Automatic ontology mapping for agent communication

International Journal of Communication Networks and Distributed Systems, 2013

Communication is the most important feature for meaningful interaction among agents in distributed multi-agent systems. Communication enables agent's interaction to achieve their goals. Agent communication languages provide a standard in the protocol and language used in the communication, but cannot provide a standard in ontology, because ontology depends on the subject and concept of the communication. This lack of standardisation is known as interoperability problem. In order to obtain semantic interoperability, agents need to agree on the basis of different ontologies. In this paper, a communication layer is proposed to outline the communication between agents, multiplatform communication system (MPCS) architecture is proposed to provide a highly flexible and scalable system. In addition a dynamic ontology mapping system for agent communication (DOMAC) is proposed based on different mapping approaches.

1994

Abstract In this paper, we discuss how ontology plays roles in building a distributed and heterogeneous knowledgebase system. First we discuss relationship between ontology and agent in the Knowledgeable Community which is a framework of knowledge sharing and reuse based on a multi-agent architecture. Ontology is a minimum requirement for each agent to join the Knowledgeable Community. Second we explain mediation by ontology to show how ontology is used in the Knowledgeable Community.

We propose an approach to achieve appropriate exchange of services and data in distributed systems subject to semantic heterogeneity. We assume differentiated ontologies: that terms have formal definitions as concepts related to other concepts, that local concepts inherit from concepts that are shared, and that most or all primitives are shared. We then develop measures of description compatibility using the structure of the source and target definitions. We evaluate these measures by generating description-logic ontologies in artificial worlds. In our simulations, the "meaning" of a concept is its denotation in a finite universe of instances. The accuracy of the description-compatibility measures can thus be judged by their success in predicting the overlap of concept denotations. Description compatibility can be used to guide agent search for services across communities that subscribe to differentiated ontologies. 2 Differentiated Ontologies To enable calculation of the compatibility measures, we make certain assumptions about the syntactic representation of terms to be mapped. Structures that satisfy these assumptions are differentiated ontologies. Assumption 1: Terms are defined as concepts in formal ontologies. Ontologies are sets of concept definitions. Formal ontologies use logic to define concepts by their relations to other concepts. For example, chair might be defined as a-kind-of furniture that a person can sit-upon. 1 The concept chair denotes the set of objects that are chairs; the instance chair001 denotes a particular chair object. The logic must, like description logic, support computational inference of subsumption (Woods 1991). Concept A subsumes B if and only if every instance of B is always an instance of A. Highchair, for example, might be defined as a-kind-of furniture that a toddler can sit-upon; by comparing the definitions for chair and highchair and person and toddler, respectively, description logic can deduce that highchair is a-kind-of chair. (See Weinstein and Birmingham (Weinstein and Birmingham 1998) for an expanded description of "formal ontology"; in this paper, Section 4 specifies the representation that we use in our simulations.) The structure of ontological definition provides a basis for syntactic comparison. The precision required by logic, and in particular the support for subsumption inference, is required to enable calculation of the compatibility measures. Whereas Assumption 1 is fundamental to our enterprise, the following assumptions are "soft" in that they can be violated, but increasing frequency of violation yields increasingly poor results for our description-compatibility measures. Assumption 2: Concepts inherit definitional structure from concepts that are shared. This assumption identifies the form of commonality that we use to enable automatic mapping. Note that mapping between concepts in differentiated ontologies is equivalent to mapping between concepts in a single ontology that provides separate spaces for concept names. Assumption 3: Primitive concepts and relations are shared. Non-primitive definitions have both necessary and sufficient conditions, but primitives have only necessary conditions. For example, given the primitive (C ⇒ D), we cannot infer that instance j ∈ C unless this is asserted explicitly. Clearly, there can be no basis for matching a primitive element of a concept definition to any element of a concept in an ontology where the primitive is undefined. We expect differentiated ontologies to occur in two important contexts. First, architects may design infrastructures for multi-agent systems that include one or more generic ontologies to which all agents are expected to subscribe. As systems change over time, developers will create local versions of ontologies that are specialized to help meet the needs of particular user communities. Over time, large ontological structures will develop, and communities of agents will be defined by the ontologies they use to communicate. Agents will interact with agents in their own community, but may use the techniques

2008

210 Abstract—The problem of ontology mapping is tackled in the context of MAS and real life ontologies, context that requires an increased degree of dynamism and the ability to handle high amounts of data. Our solution follows the guidelines of lazy mapping, striving to map only what is necessary and only when it is necessary by using lexical based mapping methods combined with proxy ontologies. However, we are not omitting from this endeavor the accuracy of mapping since our two layer process has FCA as the second mapping step.

Communication in open systems is hampered by the lack of standardization of the ontology used to describe information. This problem is known as the interoperability problem. Establishing a mapping between pairs of ontologies is a practical alternative for formulating standard ontologies. Of course, creating such a mapping by hand is not applicable in an open environment such as the Internet. Therefore, this paper proposes a domain independent method for learning a mapping between ontologies. The learning method is based on exchanging instances of concepts that are defined in the ontologies. The method starts with identifying pairs of instances of concepts that describe the same entity in the world, followed by proposing and evaluating mappings between the ontologies using the pairs of instances. For each step of this method, a likelihood measure has been derived and is used in the decision process. Important benefits of the method are that (a) no domain knowledge is required, and (b) the structures of ontologies between which a mapping must be established, play no role. Experimental results show that the proposed method gives high quality mappings between ontologies.

1995

Abstract Building ontology is one of key issues for knowledge sharing which is important technology to realize largescale knowledge-base systems. In this paper, we propose a computational framework for constructive ontologies called aspect. We first formalize aspect in a logical framework and then define it as a programming language.

Applied Ontology, 2007

This paper presents a formal ontology which intends to facilitate interoperability among agents belonging to software agent systems that use different agent communication languages. The followed design criteria for building the ontology is an elaboration on the "illocutionary force-plus-content" framework of speech acts theory. The ontology is specified in OWL. Reasons are presented through the paper why, in our opinion, an ontology based on speech acts theory and specified in a DL-based language is adequate for formal agent communication. The ontology is divided into three layers: upper, standards and applications, grouping classes by different levels of abstraction. The formalism used in the specification of classes allows automated reasoning for locating classes in the taxonomy and recognizing individuals of classes. global proposal). Classes of the COMMONT ontology are described using the Web Ontology Language OWL [3] (more specifically the OWL DL sublanguage). Therefore, communication acts among agents that commit to COMMONT have an abstract representation as individuals of a shared universal class of communication acts. Our approach is supported by reasoning services already existing in the Semantic Web area. We claim that COMMONT ontology provides interoperability support due to the recognition of communication acts stemming from one ACL as instances of communication acts in another ACL. Notice that COM-MONT manages abstract representations of messages that can be processed to be serialized as demanded by a normative syntax. Sometimes the comprehension of a communication act will not be complete, but partial comprehension of the communication may be useful and preferable to the "not understood" answer given nowadays (see section 5).

The Knowledge Engineering Review, 2002

It is now more than ten years since researchers in the US Knowledge Sharing Effort envisaged a future where complex systems could be built by combining knowledge and services from multiple knowledge bases and the first agent communication language, KQML, was proposed (Neches et al., 1991). This model of communication, based on speech acts, a declarative message content representation language and the use of explicit ontologies defining the domains of discourse (Genesereth & Ketchpel, 1994), has become widely recognised as having great benefits for the integration of disparate and distributed information sources to form an open, extensible and loosely coupled system. In particular, this idea has become a key tenet in the multi-agent systems research community.

2012

This paper describes the development of an model in area of communication between agents. This study involves the concepts of agents and communication issues in their interactions. Using the ontology as a technique of classification of knowledge, the model includes an approach to build an intermediate software component between agents and ontological knowledge base, using synonyms in treatment of inaccuracy information. The software component uses Jenaand SPARQL for manipulation of the ontology. To validation, the case study used is the model of the multi-agent system that represents the urban vegetable garden of Parque São JerÃ'nimo (Spain).

Expert Systems, 2008

Domain ontologies and knowledge-based systems have become very important in the agent and semantic web communities. As their use has increased, providing means of resolving semantic differences has also become very important. In this paper we survey the approaches that have been proposed for providing interoperability among domain ontologies. We also discuss some key issues that still need to be addressed if we were to move from semi to fully automated approaches to provide consensus among heterogeneous ontologies. .