Z. M. Ma

Fuzzy data modeling based on XML schema

Interest in XML has been growing over the last few years and XML has been the de-facto standard of information representation and exchange over the web. However, the real world is filled with imprecision and uncertainty. Classical databases have been extended to deal with imprecise and uncertain data. In this paper, we investigate how to incorporate fuzzy data into XML. We identify multiple granularity of data fuzziness in XML. Based on possibility distribution theory, we have possibilities associated with elements as well as attribute values of elements in XML. A fuzzy XML data model that addresses all of the fuzziness is developed based on XML Schema.

Efficient processing of twig pattern matching in fuzzy XML

In order to find all occurrences of a twig pattern in XML documents, a considerable amount of twig pattern matching algorithms have been proposed. At the same time, previous work mainly focuses on twig pattern query under the complete semantics. However, there is often a need to produce partial answers because XML data may have missing sub-elements. Furthermore, the existed works fall short in their ability to support twig pattern query under different semantics in fuzzy XML. In this paper, we study the problem of twig matches in fuzzy XML. We begin by introducing the extended region scheme to accurately and effectively represent nodes information in fuzzy XML. We then discuss the fuzzy query semantics and compute the membership information by using Einstein operator instead of Zadehs min-max technique. On the basis, we propose two efficient algorithms for querying twig under complete and incomplete semantics in fuzzy XML. The experimental results show that our proposed algorithms can perform on the fuzzy twig pattern matching efficiently.

Knowledge representation and reasoning of XML with ontology

Today XML has reached a wide acceptance as the data exchange format for e-commerce. Unfortunately, XML covers the syntactic level, but lacks semantics. Ontology can represent shared domain knowledge and enable semantic interoperability. Therefore, in this paper, we propose an approach for representing and reasoning on XML with ontologies. The formal definitions of XML Schemas and ontologies are given. On this basis, for representing XML with ontologies, we propose an approach which can translate the XML Schema into the ontology. Based on the constructed ontologies, we study how to reason on XML by means of ontologies, so that the reasoning problems of XML (e.g., conformance, inclusion, and equivalence) may be reasoned through the reasoning mechanism of ontologies.

Enhancements of proof number search in connect6

Based on PN search which is a best first search, a new search algorithm named PN# is proposed in this paper. Being easiest to (dis)prove, the node with least pn or dn is always expanded first in the naïve implementation of PN search. However, the frontier nodes can not be sorted by the initialized value of their pn and dn. Especially, the brother nodes is always expanded at the same degree though some of them are superior than others. For above reasons, the domain knowledge is introduced, which makes the key factors to select the nodes to be expanded next is combined with the weight of the moves, besides the threshold pn and dn. Our experiments show that compared with the naïve PN, PN# enhances the speed of program, reduces the size of search tree, and strengthens the ability to solve a position.

Algebraic operations in fuzzy object-oriented databases

The fuzzy object-oriented databases have been proposed to meet the need of dealing with fuzzy as well as complex objects. In this paper, we present a formal fuzzy object-oriented database model. Based on the semantic measure of fuzzy data, we first identify two kinds of fuzzy object redundancies, which are inclusion redundancy and equivalence redundancy, and then define three kinds of merging operation for redundancy removal. On the basis, we define some fuzzy algebraic operations for fuzzy classes and fuzzy objects. Finally, in the paper, we discuss fuzzy querying strategies and give the form of SQL-like fuzzy querying for the fuzzy object-oriented databases.

A method to construct knowledge table-base in k-in-a-row games

In any k-in-a-row game, the player should always analyze each consecutive sequence in 4 directions, which consists of either the void intersections or the intersections occupied by the same color stones. Although the difficult problem in k-in-a-row game is that any void intersection on board can be placed a stone on, just like Go, however, the hints in k-in-a-row are far more than those in Go. We find it is a good method to decrease the complexity of the k-in-a-row games by using Connection to represent the states of the game position. Then, a precise classification criterion for Connection, as well as a precise classification for the intersections is given. As the high-level knowledge of games seems hard to be acquired, the program designers always resort to human masters. However, we can construct a good knowledge table-base based on above idea without masters.

Storing fuzzy ontology in fuzzy relational database

Information imprecision and uncertainty exist in many real-world applications and for this reason fuzzy ontologies have been extensively investigated and increasingly created. Therefore, it is critical to develop scalable and efficient fuzzy ontology storage mechanism. The fuzzy relational database may be a good candidate for storing fuzzy ontologies because of the widespread use and mature techniques. In this paper, we propose an approach for storing fuzzy ontologies in fuzzy relational databases. The elements of fuzzy ontologies are introduced first, where most of constructors of fuzzy ontologies are considered. On this basis, we propose an approach for storing all of these elements of fuzzy ontologies in fuzzy relational databases, and an example is provided throughout the paper to well explain the approach.

Functional Dependencies in Vague Relational Databases

In order to model the real world with imprecise and uncertain information, various extended relational data models were proposed. Vague set, as a generalized fuzzy set, has more powerful ability to process fuzzy information than fuzzy set. In this paper, we propose a kind of vague relational model and a new similarity measure between vague sets based on the set-theoretic approach. Based on the proposed similarity measure, the paper focuses on the issues of vague functional dependencies (VFDs), and then proposes a set of sound inference rules, which are similar to Armstrongs axioms for classical case, for vague functional dependencies. Finally, the paper presents the satisfaction degree of the VFDs and the formula to determine the satisfaction degree of VFDs.

ICFC: A method for computing semantic similarity among fuzzy concepts in a fuzzy ontology

Semantic similarity calculation is an important step of ontology mapping that is an effective method to solve the problem of ontology heterogeneity. However, current semantic similarity methods can be only adapted to crisp concepts, that is to say, they are not sufficient for handling fuzzy concepts whose instances belong to them with memberships. Therefore, this paper proposes a novel semantic similarity method, ICFC (Information Content of Fuzzy Concept), for fuzzy concepts in a fuzzy ontology. In this method, a semantic similarity between two fuzzy concepts is computed by their information content values. But, it is difficult to obtain information content values for fuzzy concepts. That is because a fuzzy concept is considered as a fuzzy set, and all the elements (i.e., instances) belong to it with memberships. For this purpose, ICFC achieves two tasks: (i) determines whether or not an instance belongs to a fuzzy concept; (ii) provides a method for computing the membership of instance to fuzzy concept, which exploits the degrees that the attribute domains of fuzzy concept include the attribute values of instance. Experimental results with a fuzzy ontology from the real world indicate that ICFC performs encouragingly well.

Formal approach and automated tool for constructing ontology from object-oriented database model

Extracting domain knowledge from databases can facilitate the development of Web ontologies. In this paper, a formal approach and an automated tool for constructing ontologies from Object-oriented database models (OODMs) are developed. The approach and tool can automatically translate an OODM and its corresponding database instances into the ontology structure and ontology instances, respectively. Case studies show that the approach is feasible and the automated construction tool is efficient.