Ontology, Metadata, and Semiotics

Journal of Grid Computing, 2006

Service discovery and matchmaking in a distributed environment has been an active research issue for some time now. Previous work on matchmaking has typically presented the problem and service descriptions as free or structured (marked-up) text, so that keyword searches, tree-matching or simple constraint solving are sufficient to identify matches. In this paper, we discuss the problem of matchmaking for mathematical services, where the semantics play a critical role in determining the applicability or otherwise of a service and for which we use OpenMath descriptions of pre-and post-conditions. We describe a matchmaking architecture supporting the use of match plug-ins and describe five kinds of plug-in that we have developed to date: (i) A basic structural match, (ii) a syntax and ontology match, (iii) a value substitution match, (iv) an algebraic equivalence match and (v) a decomposition match. The matchmaker uses the individual match scores from the plug-ins to compute a ranking by applicability of the services. We consider the effect of pre-and post-conditions of mathematical service descriptions on matching, and how and why to reduce queries into Disjunctive Normal Form (DNF) before matching. A case study demonstrates in detail how the matching process works for all four algorithms.

Lecture Notes in Computer Science, 2008

In this paper we explore some of the opportunities and challenges for machine learning on the Semantic Web. The Semantic Web provides standardized formats for the representation of both data and ontological background knowledge. Semantic Web standards are used to describe meta data but also have great potential as a general data format for data communication and data integration. Within a broad range of possible applications machine learning will play an increasingly important role: Machine learning solutions have been developed to support the management of ontologies, for the semi-automatic annotation of unstructured data, and to integrate semantic information into web mining. Machine learning will increasingly be employed to analyze distributed data sources described in Semantic Web formats and to support approximate Semantic Web reasoning and querying. In this paper we discuss existing and future applications of machine learning on the Semantic Web with a strong focus on learning algorithms that are suitable for the relational character of the Semantic Web's data structure. We discuss some of the particular aspects of learning that we expect will be of relevance for the Semantic Web such as scalability, missing and contradicting data, and the potential to integrate ontological background knowledge. In addition we review some of the work on the learning of ontologies and on the population of ontologies, mostly in the context of textual data.

Research in Social Change

The aim of this paper is the analysis and a presentatition of texts and terminological ontology of scientific and professional publications in the field of regional Industrial Symbiosis and Industrial Symbiotic Networks. With computer-aided text analysis it is possible to have a comprehensive overview of the publications of works in this field and a more detailed presentation of the most fruitful concepts of publications.

Lecture Notes in Computer Science, 2006

There are two aspects of interestingness of rules, objective and subjective measures ([7], [1], [15], [16]. Objective measures are datadriven and domain-independent. Generally, they evaluate the rules based on their quality and similarity between them. Subjective measures are user-driven, domain-dependent, and include unexpectedness, novelty and actionability [7], [1], [15], [16]. Liu [7] defines a rule as actionable one, if user can do an action to his/her advantage based on that rule. In [12] it was assumed that actionability has to be expressed in terms of changes in values of certain attributes which are used in an information system. They introduced a new class of rules (called action rules) which are constructed from certain pairs of association rules extracted from the same information system. Conceptually similar definition of an action rule was proposed independently by [4]. Action rules have been investigated further in [14], [13], [11], and [18]. In order to construct action rules it is required that attributes in a database are divided into two groups: stable and flexible. Flexible attributes are used in a decision rule as a tool for making hints to a user what changes within some of their values are needed to reclassify a group of objects from one decision class into another one. In this paper, we give a strategy for constructing all action rules from a given information system and show that action rules constructed by system DEAR, presented in [13], cover only a small part of all action rules. Clearly, we are not interested in all action rules as we are not interested in extracting all possible rules from an information system. Classical strategies like See5, LERS, CART , Rosetta, W eka are discovering rules which classification part is either the shortest or close to the shortest. This approach is basically ruling out all other classification rules unless their are surprising rules [17]. In this paper, we introduce the notion of a cost of an action rule and define interesting action rules as rules of the smallest cost. We give a strategy showing how interesting action rules can be generated from action rules discovered system DEAR.

Software & Systems Modeling, 2005

In November 2000, the OMG made public the MDA™ initiative, a particular variant of a new global trend called MDE (Model Driven Engineering). The basic ideas of MDA are germane to many other approaches such as generative programming, domain specific languages, model-integrated computing, generic model management, software factories, etc. MDA may be defined as the realization of MDE principles around a set of OMG standards like MOF, XMI, OCL, UML, CWM, SPEM, etc. MDE is presently making several promises about the potential benefits that could be reaped from a move from codecentric to model-based practices. When we observe these claims, we may wonder when they may be satisfied: on the short, medium or long term or even never perhaps for some of them. This paper tries to propose a vision of the development of MDE based on some lessons learnt in the past 30 years in the development of object technology. The main message is that a basic principle ("Everything is an object") was most helpful in driving the technology in the direction of simplicity, generality and power of integration. Similarly in MDE, the basic principle that "Everything is a model" has many interesting properties, among others the capacity to generate a realistic research agenda. We postulate here that two core relations (representation and conformance) are associated to this principle, as inheritance and instantiation were associated to the object unification principle in the class-based languages of the 80's. We suggest that this may be most useful in understanding many questions about MDE in general and the MDA approach in particular. We provide some illustrative examples. The personal position taken in this paper would be useful if it could generate a critical debate on the research directions in MDE.