Selection of Ontologies for the Semantic Web

Lozano-Tello, Adolfo; Gómez-Pérez, Asuncíon; Sosa, Encarna

doi:10.1007/3-540-45068-8_77

Cited by 10 publications

(3 citation statements)

References 8 publications

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“…In the case of document retrieval, traditional measures of information retrieval such as F-score can be used ( 96 , 97 ). Lozano-Tello et al ( 98 ) proposed a technique that enables the users to determine the suitability and appropriateness of existing ontologies with the requirements of their respective systems. Porzel and Malaka et al ( 99 ) evaluated the exploitation of ontological relations in speech recognition.…”

Section: Evaluation Of Ontology Learning Techniquesmentioning

confidence: 99%

A survey of ontology learning techniques and applications

et al. 2018

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Ontologies have gained a lot of popularity and recognition in the semantic web because of their extensive use in Internet-based applications. Ontologies are often considered a fine source of semantics and interoperability in all artificially smart systems. Exponential increase in unstructured data on the web has made automated acquisition of ontology from unstructured text a most prominent research area. Several methodologies exploiting numerous techniques of various fields (machine learning, text mining, knowledge representation and reasoning, information retrieval and natural language processing) are being proposed to bring some level of automation in the process of ontology acquisition from unstructured text. This paper describes the process of ontology learning and further classification of ontology learning techniques into three classes (linguistics, statistical and logical) and discusses many algorithms under each category. This paper also explores ontology evaluation techniques by highlighting their pros and cons. Moreover, it describes the scope and use of ontology learning in several industries. Finally, the paper discusses challenges of ontology learning along with their corresponding future directions.

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Section: Evaluation Of Ontology Learning Techniquesmentioning

confidence: 99%

A survey of ontology learning techniques and applications

et al. 2018

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“…Lozano-Tello et al presented the ONTOMETRIC method (Lozano-Tello and Gomez-Perez, 2004;Lozano-Tello et al, 2003), which compares ontologies with a taxonomy of 160 characteristics organized in a multilevel tree-shaped framework. The final score for an ontology is calculated as the weighted sum of the scores given to each of the leaf node characteristics, through aggregations at each of the internal nodes governing aspects and sub-aspects of the ontology characteristics.…”

Section: Related Workmentioning

confidence: 99%

Ontology Usability Scale: Context-aware Metrics for the Effectiveness, Efficiency and Satisfaction of Ontology Uses

West

et al. 2018

Data Science Journal

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Both ontology builders and users need a way to evaluate ontologies in terms of usability, but existing ontology evaluation approaches do not fit this purpose. We propose the Ontology Usability Scale (OUS), a ten-item Likert scale derived from statements prepared according to a semiotic framework and an online poll in the Semantic Web community to provide a practical way of ontology usability evaluation. Case studies were conducted to bookkeep current usability evaluation results for ontologies expecting revisions in the future, and discussions of the poll results are presented to help proper use and customization of the OUS.

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“…Some of the methods known are criteria-based evaluation. It is one of the methods that used to scope criteria that needed to achieve for example, suitability [10], [11] and consistency [12]. References [13] and [14] works focused on managing criteria grouping.…”

Section: Introductionmentioning

confidence: 99%

Similarity algorithm for evaluating the coverage of domain ontology for semantic Web services

Mohamad

Mohd-Hamka

2014

2014 8th. Malaysian Software Engineering Conference (MySEC)

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Ontology evaluation consists of verification and validation of ontology to achieve selected evaluation objectives. Domain ontology developments were time consuming and did need proper way of evaluation so that the ontology does support the knowledge it represent throughout certain domain. Ontology reuse becomes the main objectives as to increase the resource reusability of the already built ontology. Domain ontology evaluation currently validated via gold standard or expert validation and it might lead to biased validation. User has its own perception towards the criteria of the ontology they want, there are not exact standard of ontology quality since variety of requirements to select suitable ontology for their usage. Travel domain will be our case study domain and we will pick on any ontology consider as 'travel ontology' from the searched from ontology inventories in Web. We proposed algorithm measurement to validate the coverage of ontology gathered and display the result to users. This is to ensure user select the suitable ontology based on coverage result.

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Selection of Ontologies for the Semantic Web

Cited by 10 publications

References 8 publications

A survey of ontology learning techniques and applications

A survey of ontology learning techniques and applications

Ontology Usability Scale: Context-aware Metrics for the Effectiveness, Efficiency and Satisfaction of Ontology Uses

Similarity algorithm for evaluating the coverage of domain ontology for semantic Web services

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