SKMT: A Semantic Knowledge Management Tool for Content Tagging, Search and Management

Abstract

With an ever increasing amount of content, we heavily rely on search engine to locate documents. Manual content Tagging can improve search results by allowing search engines to exploit tags generated by content authors. Manual tagging method introduces issues of bias and inconsistency from ad-hoc tagging, and increase burden to the authors. In this paper, we first introduce a semantic tagging engine that automatically generates semantic tags for the given documents. This tagging engine provides the ground for realizing semantic search, based on meanings of search terms and content tags. Then we present a Semantic Knowledge Management Tool (SKMT) as a semantic search and knowledge management platform to search, analyze and manage enterprise content. SKMT can scan different content sources and generate indexes of semantic keywords. Its user-friendly interface allows users to manage various data sources, search, explore and visualize search results at semantic level. Higher precision of semantic search and semantic data visualization are also demonstrated with examples as benefits of SKMT.