Semantic Alignment of Context-Goal Ontologies

Several distributed systems need to inter-operate and exchange information. Ontologies are gained the popularity in AI community as a means for enriching the description of information and make their context more explicit. Thus, to enable Interoperability between systems, it is necessary to align ontologies describing them in a sound manner. Our main interest is focused on ontologies describing systems functionalities. We treat these lasts as goals to achieve. In general, a goal is related to the realization of an action in a particular context. Therefore, we call ontologies describing goals and their context Context-Goal 4 Ontologies. Most of the methodologies proposed to reach interoperability are semi automatic, they are based on probabilities or statistics and not on mathematical models. The purpose of this paper is to investigate an approach where the alignment of C-G Ontologies achieves an automatic and semantic interoperability between distributed systems based on a mathematical model "Information Flow". 1 Background and Outlines The exponential growth of information and resources exchanged between different systems increases the rate of heterogeneous information and makes their understanding and analysis very difficult. A crucial problem arising from this heterogeneity concerns the preservation of the meaning (sense) of the exchanged information. This is what we call the Semantic Interoperability. A definition is commonly accepted for semantic interoperability: it gives meaning to the exchanged information and ensures that this sense is common in all systems between which exchanges must be done [9] [18]. Therefore, distributed systems may combine the received information with the local one and treat the whole in a consistent way. To ensure semantic interoperability, the exchanged information between systems must be described in a formal structure preserving its semantics. The great challenge is omnipresent in the knowledge Engineering field, where the proposed methodologies and techniques collect, identify, analyze, organize and share knowledge between different organizations. Among these techniques, ontologies are gained the popularity in AI community as a means for enriching the description of information and make their context more explicit. They represent an efficient and promising way to implement this is through the use of Ontologies, an explicit specification of conceptualization [8]. The semantic interoperability needs the use of methodologies which establish semantically links between the services provided by the communicating entities of the distributed system. In literature, discovering these links is called ontologies alignment , it aims to 1 University of LARBI-BEN-MHIDI (Algeria) and University of Savoie (FRANCE), email: nacima.mellal@univ-savoie.fr 2 University of Savoie, France 3 University of Savoie, France 4 we abbreviate Context-Goal as C-G find connections between concepts belonging to different ontologies within a single application. After a careful look at the different theories related to these topics, such as the MAFRA framework developed for the mapping of distributed ontologies [14]. Using the Bridge notion, MAFRA allows to create semantic relations between two (source and target) ontologies and apply such relations in translating source ontology instances into target ontology instances. GLUE is another framework. It is a system that employs learning techniques to semi-automatically create semantic mappings between ontologies[7]. PROMPT is a tool for merging ontologies. In [11], the approach mainly builds on the IF-Map method to map ontologies in the domain of computer science departments from five UK universities. Their method is also complemented by harvesting mechanisms for acquiring ontologies, translators for processing different ontology representation formalisms and APIs for web enabled access of the generated mappings. In [16], first-order model theory are investigated to formalize and automatize the issues arising with semantic interoperability for which they focused on particular understandings of semantics. But it would be desirable to provide a theoretical framework that accommodates various different understandings of semantics depending on the semantic interoperability. Authors made the first step towards semantic integration by proposing a mathematically sound application of channel theory to enable semantic interoperability of separate ontologies representing similar domains. In general, these works present some insufficiencies. They are semi-automatic (MAFRA, IF-MAP), or centered on probabilities (GLUE) or based on syntactic similarities (PROMPT). For that, it is useful to develop applications in order to automat the ontology alignment. In the context of distributed systems, the communicating subsystems differ, generally, by a set of services that each one provides to the other, in order to realize global goals. These goals are directly related to the provided services : accomplish a service is done with an objective and achieve a goal is also performing a particular service. In this spirit, several researches are interested in goal notion and in its representation [5], [19], [10], [17] and [2]. These works have proved the effectiveness of system modeling using goals. For this reason, we are interested in our research to the goal oriented modeling. The majority of these works are centered on the description of the functionalities of systems and their components. They have denoted a functionality of a component as a “ verb+noun ” style for representing the component’s activities (or actions) and its operands which needs an ontological schema for functional knowledge which specifies not only the data structure but also the conceptual viewpoint for capturing the target world [12]. Following this approach, we associate with each goal some possible actions (at least one) in order to fulfill the intended goal. At this level, there is a lack of formal terminology [13]. To remedy this problem and represent goal semantics formally, our approach uses the linguistical aspect based on the principle of linking names to verbs via lexical relations. For example, the structure associated with the name Temperature can contain verbs: to measure, to reduce, to increase . These verbs express the intention or the function related to this name. More precisely, if we analyze two goals "to measure the temperature of a room " or "to measure the temperature of a human being ", we note that the action is always the same but its context is different. This brings us to introduce the concept Context of a goal. Our work is focused around two issues that are, on the one hand, the representation and reasoning on goals and, on the other hand, the resolution of semantic interoperability. The present paper is divided into five sections. In the first one, we present a short background on the existing researches in our domain. The second section introduces a case study to illustrate our approach. In the third section, the C-G ontology construction is defined. The fourth section presents the appropriate part of IF model. The fifth section is centered on the principle of the C-G ontologies alignment.