Suk-Hyung Hwang

A FCA-Based ontology construction for the design of class hierarchy

One of the most tasks of object-oriented software designers is the design of the class hierarchy and the relationships among classes. Since there are many conceptual similarities with the design of an ontology, and an ontology is semantically richer than a UML class model, it makes sense to put the emphasis on ontology design. That is, an object-oriented software designer can design an ontology by organizing object classes in a class hierarchy and creating relationships among classes. UML models can then be generated from the ontology. In this paper, we introduce the Formal Concept Analysis(FCA) as the basis for a practical and well founded methodological approach to the construction of ontology. We show a semi-automatic, graphic and interactive tool to support this approach. The purpose of this work is to provide a semi-automatic methods for the ontology developers. We describe here the basic ideas of the work and its current state.

FARM: An FCA-based Association Rule Miner

Abstract Association rule mining is a well-researched and widely applied data mining technique for discovering regularities between items in a dataset. An association rule consists of an antecedent and a consequent with two measures, named support and confidence, which indicate how valuable the rule is. For several decades, intensive studies have been made on efficient association rule mining methods aiming to reduce rule-extraction time and to prevent generation of redundant rules. By incorporating negation and disjunction operators into antecedents, our study offers richer expressive power in describing user interests as antecedents, which in turn translates into more valuable association rules whose consequents match the expressed user interests. This study consists of three components: (1) a conceptual model, called plant , that represents necessary constituents for the proposed extended association rules; (2) three algorithms, called CULTIVATION algorithm family , that demonstrate how the extended association rule is processed within the model; (3) a full-fledged Java-based system, called FARM (FCA-based Association Rule Miner), that is a computerized implementation of our approach. Finally, in order to verify the efficiency and usefulness of our approach, experiments were carried out that compared the approach with extant representative methods.

An Agent Environment for Contextualizing Folksonomies in a Triadic Context

Standardized infrastructure for information or knowledge sharing is required to make autonomous agents interdependent on each other for effective collaboration in a multi-agent system. Folksonomy has become very popular as an enabling technology to provide a common conceptualization of the data that agent systems use. However, there are problems on free-form tagging in folksonomy. Folksonomy is only concerned with a group of instances which are labeled with tags without a formal definition. No available tool provides a way to contextualize folksonomies with respect to users, communities, goals, tasks, and so on. There is no formal approach to classifying and sharing tags that reflect a users mental model of information resources in terms of folksonomy. We present a novel approach to developing an agent environment for contextualizing folksonomies in a triadic context using Formal Concept Analysis. We conducted an experiment to build concept hieracrhies and contextualize folksonomies from tags of blogosphere.

Normalizing Class Hierarchies Based on the Formal Concept Analysis

Class hierarchies often constitute the backbone of object-oriented software systems. Building “good” class hierarchies is a very important and common task, but such hierarchies are not so easy to build and evolve. Therefore, their construction and evolution are very important issues in component-based and object-oriented software engineering. In this paper, we present a normalized form of class hierarchy based on the concept lattice of formal concept analysis. Our approach provides the theoretical bases for the creation and evolution of well-defined object-oriented class hierarchy structures.

A FCA-based approach for enriching users' knowledge base

Despite of the various benefits obtainable from Formal Concept Analysis (FCA) in knowledge base construction, FCA-based approaches are not enough to help an expert enrich his knowledge. This is because they provide only the clusters constructed with user-defined knowledge and super-sub relation between the clusters. In this paper, we propose an approach that provides a user with a guideline by suggesting undiscovered knowledge in the form of predicates. This approach firstly generates a set of candidate predicates by analyzing a pre-defined predicate by users. Second, it discards unqualified ones from the set of candidate predicates using a filtering method dealing with two criteria, uniqueness and support. The qualified candidate predicates are suggested, and selected by the user, and finally, his knowledge is enriched by merging the selected predicates with pre-defined ones.

A FCA-based classification of uncertainty data using rough clustering

Although the amount of electronically stored data is continuously increasing on the internet, there are no good solutions to easily deal with uncertainty contained in datasets. Formal Concept Analysis(FCA) classifies data based on the ordinary set into concept units which consists of objects and attributes that those objects have commonly. However, FCA is insufficient to process and analyze vague data, such as rough and fuzzy data. In this paper, we propose a new FCA-based approach for rough clustering in order to discovery implicit knowledge from given vague fuzzy datasets. Moreover, we show some experiments that demonstrate how our approach can be applied on web mining. Our research results would be helpful for clustering and classifying the vague web data, in particular when dealing web resources with the uncertainty.

On Developing a Semantic Annotation Tool for Managing Metadata of Web Documents based on XMP and Ontology

The goal of Semantic Web is to provide efficient and effective semantic search and web services based on the machine-processable semantic information of web resources. Therefore, the process of creating and adding computer-understandable metadata for a variety of web contents, namely, semantic annotation is one of the fundamental technologies for the semantic web. Recently, in order to manage annotation metadata, direct approach for embedding metadata into the document is mainly used in semantic annotation. However, many semantic annotation tools for web documents have been mainly worked with HTML documents, and most of these tools do not support semantic search functionalities using the metadata. In this paper, based on these problems and previous works, we propose the Ontology-based Semantic Annotation tool(OSA) to efficiently support semantic annotation for web documents(such as HTML, PDF). We define a semantic annotation model that represents ontological-semantic information by using RDFS(RDF Schema). Based on XMP(eXtensible Metadata Platform) standard, the model is encoded directly into the document. By using OSA with XMP, user can perform semantic annotation on web documents which are able to keep compatibility for managing annotation metadata. Eventually, the integrated semantic annotation metadata can be used effectively in semantic search for a variety of web contents.

An ontology analysis and error detection tool based on concept hierarchy structures

An ontology as the core element of Semantic Web is a formal specification of a conceptualization of shared domain knowledge. The use of well-defined ontologies can increase the quality of interoperable information systems in the area of Semantic Web. However, in practice, it is not easy to develop high-quality ontologies which have no errors. Therefore, with methodologies for ontology design, various methods or tools for ontology analysis supporting for error-detection might be very helpful for ontology developers. In this paper, we propose a novel approach for analyzing the core constructs of ontology based on the Formal Concept Analysis and develop a tool that supports error-checking ontologies. Our approach can serve not only as a guidance to modify the existing ontologies, but also as a valuable tool in developing high-quality ontologies.

On Development of an Automatic Tool for Extracting Association Rules of a user query using Formal Concept Analysis

ABSTRACT Formal Concept Analysis (FCA) is a widely used methodology for data analysis, which extracts concepts and builds a concept hierarchy from given data. A concept consists of objects and attributes shared by those objects, and a concept hierarchy includes information on super-sub relations among the concepts. In this paper, we propose a method for extracting Implication and Association rules from a concept hierarchy given a query by a user. The method also describes a way for displaying the extracted rules. Based on this method, we implemented an automatic tool, QAG-Wizard. Because the QAG-Wizard not only elicits relation information for the given query, but also displays it in structured form intuitively, we expect that it can be used in the fields of data analysis, data mining and information retrieval for various purposes.Key Words:Formal Concept Analysis, Concept lattice, User Query, Implication, Association Rules 1. 서 론 1) 대용량 데이터를 저장하고 관리할 수 있는 데이터베이스 기술과 정보기술이 발전함에 따라, 개인 및 조직이 보유하고 접근할 수 있는 데이터의 양은 기하급수적으로 증가하고 있다. 그러나, 가용한 데이터 양이 증가함에 따라서, 대용량 데이터를 사용하는 도메인의 특성을 수월하게 파악하기 어렵다는 문제점이 발생하고 있다. 이러한 문제점을 해결하기

The Development of an Automatic Tool for Formal Concept Analysis and its Applications on Medical Domain

For extracting and processing information explicitly from given data, Formal Concept Analysis(FCA) is provided a method which is widely used for data analysis and clustering. The data can be structured into concepts, which are formal abstractions human thought allowing meaningful comprehensible interpretation. However, most FCA tools mainly focus on analyzing one-valued contexts that represent objects, attributes and binary relations between them. There we few FCA tools available that provide scaling and analyzing many-valued contexts representing objects, attributes and relations with attributes` values. In this paper, we propose not only a scaling algorithm for interpreting and simplifying the multivalued input data, but also an algorithm to generate concepts and build concept hierarchy from given raw data as well. Based on these algorithms, we develop an automate tool, FCA Wizard, for concept analysis and concept hierarchy. We also present FCA Wizard based applications in medical domain.