Semantic TagPrint - Tagging and Indexing Content for Semantic Search and Content Management

Abstract

Existing search and content management technology is facing a challenge of locating desired content with the exponentially growing volume of documents. An approach for mitigating this issue is to make use of user-generated tags. However, the improvements are limited because tags are (1) free from context and form, (2) user generated, (3) used for purposes other than description, and (4) often ambiguous. Since tagging is a voluntary action, some documents are not tagged at all. Furthermore, the interpretation of the tags associated with tagged documents also remains a challenge. To overcome these challenges, semantic web resources and technologies can be utilized to automatically generate semantic tags. Semantic tags not only reflect document content more accurately, they also enable better search results. Ontology coverage, ontology mapping and weighting significant ontological entities within a context are key challenges in semantic tagging systems. To address these challenges, this paper presents a semantic tagging system - Semantic TagPrint - to map a text document to semantic tags defined as entities in an ontology. Semantic TagPrint uses a linear time lexical chaining Word Sense Disambiguation (WSD) algorithm for real time concept mapping. In addition, it utilizes statistical metrics and ontological features of the ontology for weighting and recommending the semantic tags. A comparative evaluation shows that our mapping algorithm is fairly accurate and our tag recommendation algorithm performs better than other systems and algorithms.