Result: Discovering mappings in hierarchical data from multiple sources using the inherent structure : CoMMA: a framework for integrated multimedia mining using multi-relational associations

Title:
Discovering mappings in hierarchical data from multiple sources using the inherent structure : CoMMA: a framework for integrated multimedia mining using multi-relational associations
Authors:
SELCUK CANDAN, K, JONG WOOK KIM, HUAN LIU, SUVARNA, Reshma
Source:
Knowledge and information systems. 10(2):185-210
Publisher Information:
Godalming: Springer, 2006.
Publication Year:
2006
Physical Description:
print, 59 ref
Original Material:
INIST-CNRS
Subject Terms:
Computer science, Informatique, Sciences exactes et technologie, Exact sciences and technology, Sciences appliquees, Applied sciences, Informatique; automatique theorique; systemes, Computer science; control theory; systems, Logiciel, Software, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Telecommunications et theorie de l'information, Telecommunications and information theory, Télécommunications, Telecommunications, Radiodiffusion. Vidéocommunications. Audiovisuel, Broadcasting. Videocommunications. Audiovisual, Base donnée multimédia, Multimedia databases, Découverte connaissance, Knowledge discovery, Descubrimiento conocimiento, Evaluation performance, Performance evaluation, Evaluación prestación, Extraction information, Information extraction, Extracción información, Fouille donnée, Data mining, Busca dato, Langage XML, XML language, Lenguaje XML, Multimédia, Multimedia, Métadonnée, Metadata, Metadatos, Ontologie, Ontology, Ontología, Performance algorithme, Algorithm performance, Resultado algoritmo, Sémantique, Semantics, Semántica, Type donnée, Data type, Tipo dato, Extracting and mining semantics from multimedia databases, Hierarchical multimedia, Knowledge discovery in XML, Multimodal integration, Ontologies, Retrieval and mining of semantics, Structure-based mining
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Computer Science and Engineering, Arizona State University, Tempe AZ 82857, United States
ISSN:
0219-1377
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=18088279
Rights:
Copyright 2006 INIST-CNRS
CC BY 4.0
Sauf mention contraire ci-dessus, le contenu de cette notice bibliographique peut être utilisé dans le cadre d’une licence CC BY 4.0 Inist-CNRS / Unless otherwise stated above, the content of this bibliographic record may be used under a CC BY 4.0 licence by Inist-CNRS / A menos que se haya señalado antes, el contenido de este registro bibliográfico puede ser utilizado al amparo de una licencia CC BY 4.0 Inist-CNRS
Notes:
Computer science; theoretical automation; systems

Telecommunications and information theory
Accession Number:
edscal.18088279
Database:
PASCAL Archive

Further Information

Unprecedented amounts of media data are publicly accessible. However, it is increasingly difficult to integrate relevant media from multiple and diverse sources for effective applications. The functioning of a multimodal integration system requires metadata, such as ontologies, that describe media resources and media components. Such metadata are generally application-dependent and this can cause difficulties when media needs to be shared across application domains. There is a need for a mechanism that can relate the common and uncommon terms and media components. In this paper, we develop an algorithm to mine and automatically discover mappings in hierarchical media data, metadata, and ontologies, using the structural information inherent in these types of data. We evaluate the performance of this algorithm for various parameters using both synthetic and real-world data collections and show that the structure-based mining of relationships provides high degrees of precision.