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As existing computer search engines struggle to understand the meaning of natural language, semantically enriched metadata may improve interest-based search engine capabilities and user satisfaction. This paper presents an enhanced version of the ecosystem focusing on semantic topic metadata detection and enrichments. It is based on a previous paper, a semantic metadata enrichment software ecosystem (SMESE). Through text analysis approaches for topic detection and metadata enrichments this paper propose an algorithm to enhance search engines capabilities and consequently help users finding content according to their interests. It presents the design, implementation and evaluation of SATD (Scalable Annotation-based Topic Detection) model and algorithm using metadata from the web, linked open data, concordance rules, and bibliographic record authorities. It includes a prototype of a semantic engine using keyword extraction, classification and concept extraction that allows generating semantic topics by text, and multimedia document analysis using the proposed SATD model and algorithm. The performance of the proposed ecosystem is evaluated using a number of prototype simulations by comparing them to existing enriched metadata techniques (e.g., AlchemyAPI, DBpedia, Wikimeta, Bitext, AIDA, TextRazor). It was noted that SATD algorithm supports more attributes than other algorithms. The results show that the enhanced platform and its algorithm enable greater understanding of documents related to user interests. KEYWORDS Natural Language Processing, Semantic Topic Detection, Semantic Metadata Enrichment, Text and Data Mining |
