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Abstract
Introduction
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Computer Science
Distributed Systems

Integrating Ontologies in Mobile Agents

Profile image of Emanuela MerelliEmanuela Merelli

2005

Abstract

The process of information extraction and data integration in a global information system demands automatic techniques for quickly determining semantic similarity among concepts across different ontologies. This paper presents a graph based approach for computing, on-the-fly, semantic similarities among ontologies of a specific domain. The approach consists of integrating mobile agents and ontologies to support a variety of applications in distributed environments. The resulting technique is illustrated on Hermes, agent-based middleware for mobile computing, by an example in molecular biology domain.

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An ontology-based software agent system case study
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Proceedings ITCC 2003. International Conference on Information Technology: Coding and Computing, 2003

Developing a knowledge-sharing capability across distributed heterogeneous data sources remains a significant challenge. Ontology-based approaches to this problem show promise by resolving heterogeneity, if the participating data owners agree to use a common ontology (i.e., a set of common attributes). Such common ontologies offer the capability to work with distributed data as if it were located in a central repository. This knowledge sharing may be achieved by determining the intersection of similar concepts from across various heterogeneous systems. However, if information is sought from a subset of the participating data sources, there may be concepts common to the subset that are not included in the full common ontology, and therefore are unavailable for knowledge sharing. One way to solve this problem is to construct a series of ontologies, one for each possible combination of data sources. In this way, no concepts are lost, but the number of possible subsets is prohibitively large. This paper describes a software agent case study that demonstrates a flexible and dynamic approach for the fusion of data across combinations of participating heterogeneous data sources to maximize knowledge sharing. The software agents generate the largest intersection of shared data across any selected subset of data sources. This ontology-based agent approach maximizes knowledge sharing by dynamically generating common ontologies over the data sources of interest. The approach was validated using data provided by five (disparate) national laboratories by defining a local ontology for each laboratory (i.e., data source). In this experiment, the ontologies are used to specify how to format the data using XML to make it suitable for query. Consequently, software agents are empowered to provide the ability to dynamically form local ontologies from the data sources. In this way, the cost of developing these ontologies is reduced while providing the broadest possible access to available data sources.

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Ontology Engineering Group, Departamento de Inteligencia Artificial
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Abstract. Information coming from sensor networks is being increasingly used in a variety of systems (decision support systems, information portals, etc), normally combined with information coming from more traditional sources (e.g., relational databases, web documents, etc). However, existing ontologybased information integration approaches cannot be easily used for this combination task since they are mainly focused on the integration of information coming from these traditional sources, and do not support sensor network data. In this paper we make a first step towards enabling the inclusion of sensor network data into these integration approaches, with the automatic generation of data wrapping ontologies for sensor networks. Our approach extends existing ones used for extracting data wrapping ontologies from relational databases, using the schema of sensor network queries and external ontology search and relation discovery services.

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Alternative Approaches for Ontology Matching
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International Journal of Computer Applications, 2012

Ontology matching is generally defined as the process of finding correspondences between entities of different ontologies. It can help the data integration between autonomous agents, web services composition, and P2P information sharing. This process is applied through the use of ontology matching tools which use one or more ontology matching techniques. This paper presents tools which have been published in this field, such as Prompt [7], Smatch [5] and Ontobuilder [16]. Moreover the paper illustrates the drawbacks of these tools. New two tools are proposed to handle these drawbacks. The new proposed and other tools are tested using GlycO [8]and EnzyO[10] in the biochemistry field, Osteoarthritis and Rheumatoid in the medical field.

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150. Finding the Most Similar Concepts In Two Different Ontologies
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Lecture Notes in Artificial Intelligence LNAI 2972, 2004

A concise manner to send information from agent A to B is to use phrases constructed with the concepts of A: to use the concepts as the atomic tokens to be transmitted. Unfortunately, tokens from A are not understood by (they do not map into) the ontology of B, since in general each ontology has its own address space. Instead, A and B need to use a common communication language, such as English: the transmission tokens are English words. An algorithm is presented that finds the concept cB in OB (the ontology of B) most closely resembling a given concept cA. That is, given a concept from ontology OA, a method is provided to find the most similar concept in OB, as well as the similarity sim between both concepts. Examples are given.""""

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Combining Semantic Web technologies with Multi-Agent Systems for integrated access to biological resources
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Journal of Biomedical Informatics, 2008

The increasing volume and diversity of information in biomedical research is demanding new approaches for data integration in this domain. Semantic Web technologies and applications can leverage the potential of biomedical information integration and discovery, facing the problem of semantic heterogeneity of biomedical information sources. In such an environment, agent technology can assist users in discovering and invoking the services available on the Internet. In this paper we present SEMMAS, an ontology-based, domain-independent framework for seamlessly integrating Intelligent Agents and Semantic Web Services. Our approach is backed with a proof-of-concept implementation where the breakthrough and efficiency of integrating disparate biomedical information sources have been tested.

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Towards semantics-based ontology similarity
Sofia Pinto

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Abstract. As the Semantic Web emerges the problem of semantic heterogeneity is becoming more acute. Ontology matching techniques aim at tackling this problem by establishing correspondences between elements of the ontologies. These techniques rely on distance metrics, often called (dis) similarity measures, to assess the similarity of elements within the ontologies. Most of these approaches are either terminological, structural and/or extensional.

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Keywords: ontology, semantics, semantic web, agent systems, RDF
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2011

The development of next-generation Internet content services is predicated on the ability to process information automatically on a semantically-rich level. This requires design of semantic languages, domain ontologies written in semantic languages, information described with these ontologies, and agents for exploiting the information. This paper explores the latter two requirements. We describe the basic concept of ontology (as used in informatics), summarize research directions in this area. In the second part of the paper, we present and discuss our initial design of a travel-related ontology for a software agent system and illustrate its RDF-based implementation. 1.

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A Semantic Similarity Measure for the SIMS Framework
Pierluca Sangiorgi, Nunzio Ingraffia, Roberto Pirrone

Lecture Notes in Computer Science, 2008

The amount of currently available digital information grows rapidly. Relevant information is often spread over different information sources. An efficient and flexible framework to allow users to satisfy effectively their information needs is required. The work presented in this paper describes SIMS (Semantic Information Management System), a reference architecture for a framework performing semantic annotation, search and retrieval of information from multiple sources. The work presented in this paper focuses on a specific SIMS module, the SIMS Semantic Content Navigator, proposing an algorithm and the related implementation to calculate a semantic similarity measure inside an OWL ontology. This measure is based on ontology structure and on the information provided by attributes and relations that are defined inside the ontology. This work is the result of a collaborative effort between the DINFO (Department of Computer Science and Engineering) and the research team of Engineering -Ingegneria Informatica.

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Automatic ontology mapping for agent communication
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Proceedings of the first international joint conference on Autonomous agents and multiagent systems part 2 - AAMAS '02, 2002

Agent communication languages such as ACL and KQML provide a standard for agent communication. These languages enable an agent to specify the intention and the content of a message as well as the protocol, the language, and the ontology that are used. For the protocol and the language some standards are available and should be known by the communicating agents.

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DIA: DATA INTEGRATION USING AGENTS
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The classic problem of information integration has been addressed for a long time. The Semantic Web project is aiming to define an infrastructure that enables machine understanding. This is a vision that tackles the problem of semantic heterogeneity by using ontologies for information sharing. Agents have an important role in this infrastructure. In this paper we present a new solution, known as DIA (Data Integration using Agents), for semantic integration using mobile agents and ontologies.

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Related topics

  • Computer Science
  • Ontology
  • Molecular Biology
  • Semantic similarity
  • Global Information Systems
  • Data Integrity
  • Mobile Agent
  • Mobile Computer
  • Distributed Environment
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