The goal of Formal Concept Analysis (FCA) and Ontologies are modeling concepts with varying needs. An ontology is an explicit formal conceptualization of some domain of interest. Ontologies are widely used in various fields such as E-commerce, Semantic Web and knowledge management. FCA is a method of deriving a formal ontology or a concept hierarchy from a group of objects with their properties. FCA facilities an environment to make the data simpler by analyzing, structuring and visualizing. This paper presents a survey of Ontology construction and merging using FCA.

Priya M

Department of Digital Communications

School of Information Technology and Engineering

Vellore Campus

Aswani Kumar Cherukuri

Department of Information Technology

Fulltext

Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Indian Journal of Science and Technology

Granular Computing

An approach to merge domain ontologies using granular computing

PurposeThe 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/approachThe 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.FindingsThe 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 implicationsIn 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/valueThe HSSM is not applied for merging the ontologies in any former studies up to the knowledge of authors.

Library Hi Tech

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

Formal Concept Analysis (FCA), in which data is represented as a formal context, offers a framework for Association Rules Mining (ARM) by handling functional dependencies in the data. However, with the size of the formal context, the number of rules grows exponentially. In this article, we apply Fuzzy K-Means clustering on the data set to reduce the formal context and FCA on the reduced data set for mining association rules. With experiments on two real-world healthcare data sets, we offer the evidence for performance of FKM-based FCA in mining association rules. Copyright © 2012 Taylor &amp; Francis Group, LLC.

Applied Artificial Intelligence

FUZZY CLUSTERING-BASED FORMAL CONCEPT ANALYSIS FOR ASSOCIATION RULES MINING

Knowledge discovery in data using formal concept analysis and random projections In this paper our objective is to propose a random projections based formal concept analysis for knowledge discovery in data. We demonstrate the implementation of the proposed method on two real world healthcare datasets. Formal Concept Analysis (FCA) is a mathematical framework that offers a conceptual knowledge representation through hierarchical conceptual structures called concept lattices. However, during the design of a concept lattice, complexity plays a major role.

International Journal of Applied Mathematics and Computer Science

Knowledge discovery in data using formal concept analysis and random projections

During the design of concept lattices, complexity plays a major role in computing all the concepts from the huge incidence matrix. Hence for reducing the size of the lattice, methods based on matrix decompositions like SVD are available in the literature. However, SVD computation is known to have large time and memory requirements. In this paper, we propose a new method based on Fuzzy K-Means clustering for reducing the size of the concept lattices. We demonstrate the implementation of proposed method on two application areas: information retrieval and information visualization. © 2009 Elsevier Ltd. All rights reserved.

Expert Systems with Applications

Concept lattice reduction using fuzzy K-Means clustering

2012 Fourth International Conference on Knowledge and Systems Engineering

Improving the formal concept analysis algorithm to construct domain ontology

Applied Soft Computing

Merging domain ontologies based on the WordNet system and Fuzzy Formal Concept Analysis techniques

A Survey of State of the Art of Ontology Construction and Merging using Formal Concept Analysis

Journal	Indian Journal of Science and Technology
Publisher	Indian Society for Education and Environment
ISSN	0974-6846
Open Access	Yes