Background knowledge for ontology construction

Fortuna, Blaž; Grobelnik, Marko; Mladenić, Dunja

doi:10.1145/1135777.1135959

Cited by 15 publications

(7 citation statements)

References 7 publications

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“…Instances of selected concept can be visualized using text-document visualization techniques described in [6]. The instances are presented as points on a map in such a way that each instance located close to similar instance and far from less-similar instances.…”

Section: Concept Visualizationmentioning

confidence: 99%

OntoGen: Semi-automatic Ontology Editor

Fortuna

Grobelnik

Mladenić

2007

Lecture Notes in Computer Science

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Abstract. In this paper we present a semi-automatic ontology editor as implemented in a new version of OntoGen system. The system integrates machine learning and text mining algorithms into an efficient user interface lowering the entry barrier for users who are not professional ontology engineers. The main features of the systems include unsupervised and supervised methods for concept suggestion and concept naming, as well as ontology and concept visualization. The system was tested in extensive user trails and in several realworld scenarios with very positive results.

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Section: Concept Visualizationmentioning

confidence: 99%

OntoGen: Semi-automatic Ontology Editor

Fortuna

Grobelnik

Mladenić

2007

Lecture Notes in Computer Science

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“…In the context of knowledge sharing, the term ontology [5] means a specification of a conceptualization. That is, ontology is a description (like a formal specification of a program) of the concepts and relationships.…”

Section: Ontology Modelmentioning

confidence: 99%

“…Just similar to VSM in text classification [4,5,6,7],we define the query interface schema S i of deep Web as a vector with a group of features (t i1 ,t i2 ,…,t im ) and related weights (w il ，w i2 ，…，w im ). So a VSM with 'n' deep Web sources can be showed as the below： …”

Section: Deep Web Vector Space Modelmentioning

confidence: 99%

“…To date, in terms of the Classification, existing works mainly focus on classifying texts or Web documents [4,5,6,7,8], while there is little in the deep Web. Bin [9] proposes a clustering approach to organize the structured web sources, which is based on hypothetical hidden models for homogeneous sources that maybe not exist in the real worlds.…”

Section: Introductionmentioning

confidence: 99%

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A Method of Deep Web Classification

Hao

Wang

et al. 2007

2007 International Conference on Machine Learning and Cybernetics

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The research on deep Web classification is an important area in large-scale deep Web integration, which is still at its early stage. Many deep Web sources are structured by providing structured query interfaces and results. Classifying such structured sources into domains is one of the critical steps toward the integration of heterogeneous Web sources. In this paper, we present an Ontology-based deep Web classification, which includes a category ontology model and a deep Web Vector Space Model (VSM). The experimental results show that we can get a good performance with average precision 91.6% and average recall 92.4%.

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“…These approaches cannot obtain the satisfactory results due to the existences such as homonym, synonymy and other semantic heterogeneity phenomenon. With the advent and development of ontology, ontology plays an important role in knowledge representation and semantic matching [11][12][13][14]. The construction and application of domain ontology will provide an effectively alternative method for identifying and classifying Deep Web query interfaces.…”

Section: Introductionmentioning

confidence: 99%

Identification and Classification of Deep Web Query Interfaces via Ontology

Qiang¹,

Cai²,

Wen³

et al. 2011

IJACT

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In order to obtain the large quantities of valuable information on Deep Web, it is required to discover the related individual query interface and design the integrated query interface on which user query request can be submitted. The key challenges are to identify and classify the Deep Web query interface accurately. In view of the regular data of Deep Web, we consider to construct the Deep Web domain ontology to help identify and classify the Deep Web query interfaces. Correspondingly, one domain ontology construction approach by referring to the hierarchal schema of query interface is proposed. Based on the constructed domain ontology, the modified interface expression algorithm and the identification and classification algorithm for Deep Web query interfaces are also presented, respectively. Experimental results show the effectiveness of our proposed algorithms. .

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Background knowledge for ontology construction

Cited by 15 publications

References 7 publications

OntoGen: Semi-automatic Ontology Editor

OntoGen: Semi-automatic Ontology Editor

A Method of Deep Web Classification

Identification and Classification of Deep Web Query Interfaces via Ontology

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