Anna Formica

Ontology-based concept similarity in Formal Concept Analysis

Both domain ontologies and Formal Concept Analysis (FCA) aim at modeling concepts, although with different purposes. In the literature, a promising research area concerns the role of FCA in ontology engineering, in particular, in supporting the critical task of reusing independently developed domain ontologies. With this regard, the possibility of evaluating concept similarity is acquiring an increasing relevance, since it allows the identification of different concepts that are semantically close. In this paper, an ontology-based method for assessing similarity between FCA concepts is proposed. Such a method is intended to support the ontology engineer in difficult activities that are becoming fundamental in the development of the Semantic Web, such us ontology merging and ontology mapping and, in particular, it can be used in parallel to existing semi-automatic tools relying on FCA.

Concept similarity in Formal Concept Analysis: An information content approach

Abstract Formal Concept Analysis (FCA) is revealing interesting in supporting difficult activities that are becoming fundamental in the development of the Semantic Web. Assessing concept similarity is one of such activities since it allows the identification of different concepts that are semantically close. In this paper, a method for measuring the similarity of FCA concepts is presented, which is a refinement of a previous proposal of the author. The refinement consists in determining the similarity of concept descriptors (attributes) by using the information content approach, rather than relying on human domain expertise. The information content approach which has been adopted allows a higher correlation with human judgement than other proposals for evaluating concept similarity in a taxonomy defined in the literature.

Semantic Web search based on rough sets and Fuzzy Formal Concept Analysis

Fuzzy Formal Concept Analysis (FFCA) is a generalization of Formal Concept Analysis (FCA) for modeling uncertainty information. FFCA provides a mathematical framework which can support the construction of formal ontologies in the presence of uncertainty data for the development of the Semantic Web. In this paper, we show how rough set theory can be employed in combination with FFCA to perform Semantic Web search and discovery of information in the Web.

Concept Similarity in SymOntos: An Enterprise Ontology Management Tool

playsan important role. It is constantly used whenever certain goods or services are not availablewith the required characteristics. Then a substitute may be accepted, as far as it is sufficientlyclose to what was originally required. In this paper we propose a method for evaluating conceptsimilarity. The work has been performed within the

Similarity of XML-Schema Elements

EXtensible Markup Language (XML)-Schemas are the emerging standards for describing and validating semi-structured documents across the Internet, due to the rich set of modeling constructors, types and constraints they provide. Semantic similarity is growing in importance in different settings, such as digital libraries, heterogeneous databases and, in particular, the Semantic Web. The focus of this paper is the definition of a method for determining semantic similarity of XML-Schema elements in the presence of type hierarchies. Such a method has been defined by combining and revisiting: (i) the information content approach, and (ii) a method for comparing the structural components of type declarations, inspired by the maximum weighted matching problem in bipartite graphs.

CONCEPT SIMILARITY IN FUZZY FORMAL CONCEPT ANALYSIS FOR SEMANTIC WEB

This paper presents a method for evaluating concept similarity within Fuzzy Formal Concept Analysis. In the perspective of developing the Semantic Web, such a method can be helpful when the digital resources found on the Internet cannot be treated equally and the integration of fuzzy data becomes fundamental for the search and discovery of information in the Web.

Correctness of ISA hierarchies in object-oriented database systems

In Object-Oriented databases, ISA hierarchy provides a powerful modeling tool that, through the inheritance mechanism, yields a coincise description of the world. In this paper, we tackle the problem of the correctness of ISA hierarchies in Object-Oriented database schemas. In general, the inheritance mechanisms proposed in literature do not preserve subtyping, i.e. they are not sound. Strict inheritance has been proposed in order to guarantee subtyping after inheritance. However, strict inheritance is sound but fails to cope with a significant class of schemas. The problem relies on refinement rules. In the paper, we characterize the schemas for which strict inheritance is sound and complete and we present a methodology aimed at identifying such schemas. The problem is addressed using the Object-Oriented data definition language TQL.

Integrity Constraints Representation in Object-Oriented Databases

This paper presents a Data Definition Language (DDL), called TQL, based on an Object-Oriented data model characterized by the possibility of expressing integrity constraints in the schema of the database. This work originates from the need to enrich the amount of knowledge represented, declaratively, in the database schema and processed by the Database Management Systems (DBMS). The proposed approach allows the reduction of the amount of code in methods. However, by increasing the power of the DDL, the possibility of introducing errors in the schema also increases. Therefore, rich data models require enhanced checking facilities in order to support the design phase. In the paper, after having formally presented the language TQL, the notions of satisfiability and correctness of a TQL schema, which are strictly related to the notion of legal database state, are introduced. These issues are presented using a formal approach based on a denotational semantics which concerns both the structural part of the schema and the integrity constraints.

Similarity reasoning for the semantic web based on fuzzy concept lattices: An informal approach

Similarity Reasoning in the presence of vague information is becoming fundamental in several research areas and, in particular, in the Semantic Web. Fuzzy Formal Concept Analysis (FFCA) is a generalization of Formal Concept Analysis (FCA) for modeling uncertainty information. Although FFCA has become very interesting for supporting different activities for the development of the Semantic Web, in the literature it is usually addressed at a technical level and intended for a restricted audience. This paper proposes a similarity measure for FFCA concepts. The key notions underlying the proposed approach are presented informally, in order to reach a broad audience of readers.

Semantic search for matching user requests with profiled enterprises

Semantic search is an important approach that promises significant improvements for customers to identify products of their interest. To perform semantic search, enterprises need to publish semantically enriched descriptions of their offered goods and services; then a customer expresses his/her request, in an easy Google like fashion, by providing a list of desired features. If enterprise offerings and customer requests are based on the same vocabulary (i.e., ontology), they can be semantically matched by using advanced semantic methods. In this paper, we propose an ontology-based method aimed at finding the best matches between a user request and the services offered by different enterprises. We assume that in a given business ecosystem (in the paper, as an example, the tourism sector) a group of SMEs agree on the adoption of a reference ontology, used to build the company profiles on the basis of the offered services. Accordingly, a user request, represented by a set of desired features, is expressed in terms of the reference ontology terminology (i.e., concepts). In this paper, we illustrate SemSim, a method used to collectively search the SME profiles to identify the services that match at best the user request. SemSim is based on the well-known information content approach used to evaluate the semantic similarity between concepts. The experimental results show that our proposal performs better than some of the most representative similarity search methods proposed in the literature.