Multimedia data and information management is an important task according to the development of media processing technology. Multimedia is a useful resource that people understand complex situations such as the elderly care domain. Appropriate annotation is beneficial in several tasks of information management, such as storing, retrieval, and summarization of data, from a semantic perspective. However, the metadata annotation for multimedia data remains problematic because metadata is obtained as a result of interpretation depending on domain-specific knowledge, and it needs well-controlled and comprehensive vocabulary for annotation. In this study, we proposed a collaborative methodology for developing ontologies and annotation with domain experts. The method includes (1) classification of knowledge types for collaborative construction of annotation data, (2) division of tasks among a team composed of domain experts, ontology engineers, and annotators, and (3) incremental approach to ontology development. We applied the proposed method to 11 videos on elderly care domain for the confirmation of its feasibility. We focused on annotation of actions occurring in these videos, thereby the annotated data is used as a support in evaluating staff skills. The application results show the content in the ontology during annotation increases monotonically. The number of “action concepts” is saturated and reused among the case studies. This demonstrates that the ontology is reusable and could represent various case studies by using a small number of “action concepts”. This study concludes by presenting lessons learnt from the case studies.

This paper presents a method for disambiguating word senses in Korean-Japanese machine translation by using a language independent ontology. This ontology stores semantic constraints between concepts and other world knowledge, and enables a natural language processing system to resolve semantic ambiguities by making inferences with the concept network of the ontology. In order to acquire a language-independent and reasonably practical ontology in a limited time and with less manpower, we extend the existing Kadokawa thesaurus by inserting additional semantic relations into its hierarchy, which are classified as case relations and other semantic relations. The former can be obtained by converting valency information and case frames from previously-built electronic dictionaries used in machine translation. The latter can be acquired from concept co-occurrence information, which is extracted automatically from a corpus. In practical machine translation systems, our word sense disambiguation method achieved an improvement of average precision by 6.0% for Japanese analysis and by 9.2% for Korean analysis over the method without using an ontology.