Merging of axiomatic definitions of concepts in the complex OWL ontologies

Fahad, Muhammad

doi:10.1007/s10462-016-9479-5

Cited by 8 publications

(2 citation statements)

References 24 publications

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“…The ontologies can be merged based on the similarity measure. Many similarities based ontology merging methods are existing, (Abbes and Gargouri, 2016;Chatterjee et al, 2017;Fahad, 2017;Fahad, 2018;Makwana and Ganatra, 2018). Table IV shows these algorithms descriptions along with its merits and demerits.…”

Section: Related Workmentioning

confidence: 99%

A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

Priya

Kumar

2019

LHT

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Purpose The purpose of this paper is to merge the ontologies that remove the redundancy and improve the storage efficiency. The count of ontologies developed in the past few eras is noticeably very high. With the availability of these ontologies, the needed information can be smoothly attained, but the presence of comparably varied ontologies nurtures the dispute of rework and merging of data. The assessment of the existing ontologies exposes the existence of the superfluous information; hence, ontology merging is the only solution. The existing ontology merging methods focus only on highly relevant classes and instances, whereas somewhat relevant classes and instances have been simply dropped. Those somewhat relevant classes and instances may also be useful or relevant to the given domain. In this paper, we propose a new method called hybrid semantic similarity measure (HSSM)-based ontology merging using formal concept analysis (FCA) and semantic similarity measure. Design/methodology/approach The HSSM categorizes the relevancy into three classes, namely highly relevant, moderate relevant and least relevant classes and instances. To achieve high efficiency in merging, HSSM performs both FCA part and the semantic similarity part. Findings The experimental results proved that the HSSM produced better results compared with existing algorithms in terms of similarity distance and time. An inconsistency check can also be done for the dissimilar classes and instances within an ontology. The output ontology will have set of highly relevant and moderate classes and instances as well as few least relevant classes and instances that will eventually lead to exhaustive ontology for the particular domain. Practical implications In this paper, a HSSM method is proposed and used to merge the academic social network ontologies; this is observed to be an extremely powerful methodology compared with other former studies. This HSSM approach can be applied for various domain ontologies and it may deliver a novel vision to the researchers. Originality/value The HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

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Section: Related Workmentioning

confidence: 99%

A novel method for merging academic social network ontologies using formal concept analysis and hybrid semantic similarity measure

Priya

Kumar

2019

LHT

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“…OWL provides three languages, each with a higher level of expressiveness than the previous one. In the context of thesauri and taxonomies, the appropriate language is OWL Lite [37], as it allows for the introduction of hierarchical classifications and simple restrictions such as those referring to cardinality.…”

Section: The Problem Of the Representation Of A Classification Systemmentioning

confidence: 99%

Development of a classification system for Mathematical Logic

Cabezuelo

2019

Journal of Information Science

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The number of digital resources that exist in repositories and on the Internet in general is enormous. Recovering resources that fit with the user’s specific needs poses a problem. To solve this problem, metainformation is added to the resources. One type of metainformation is the classification of a resource using a classification system that is widely recognised and agreed upon by its users. In this way, each resource is assigned a precise place within the classification system, thus facilitating its location. This article proposes a taxonomy for the classification of the resources (notes, exercises, exams or programmes that could be stored within a digital repository) that are generated within the scope of a Mathematical Logic course for a computer science degree programme. It also describes how to represent the proposed taxonomy using the IMS-VDEX standard and how to integrate it in the LOM and Dublin Core metadata specifications and proposes a set of controlled vocabularies that make it possible to refine the taxonomic metainformation.

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