关键词: 统计主题模型, 本体, 语义标注, 概念, 信息检索

Abstract: Concerning the Web document annotation techniques available have weakness in integrity annotation, Latent Dirichlet Allocation (LDA) model was applied to semantic annotation. By embedding document domain information to LDA model, a new LDA model called domain-enabled LDA was introduced. An association between the statistical topical model and domain ontology was established, so the implied topic generated could be interpreted by concepts and an explicit semantic in document was acquired. Because the LDA model assigned a topic to each word in document, a multi-granularity annotation strategy was proposed. The experiments on 20news-group and WebKB show that the domain-enabled LDA model proposed can improve the annotation effectiveness and the multi-granularity annotation method helps different types of query in information retrieval.

Key words: statistical topical model, ontology, semantic annotation, concept, information retrieval