Marcin Pietranik

A METHOD FOR ONTOLOGY ALIGNMENT BASED ON SEMANTICS OF ATTRIBUTES

In this article, a method for ontology alignment, which is based on using semantics of attributes describing concepts, is presented. This work is an extended version of our previous works on ontology alignment, which is the task of designating correspondences between two ontologies. After investigating recent approaches, we noticed a lack of analysis of the lowest level of expressing knowledge within them. We treat attributes of concepts as this level and we claim that embedding their precise definition, semantics, and the ways in which they can interact with each other in the process of mapping ontologies can enhance former solutions of this problem. We show that developing such an approach brings us closer to a consistent and more intuitive methodology of aligning ontologies.

Ontology relation alignment based on attribute semantics

The problem of ontology alignment is based on finding mappings between instances, concepts and relations of two ontologies which (following Grubers work [8]) can be defined as explicit specification of decomposition of some part of reality. This specification spreads over three levels of detail: the concept attribute level, the concept level and the relation level. This paper concentrates on identifying matches between relations of concepts which describe how these entities interact with each other. After careful analysis we have noticed that this level can be a source of many inconsistencies when two ontologies are blindly integrated. We take our work on attribute-based concept alignment and the consensus theory as a starting point. We extend it to handle the issues that appear when aligning relations. We give formal definitions along with careful formalization of set of requirements that eventual mapping algorithm should satisfy in order to reliably designate matches between ontologies on relation level.

Preliminary Experimental Results of the Multi-attribute and Logic-Based Ontology Alignment Method

This paper presents partial results of our novel method of ontology alignment, which is based on processing varying semantics of attributes within concepts. Following our previous publications in which we presented well grounded theoretical foundations of the alignment framework, we have created custom built implementation. This paper is an overview of the preliminary experimental results we ware able to gather. We explain the most important problems we encountered while preparing test methodology, describe used input data and give straight comparison to previously used ontology mapping verification procedures.

A Multi-attribute and Logic-Based Framework of Ontology Alignment

Ontology alignment is an issue that focuses on designating the way of migrating the contents of two ontologies. These structures can be treated as a method of decomposing some domain of interest and expressing its complexity by describing semantic correlations between extracted elements. The necessity of transforming information stored in two separated, independent ontologies arises when two computer systems incorporating such ontologies need to be integrated. Careful research on previously proposed methods made us think about a novel approach to this task, based on deeper analysis of basic building blocks of concepts, which are their attributes. This chapter is a comprehensive description of our ideas, conceptions and algorithms. We also give a brief commentary on preliminary results that will illustrate our contribution to the topic.

A New Distance Function for Consensus Determination in Decision Support Systems

Consensus methods are used mainly to solve conflicts of knowledge in decision support systems. Generally speaking, conflicts of knowledge arise from the fact that system nodes (for example, agents, experts) may present various decisions or solutions to the user. This may be due to the use of various methods of decision support or different information sources by agents/experts. If there is a conflict of knowledge in the system and they are not automatically resolving the system cannot generate the final decision, and hence - the decision maker will not receive hints from the system. The use of consensus methods eliminates this problem, because they enable to solve conflicts of knowledge in near real time. At the same time they guarantee the achievement of a good compromise. However, the effective determination of consensus depends, among other, on the correct definition of the distance function.

Preliminary Evaluation of Multilevel Ontology Integration on the Concept Level

In many real situations it is not possible to merge multiple knowledge bases into a single one using one-level integration. It could be caused, for example, by high complexity of the integration process or geographical distance between servers that host knowledge bases that expected to be integrated. The paralleling of integration process could solve this problem and in this paper we propose a multi-level ontology integration procedure. The analytical analysis pointed out that for presented algorithm the one- and multi-level integration processes give the same results (the same final ontology). However, the multi-level integration allows to save time of data processing. The experimental research demonstrated a significant difference between times required for the one- and multi-level integration procedure. The latter could be even 20 \(\%\) faster than the former, which is important especially in the emerging context of Big Data. Due to the limited space we can only consider integration on the concept level.

The Use of an Ontotrigger for Designing the Ontology of a Model Maturity Capsule

The aim of this work is to give the definition and present the possibility of applying (introduced and defined here) ontotriggers to design the ontology of a maturity capsule used in the assessment of IT projects. The complexity of designing ontology processes raises the question of whether there is a need for designing ontologies in a situation where it is possible to map them. The work is divided into four main parts. The first part presents and defines the concept of an ontotrigger. The second part presents a model maturity capsule. Similarities to the maturity capsule of a project managed in accordance with the SCRUM methodology have also been indicated. The third part discusses the method of building ontologies for both capsules and indicates the possibility of mapping them. The fourth part presents the application of an ontotrigger which uses the ability to map both ontologies. In summary, the applicability of ontotriggers has been demonstrated for the design of ontologies of any class and their objects. The process of verifying this applicability for two maturity capsules: the model and SCRUM maturity capsules, showed that the design of ontologies for any IT project management method can be implemented through maturity capsule ontotriggers rather than designing new ontologies.

Framework for ontology evolution based on a multi-attribute alignment method

In this paper we want to investigate how to measure changes that occur when ontologies evolve in time. In the modern semantic online environments users cannot assume that initially created ontologies will remain static throughout the whole lifespan of particular application. Moreover, alignments originally established between such ontologies can become stale and invalid when certain changes have been applied to maintained ontologies. Therefore, such mappings should evolve in parallel. These issues raise some important questions: How to express changes introduced to ontologies? When the revalidation of alignments should occur? Is it necessary to relaunch an alignment procedure for the whole ontologies or is it possible to check and adjust only small fragments of mappings affected by applied modifications?

Increasing the Efficiency of Ontology Alignment by Tracking Changes in Ontology Evolution

In this paper we present a development of our ontology alignment framework based on varying semantics of attributes. Emphasising the analysis of explicitly given descriptions of how attributes change meanings they entail while being included within different concepts have been proved useful. Moreover, we claim that it is consistent with the intuitive way how people see the real world and how they find similarities and correspondences between its elements. In this paper we concentrate on the issue of tracking changes that may occur within aligned ontologies and how these potential changes can influence the process of finding new mappings or validating ones that have already been found.