OntoPop: An Ontology Population System for the Semantic Web

Abstract

SUMMARYThe development of ontology at the instance level requires the extraction of the terms defining the instances from various data sources. These instances then are linked to the concepts of the ontology, and relationships are created between these instances for the next step. However, before establishing links among data, ontology engineers must classify terms or instances from a web document into an ontology concept. The tool for help ontology engineer in this task is called ontology population. The present research is not suitable for ontology development applications, such as long time processing or analyzing large or noisy data sets. OntoPop system introduces a methodology to solve these problems, which comprises two parts. First, we select meaningful features from syntactic relations, which can produce more significant features than any other method. Second, we differentiate feature meaning and reduce noise based on latent semantic analysis. Experimental evaluation demonstrates that the OntoPop works well, significantly out-performing the accuracy of 49.64%, a learning accuracy of 76.93%, and executes time of 5.46 second/instance. key words: ontology population, syntactic feature extraction, latent semantic analysis, semantic web

An ontology knowledge inspection methodology for quality assessment and continuous improvement

“…OntoPop platform [19] implements a methodology. It aims to guide users in the integration of information extraction (IE) and knowledge representation (KR) tools in order to design domain-oriented applications for knowledge management.…”

Représentation et vérification d’un environnement intelligent à partir de spécifications utilisateur en langage naturel

“…Le recours aux modèles formels tels que les ontologies comme représentation intermédiaire n'a pas encore été exploré. Pourtant, les travaux sur le passage automatique de textes en LN vers des ontologies ont montré des résultats prometteurs (Petasis et al, 2011 ;Ruiz-Martìnez et al, 2011 ;Thongkrau, Lalitrojwong, 2012) et divers travaux ont déjà proposé des méthodes de traduction automatique entre une ontologie et un langage formel tels que Alloy (Song et al, 2012) ou Maude (Verdejo et al, 2005 ;Senanayake et al, 2005 ;Huang et al, 2009). En outre, de nombreux travaux insistent sur la pertinence de l'usage d'ontologies en ingénierie des exigences (Castañeda et al, 2010), pour codifier les connaissances pertinentes et faciliter la compréhension du domaine (Omoronyia et al, 2010 ;Al Balushi et al, 2013), minimiser l'ambiguïté linguistique (Castañeda et al, 2012), identifier des règles métier redondantes, jamais applicables ou qui violent le domaine de leur définition (Chniti et al, 2012), ou s'appuyer sur les connaissances d'ontologies pour aider l'analyse dans ses décisions pour améliorer des spécifications (Körner, Brumm, 2009).…”

From Natural Language Requirements to Formal Specification Using an Ontology

“…Some methods restrain the definition of the context on the presence of a verb and the recognition of its neighbourhood [Lin and Pantel, 2001], [Makki et al, 2008]. Other methods rely on the classification of pairs of entities known to be linked by a semantic relationship [Hasegawa et al, 2004], [Nakamura-Delloye, 2011], [Thongkrau and Lalitrojwong, 2012]. The majority of these approaches exploit lexical and syntactic knowledge.…”

“…Populating an ontology consists in adding new instances, associated to concepts and properties recognized in texts, without changing its conceptual structure. Their identification may focus on the recognition of concept instances [Thongkrau and Lalitrojwong, 2012], or on the recognition of relation instances [Nakamura-Delloye, 2011]. In both cases the recognition is limited to the surface of the text, whereas the recognition of identical instances and implicit knowledge requires to infer its meaning at a semantic level.…”