ISSN (Print) : 0974-6846 Indian Journal of Science and Technology, Vol 9(28), DOI: 10.17485/ijst/2016/v9i28/95135, July 2016 ISSN (Online) : 0974-5645 Fuzzy Concept Lattice for Ontology Learning and Concept Classification K. Selvi1* and R. M. Suresh2 1Sathyabama University, Sholinganallur, Chennai - 600119, Tamil Nadu, India; [email protected] 2Sri Lakshiammal Engineering College, Chennai.- 600126, Tamil Nadu, India; [email protected] Abstract Objective: Methods To discover the the new latent concept, which is significant to self-learning and machine learning. To better understand the conceptual relations of the query terms. : The application of Fuzzy Formal Concept to construct a Concept LatticeFindings: that better describes the semantic relations of incoming patterns for a collection of documents. There has been little work that evaluates the effect of various techniques and parameterApplications/Improvements: settings in the word space construction The results from corpora. The present paper experimentally investigates how the choice of a particular domain helps the user to discover the information which is much closer to his preferences. generated using our novel approach has been experimented for the context of finding the Sea side Schools based on user requirements. A comparision of related papers is performed to encounter the challenging issues of Ontology construction for text classification in Semantic Web. Keywords: Fuzzy Formal Concept Lattice, Machine Learning, Ontology, Semantic Web, Web Text Analysis 1. Introduction 1.1 Principles of Ontology Construction Ontology is defined as a lexical representation of seman- Ontology construction can be categorized into two tic relations that occur between word pairs which can be approaches. One is the bottom up approach that is represented as a conceptual model for any context. For widely used in mathematics and the other is the top expressing and processing knowledge, ontology plays a down approach that is most suitable for the domain with vital role in web text analysis tasks and widely used in objects and attributes4. In top down approach, the relation all machine learning algorithms. To process a language between the objects and attributes is fragmented on the is to understand and accurately apply its grammar. The basis of the object language for a particular domain that is context of lexis of an entity object for a particular field obtained by following the steps given below: consists of words that represent the properties and rela- • List the domain according to the context. tions of valid constants. We can articulate boundaries on • Find the prime lexis. the achievable value of predicates by using the axioms1. • Apply the grammar rules. Those axioms are used to identify the semantic relations • Include the secondary terms by intended defini- between the query terms, which will implicitly follow the tions. interpretation of these primary terms. At the other end, • Extend the depth of search by finding the tertiary the axioms find the set of possible interpretation of the terms. query words that match the formal scheme into the lexes Initially the lexemes required to form the language of the language. That is why Ontology is chosen to find descriptions for a given context are listed with a well- only related information. defined lexicon. As a next step, the axioms and predicates *Author for correspondence Fuzzy Concept Lattice for Ontology Learning and Concept Classification that match the selected lexemes are identified. The struc- document classification namely, the naive Bay’s classifier, tural properties of the given terms and their relations the nearest neighbourhood classifier, decision trees and are identified by applying the grammar rules. Using the a subspace method. These were applied to Yahoo news vocabulary of the primary terms, the secondary and groups like business, entertainment, health, international, tertiary terms are identified using the predicates of the politics, sports and technology individually and in com- individual terms in the domain. The formulation of axi- bination9. oms will use a variable that is representing an individual In10 proposed a Iceberg Concept Lattice which is a for example, if a is the daughter of b and b is the bother huge concept lattice that is clustered into small one based of c then c is the uncle of a, which is an axiom relating on concept clustering algorithm10–13, which is conducive the predicates Daughter Of, Brother Of and Uncle Of. a, b to concept lattice’s scaling and displaying. In14–17 identifies and c are here understood to characterize unnamed per- articles for a specific domain with the linked classification sons. The semantic relations among the terms are clearly information available in Wiki pages. But only a concept recognized with the structural properties specified in the hierarchy can be built for the available pages. In18,19 the above steps for all iterations. authors demonstrate how ontology helps in identifying clinical predictors for coronary heart disease. The depth 1.2 Ontology Construction Approaches of the conceptual expression is easily understood which help us in implementing operational aspect of the seman- Linguistic analysis: This method is a compilation hypoth- tic web. Classification is often used as a basis for many esis of a lexical dictionary where the pecking order of machine-learning applications. As a part of data security, conceptions is designed without human intervention. knowledge discovery and classification remain and work Lexical dictionaries possess words along with their syn- together during various query-processing steps21. Our onyms, root words, word origins, etc., which forms the proposed method enables attribute classification that basis for other construction methods. Combinations of several classifiers did not improve the classification accu- helps us to determine the facilities based on our require- racy as that of other individual classifier5. We tackle the ments. Data classification requires both manual process problem of information processing by an elegant way of and automatic tools to achieve high percentage of effi- 22 representing uncertainty in data. The objective of this ciency . ontology construction is to better understand the seman- The major criteria of fuzzy logic are fuzzy sets, lin- tic relations of the incoming patterns in machine learning guistic patchy, possibility distributions, and fuzzy if and to discover concepts and relation between the objects – then rules. Fuzziness or Degree of Uncertainty per- and its attributes. tains to the uncertainty associated with a system, i.e., the In6 finds the cluster index using c-means fuzzy clus- fact that nothing can be predicted with exact. Fuzzy set tering algorithm where is the maximum number theory incorporates a coherent basis for machine learn- of cluster partition for ‘n’ clusters which is not satisfac- ing and also forms an elegant, statistically well founded, tory for all context. In7 identifies the cancer cells using illustration of the improbability in the data. Subsequently fuzzy enhanced mammogram approach that took less the data that are to be operated are frequently impre- time compared to the existing methods. In8 classifies cise. Application of fuzzy set theory or its derivatives has documents based on concept and semantic relations become a common approach in recent years in all text by observing the activities and comparing with the fea- mining and machine learning techniques23–26. tures extracted accessed for the corresponding context in This paper is structured as follows. Next section con- which they occur. fers the need for Ontology Learning and explains how The lexical dictionary based method is restricted to the Concept Lattice is constructed briefly. In Section the category size of the word list and can therefore form 3, a survey of domain ontology’s and the role of Fuzzy domains having dissimilar scopes. This lexical diction- Concept is given with the various applications in the field ary based ontology has a formal portrayal which is not of Natural Language Processing. Successively, in Section pertained to a particular domain. After integrating with 4, the overview of the proposed method for constructing other methods an ontological framework is developed for concept lattices is accessible, followed by the comparative Ontology learning. There are four different techniques for study of interrelated methods used by various authors. 2 Vol 9 (28) | July 2016 | www.indjst.org Indian Journal of Science and Technology K. Selvi1 and R. M. Suresh Finally, Section 6 projects the views of how this work can #Rule 1: Concept Node → class be extended to generalize this technique for all domains. #Rule 2 :Extension collection of Concept → Instance of Class 2. Materials and Methods #Rule 3: Intension collection of Concept → Attribute of class Ontology provide a frequent indulgent of exclusive Figure 1. Conversion rules. domains that can be shared among people and particu- lar application systems. Many deficiencies still exists in Fuzzy Formal Concept Analysis is a overview of Fuzzy ontology. It is hard to find the granularity of ontology and Concept Analysis for exhibiting improbability of data. It the depth of concept expression. Moreover Formal con- is a binary table K = {G, M, I} called formal context, where cept analysis can help resolving the problem that ontology G is a set of Objects, M is a set of attributes and I represents is poor in describing the depth of information27.