• Relation-Centric Semantic Anno...

Result: Relation-Centric Semantic Annotation using Semantic Role Labeling and Coreference Resolution

Title:
Relation-Centric Semantic Annotation using Semantic Role Labeling and Coreference Resolution
Authors:
LIN, Chia-Hung, HUNG, Sheng-Hao, LIAO, Chien-Hsiang
Source:
SWWS 2011 : proceedings of the 2011 international conference on semantic web & web services (Las Vegas NV, July 18-21, 2011). :85-90
Publisher Information:
[S.l.]: CSREA Press, 2011.
Publication Year:
2011
Physical Description:
print, 34 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, Systèmes informatiques et systèmes répartis. Interface utilisateur, Computer systems and distributed systems. User interface, Organisation des mémoires. Traitement des données, Memory organisation. Data processing, Systèmes d'information. Bases de données, Information systems. Data bases, Intelligence artificielle, Artificial intelligence, Reconnaissance et synthèse de la parole et du son. Linguistique, Speech and sound recognition and synthesis. Linguistics, Sciences biologiques et medicales, Biological and medical sciences, Sciences biologiques fondamentales et appliquees. Psychologie, Fundamental and applied biological sciences. Psychology, Psychologie. Psychophysiologie, Psychology. Psychophysiology, Psychologie sociale, Social psychology, Attribution, perception et cognition sociale, Social attribution, perception and cognition, Psychologie. Psychanalyse. Psychiatrie, Psychology. Psychoanalysis. Psychiatry, Attributioné perception et cognition sociale, Analyse conceptuelle, Conceptual analysis, Análisis conceptual, Analyse sémantique, Semantic analysis, Análisis semántico, Annotation, Anotación, Archive, Archivo, Cognition sociale, Social cognition, Cognición social, Découverte connaissance, Knowledge discovery, Descubrimiento conocimiento, Ontologie, Ontology, Ontología, Relation sémantique, Semantic relation, Relación semántica, Similitude, Similarity, Similitud, Système réactif, Reactive system, Sistema reactivo, Sémantique, Semantics, Semántica, Web sémantique, Semantic web, Web semántica, Gestion relation client, Customer relationship management, Gestión de la relación con los clientes
Document Type:
Conference Conference Paper
File Description:
text
Language:
English
Author Affiliations:
Department of Management Science, R.O.C. Military Academy, Tawain, Province of China
Computer and Network Center, National Chi Nan University, Tawain, Province of China
Department of Information Management, National Central University, Tawain, Province of China
Access URL:
http://pascal-francis.inist.fr/vibad/index.php?action=search&terms=26080242
Rights:
Copyright 2015 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

Psychology. Ethology

FRANCIS
Accession Number:
edscal.26080242
Database:
PASCAL Archive

Further Information

Automatic semantic annotation based on domain-specific ontologies is a one of the critical issues for the success of the semantic web. Most existing approaches focused on the detection of concepts such as named entities, dates, monetary amounts. This study explores automatic semantic annotation techniques for applications using relation-centric ontologies which represent domain knowledge using a set of concepts with many inter-class relations. We propose a framework to detect event-based concepts and inter-concept relations using semantic role labeling and coreference resolution techniques. We gave an illustration of the processes by a semantic annotation application using CIDOC-CRM as the underlying ontology. Experiments using archives with a large number of image descriptions were conducted. The primitive results show that the accuracy is about 80% or so.