evaluation against the developed reference ontology. The empirical results show an improvement over the existing reference ontologies in terms of completeness, richness, and coverage. (B. Amini), roliana@utm.my (R. Ibrahim), shahizan@utm.my (M.S. Othman), nematbakhsh@eng.ui.ac.ir (M.A. Nematbakhsh). Expert Systems with Applications 42 (2015) 913-928

International Journal of Enterprise Information Systems, 2009

1983

The Knoesphere project is an attempt to build an expert system that is encyclopedic, in the breadth of coverage of its knowledge base, and in the degree of integration of that knowledge. The primary issue is how to aid users in searching complex bodies of knowledge. Our approach is to frame the system more as a museum than a set of tomes, and to have the user take more or less guided tours of the exhibits therein. The impact of such a system on everyday life ~ entertainment and, eventually, education --is clear. We discuss its potential for progress in Al as well: a testbed for representation, speech understanding, natural language understanding and generation, automatic story generation and animation, learning, user modelling, and planning.

Abstract. Finding relevant experts is essential for collaboration. Many Expert Finder applications succeed in finding experts, only at coarse levels of granularity, and within specific domains. Finding experts at fine granularity however, is often needed for effective functioning. Semantics can refine the granularity at which the expertise of various experts can be determined, by explicitly expressing relationships between topics and subtopics in a taxonomy.

2007

Finding relevant experts in research is often critical and essential for collaboration. Semantics can refine the level of granularity at which the expertise of various experts can be determined, by explicitly expressing relationships between topics and various subtopics using a taxonomy. Such topic-subtopics relationships allow extrapolation of expertise, based on the notion that expertise in subtopics is also indicative of expertise in a topic itself.

Proceedings of the 9th International Conference on Agents and Artificial Intelligence, 2017

In this work, we propose enhanced data-driven techniques that optimize expert representation and identify subject experts via automated analysis of the available online information. We use a weighting method to assess the levels of expertise of an expert to the domain-specific topics. An expert profile is presented by a description of the topics in which the person is an expert plus the relative levels (weights) of knowledge or experience he/she has in the different topics. In this context, we define a way to estimate the expertise similarity between experts. Then the experts finding task is viewed as a list completion task and techniques that return similar experts to ones provided by the user are considered. The proposed techniques are tested and evaluated on data extracted from PubMed repository.