Semantic association networks: Using semantic web technology to improve scholarly knowledge and expertise management

Abstract

This chapter introduces Semantic Association Networks (SANs), a novel means of using SemanticWeb technology to tag and interlink scientific datasets, services (e.g., algorithms, techniques, or approaches), publications (e.g., papers, patents, grants), and expertise (i.e., author and user information) to improve scholarly knowledge and expertise management. Among other ends, the proposed SANs: • Facilitate new types of searches (e.g., the retrieval of all authors that worked with dataset x or all papers that used algorithm y). • Ease the reuse of datasets and services, thus increasing the reproducibility of results. • Enable dataset/algorithm/result comparisons at the data/code/implication level. • Exploit data access and data origin logs to indicate the usefulness of resources and the reputation of authors. Section 11.2 outlines the need for improved scholarly knowledge and expertise management by discussing major trends in the evolution of science and our current means to access scholarly knowledge and expertise. Section 11.3 introduces Semantic Association Networks, and their usage for improving storage, correlation, analysis, access, and management of scientific knowledge and expertise. Section 11.4 discusses opportunities and sociotechnical challenges for the implementation of SANs. Section 11.5 contains concluding remarks and presents a vision for the future of scholarly publishing knowledge and expertise management.