Data & Knowledge Engineering - Special issue on linguistic instruments in knowledge engineering (LIKE)
Word sense disambiguation for free-text indexing using a massive semantic network
CIKM '93 Proceedings of the second international conference on Information and knowledge management
Knowledge representation: logical, philosophical and computational foundations
Knowledge representation: logical, philosophical and computational foundations
The Knowledge Acquisition and Representation Language, KARL
IEEE Transactions on Knowledge and Data Engineering
OntoSeek: Content-Based Access to the Web
IEEE Intelligent Systems
WWCA '97 Proceedings of the International Conference on Worldwide Computing and Its Applications
ImageRoadMap: A New Content-based Image Retrieval System
DEXA '97 Proceedings of the 8th International Conference on Database and Expert Systems Applications
Using information content to evaluate semantic similarity in a taxonomy
IJCAI'95 Proceedings of the 14th international joint conference on Artificial intelligence - Volume 1
A Model-Based Approach to Semantic-Based Retrieval of Visual Information
SOFSEM '02 Proceedings of the 29th Conference on Current Trends in Theory and Practice of Informatics: Theory and Practice of Informatics
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Most users want to find visual information based on the semantics of visual contents such as a name of person, semantic relations, an action happening in a scene, ...etc. However, techniques for content-based image or video retrieval are not mature enough to recognize visual semantic completely, whereas retrieval based on color, size, texture and shape are within the state of the art. Therefore, smart ways to manage textual annotation is visual information retrieval are necessary. In this paper, a framework for integration of textual and visual content searching mechanism is presented. The proposed framewrok includes ontology-based semantic query processing through efficient semantic similarity measurement. A new conceptual similarity distance measure between two conceptual entities in a large taxonomy structure is proposed and its efficiency is demonstrated. With the proposed method, an information retrieval system can benefit such as (1) reduction of the number of trial-and-errors to find correct keywords, (2) Improvement of precision rates by eliminating the semantic heterogeneity in description, and (3) Improvement of recall rates through precise modeling of concepts and their relations.