AUTOMATIC FUZZY ONTOLOGY GENERATION FOR SEMANTIC WEB
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23-12-2010, 01:54 PM


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ADALF M



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ABSTRACT
The Semantic Web is a project and implimentation that intends to create a universal medium for information exchange by putting documents with computer-processable meaning (semantics) on World Wide Web in a standardized way.
Semantic Web is capable of integrating the day-to-day mechanisms of trade, bureaucracy and our daily lives so that they will be handled by machines talking to machines, making them intelligent agents.
Ontology is a conceptualization of a domain into a human understandable, machine-readable format consisting of entities, attributes, relationships, and axioms. It is used as a standard knowledge representation for the Semantic Web.
Ontology is an effective conceptualism commonly used for the Semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA (Fuzzy Ontology Generation framework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: Fuzzy Formal Concept Analysis, Concept Hierarchy Generation, and Fuzzy Ontology Generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.

INTRODUCTION
Currently there is much data on our computers which we cannot browse, or process by, including personal data like calendars, playlists, GPS coordinates, bank statements; enterprise product and workflow and resources; and public data such as weather, events and the properties of materials.ONTOLOGY is a conceptualization of a domain into ahuman understandable, machine-readable format consisting of entities, attributes, relationships, and axioms. It is used as a standard knowledge representation for the Semantic Web. However, the conceptual formalism supported by typical ontology may not be sufficient to represent uncertainty information commonly found in many application domains due to the lack of clear-cut boundaries between concepts of the domains. For example, a document can be very relevant, relevant, or irrelevant to a research area. In addition, keywords extracted from scientific publications can be used to infer the corresponding research areas. However, it is inappropriate to treat all keywords equally as some keywords may be more significant than others. To tackle this type of problems, one possible solution is to incorporate fuzzy logic into ontology to handle uncertainty data. Traditionally, fuzzy ontology is generated and used in text retrieval and search engines , in which membership values are used to evaluate the similarities between the concepts in a concept hierarchy.
However, manual generation of fuzzy ontology from a predefined concept hierarchy is a difficult and tedious task that often requires expert interpretation. So, automatic generation of concept hierarchy and fuzzy ontology from uncertainty data of a domain is highly desirable.
1.2 OUR OBJECTIVE OF PROJECT
In this project and implimentation, we propose a framework known as FOGA (Fuzzy Ontology Generation framework) that can automatically generate a fuzzy ontology from uncertainty data based on Formal Concept Analysis (FCA) theory. The generated fuzzy ontology is mapped to a semantic representation in OWL (Web Ontology Language) For analyzing these data we need to pull all of them into a spreadsheet, graphing it or joining it with other data. This is potentially impossible.


1.1 Overview of the System
The conceptual formalism supported by typical ontology is not sufficient to represent uncertainty information commonly found in many application domains.There would be lack of or no clear-cut boundaries between concepts of various domains.
Keywords extracted from documents can be used to infer the corresponding information, but it is inappropriate to treat all keywords equally in terms of significance and situations.
Fuzzy Logic is a problem-solving methodology that provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy or missing-input information. It uses an imprecise but very descriptive language to deal with input data more like a human operator. Incorporating Fuzzy Logic in ontology generation in unsupervised fashion is more appropriate and desirable.
Although editing tools have been developed to help users to create and edit ontology, it is a troublesome task to manually derive ontology from data. typically, ontology can be generated from various data types such as textual data, knowledge-based ,semi-structured schemata , and relational schemata. Compared to other types of data, ontology generation from textual data has attracted the most attention. Among techniques used for processing textual data, clustering is one of the most effective techniques for ontology learning. Conceptual clustering techniques such as COBWEB and CLASS IT are powerful clustering techniques that can conceptualize clusters for ontology generation. Formal Concept Analysis FCA is a formal technique for data analysis and knowledge presentation. It defines formal contexts to represent relationships between objects and attributes in a domain.
From the formal contexts, FCA can then generate formal concepts and interpret the corresponding concept lattice, so that information can be browsed or retrieved effectively. FCA is widely used for various applications, such as, text processing ontology merging e-mail manager, e-learning, Web navigation, and expert system. However, as most concept lattices are quite complicated in terms of the number of concepts generated, it is necessary to simplify the lattice generated. In the Iceberg concept lattice, association rules are typically used for clustering concepts on the lattice. Conceptual scaling or lattice theory is then used to generate the concept hierarchy in the TOSCANA and GALOIS systems, respectively. In order to prune the lattices generated for text mining, clustering is first performed on the data set to generate clusters of documents. Then, feature selection is used to extract frequent keywords (or terms) from documents in each cluster as attributes for the cluster. Traditional FCA-based conceptual clustering approaches are hardly able to represent such vague information. To tackle this problem, fuzzy logic can be incorporated into FCA to handle uncertainty information for conceptual clustering and concept hierarchy generation.

Pollandt, Burusco and Fuentes-Gonzalez , and Huynh and Nakamori have proposed the L-Fuzzy context as an attempt to combine fuzzy logic with FCA. The L-Fuzzy context uses linguistic variables, which are linguistic terms associated with fuzzy sets, to represent uncertainty in the context. However, human interpretation is required to define the linguistic variables. Moreover, the fuzzy concept lattice generated from the L-fuzzy context usually causes a combinatorial explosion of concepts as compared to the traditional concept lattice.

We propose a new technique that combines fuzzy logic and FCA as Fuzzy Formal Concept Analysis (FFCA), in which the uncertainty information is directly represented by a real number of membership value in the range of [0,1]. As such, linguistic variables are no longer needed. Compared to the fuzzy concept lattice generated from the L-fuzzy context, the fuzzy concept lattice generated using FFCA will be simpler in terms of the number of formal concepts. It also supports a formal mechanism for calculating concept similarities.


1.1Problem Definition

The conceptual formalism supported by typical ontology is not sufficient to represent uncertainty information commonly found in many application domains.There would be lack of or no clear-cut boundaries between concepts of various domains.

Keywords extracted from documents can be used to infer the corresponding information, but it is inappropriate to treat all keywords equally in terms of significance and situations.

Fuzzy Logic is a problem-solving methodology that provides a simple way to arrive at a definite conclusion based upon vague, ambiguous, imprecise, noisy or missing-input information.

It uses an imprecise but very descriptive language to deal with input data more like a human operator.

Incorporating Fuzzy Logic in ontology generation in unsupervised fashion is more appropriate and desirable.


System Environment
Front End :
The front end is designed and executed with the J2SDK1.4.0 handling the core java part with User interface Swing component. Java is robust , object oriented , multi-threaded , distributed , secure and platform independent language. It has wide variety of package to implement our requirement and number of classes and methods can be utilized for programming purpose. These features make the programmer’s to implement to require concept and algorithm very easier way in Java.
The features of Java as follows:
Core java contains the concepts like Exception handling, Multithreading , Streams can be well utilized in the project and implimentation environment.The Exception handling can be done with predefined exception and has provision for writing custom exception for our application.Garbage collection is done automatically, so that it is very secure in memory management.
The user interface can be done with the Abstract Window tool Kit and also Swing class. This has variety of classes for components and containers. We can make instance of these classes and this instances denotes particular object that can be utilized in our program.
Event handling can be performed with Delegate Event model. The objects are assigned to the Listener that observe for event, when the event takes place the corresponding methods to handle that event will be called by Listener which is in the form of interfaces and executed.
This application make use of ActionListener interface and the event click event gets handled by this. The separate method actionPerformed() method contains details about the response of event.
Java also contains concepts like Remote method invocation, Networking can be useful in distributed environment.
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