Semantic Web Based Integration of Knowledge Resources for Expertise Finding

This article discusses the challenges of expertise data integration and expert finding in modern organizations using an illustrative case study of a concrete research-intensive establishment, the Mihajlo Pupin Institute (MPI). It presents how the latest semantic technologies (Ontologies, Web services, Semantic Wiki) could be used on the top of the commercial ERP (Enterprise Resource Planning) software (SAP®) and the open-source ECM (Enterprise Content Management) software (Alfresco) to ensure meaningful search and retrieval of expertise for in-house users, as well as the integration into the Semantic Web community space. This article points out the necessary adjustments in enterprise knowledge management infrastructure in the light of uprising initiatives for standardization of the Semantic Web data.

Ontologies in Expertise Finding Systems

Knowledge Management Systems that enhance and facilitate the process of finding the right expert in an organization have gained much attention in recent years. This chapter explores the potential benefits and challenges of using ontologies for improving existing systems. A modeling technique from requirements engineering is used to evaluate the proposed system and analyze the impact it would have on the goals of the stakeholders. Based on the analysis, an ontology-based expertise finding system is proposed. This chapter also discusses the organizational settings required for the successful deployment of the system in practice.

Expertise Learning and Identification with Information Retrieval

In today’s knowledge-based economy, having proper expertise is crucial in resolving many tasks. Expertise Finding (EF) is the area of research concerned with matching available experts to given tasks. A standard approach is to input a task description/proposal/paper into an EF system and receive recommended experts as output. Mostly, EF systems operate either via a content-based approach, which uses the text of the input as well as the text of the available experts’ profiles to determine a match, and structure-based approaches, which use the inherent relationship between experts, affiliations, papers, etc. The underlying data representation is fundamentally different, which makes the methods mutually incompatible. However, previous work (Watanabe et al., 2005a) achieved good results by converting content-based data to a structure-representation and using a structure-based approach. The authors posit that the reverse may also hold merit, namely, a content-based approach leveraging structure-based data converted to a content-based representation. This paper compares the authors’ idea to a content only-based approach, demonstrating that their method yields substantially better performance, and thereby substantiating their claim.

ELIxIR

In today’s knowledge-based economy, having proper expertise is crucial in resolving many tasks. Expertise Finding (EF) is the area of research concerned with matching available experts to given tasks. A standard approach is to input a task description/proposal/paper into an EF system and receive recommended experts as output. Mostly, EF systems operate either via a content-based approach, which uses the text of the input as well as the text of the available experts’ profiles to determine a match, and structure-based approaches, which use the inherent relationship between experts, affiliations, papers, etc. The underlying data representation is fundamentally different, which makes the methods mutually incompatible. However, previous work (Watanabe et al., 2005a) achieved good results by converting content-based data to a structure-representation and using a structure-based approach. The authors posit that the reverse may also hold merit, namely, a content-based approach leveraging structure-based data converted to a content-based representation. This paper compares the authors’ idea to a content only-based approach, demonstrating that their method yields substantially better performance, and thereby substantiating their claim.

Semantic Web Based Integration of Knowledge Resources for Expertise Finding

This article discusses the challenges of expertise data integration and expert finding in modern organizations using an illustrative case study of a concrete research-intensive establishment, the Mihajlo Pupin Institute (MPI). It presents how the latest semantic technologies (Ontologies, Web services, Semantic Wiki) could be used on the top of the commercial ERP (Enterprise Resource Planning) software (SAP®) and the open-source ECM (Enterprise Content Management) software (Alfresco) to ensure meaningful search and retrieval of expertise for in-house users, as well as the integration into the Semantic Web community space. This article points out the necessary adjustments in enterprise knowledge management infrastructure in the light of uprising initiatives for standardization of the Semantic Web data.