ONTOLOGY MERGING USING PROTÉGÉ – A CASE STUDY

The exponential growth of data and the boom of online businesses necessitates the need for data to be machine-readable, as humans are no longer able to manually manage the vast amounts of data. Ontologies can define concepts and relations that are amenable to processing by machines. Ontologies are created in silos and pockets of domains, and the need to merge these resources is key to universal access to multi-domain knowledge. Merging of ontologies has been explored to an extent over the last two decades, and this paper explores the extent of the tools and techniques available with a case study of merging two ontologies which are publicly available, the Person ontology and Institutional ontology, using the latest tools available on the most popular ontology editor, Protégé. It is found that automated merging tools have not been improved much over the last two decades, and the most current merging tools provided combine the two ontologies into one but do not unite or merge any of the classes or axioms which are equivalent. This can be seen in the axiom count, which does not decrease in the merged ontology, showing that no similar classes or actual axioms were merged. Protégé plugins which used to provide the semi-automatic mapping of similar classes to assist the merging process were found to be no longer available, and manual mapping by the knowledge engineer was required. This supports further research in automated ontology merging techniques.

A Meta-Mining Ontology Framework for Data Processing

Extracting knowledge from data streams received from observed objects through data mining is required in various domains. However, there is a lack of any kind of guidance on which techniques can or should be used in which contexts. Meta mining technology can help build processes of data processing based on knowledge models taking into account the specific features of the objects. This paper proposes a meta mining ontology framework that allows selecting algorithms for solving specific data mining tasks and build suitable processes. The proposed ontology is constructed using existing ontologies and is extended with an ontology of data characteristics and task requirements. Different from the existing ontologies, the proposed ontology describes the overall data mining process, used to build data processing processes in various domains, and has low computational complexity compared to others. The authors developed an ontology merging method and a sub-ontology extraction method, which are implemented based on OWL API via extracting and integrating the relevant axioms.

Educating the Information Integration Using Contextual Knowledge and Ontology Merging in Advanced Levels

This paper defines the methods of educating the information integration by the use of ontologies. For this there are two various architecture are central and peer-to-peer data integration. A ciis generally has a worldwide mapping, which gives the client a uniform interface to get to data put away in the information sources. Conversely, in piis, there are no worldwide purposes of control on the information sources. Such systems enable developers to develop an integrated hybrid contextual based system and new concepts to be introduced. This enables the retrival of the information is easier and faster. The two most significant methodologies for structure an information integration framework is global as view & local as view (lav). In the gav method, each substance in the worldwide pattern is related nearby outline. In this paper we use various ontology languages like xml, rdf, daml+oil, owl etc.

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

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.

Using ontology merging for the integration of information systems and the production capacity planning system

This article describes the method of integrating information systems of an aircraft factory with the production capacity planning system based on the ontology merging. The ontological representation is formed for each relational database (RDB) of integrated information systems. The ontological representation is formed in the process of analyzing the structure of the relational database of the information system (IS). Based on the ontological representations merging the integrating data model is formed. The integrating data model is a mechanism for semantic integration of data sources.