With the explosive growth of user-generated multimedia resources in the big data era (e.g., video, audio, text or even their combinations), bridging the semantic gap between low-level features and high-level semantics in multiple modes of data is a critical and indispensable issue. In this paper, we exploit a popular type of metadata called collaborative tags to address this issue. Specifically, we identify basic level concepts and then construct ontologies from the collaborative tags. The generated ontologies can be employed to organize and index user-generated multimedia data. Existing research lacks a principle to supervise the ontology extraction from a human perspective. In contrast, we borrow the idea of a family of concepts called basic level concepts from cognitive psychology. These basic level concepts are frequently used by people in their daily life and when organizing human knowledge. In this paper, we extract ontologies with basic level concepts from collaborative tags. Furthermore, we model the effect of context and present a method for context-aware basic level concepts detection for ontology learning. To the best of our knowledge, this is the first work on context-aware basic level concept detection in collaborative tagging for the construction of ontologies. To evaluate the proposed method, experiments were conducted on real datasets using the Open Directory Project (ODP) as a benchmark. The experimental results illustrate that ontologies extracted by our approach are more rational and human-oriented.
Bibliographical noteThis paper is an extended article based on the preliminary conference proceeding published.
- Basic level concept
- Collaborative tags
- Ontology generation
- Semantic gap