Integrating Ontologies in Mobile Agents (original) (raw)

2008

In this paper, we present a hybrid similarity matching algorithm i.e. Semantic Relatedness Score (SRS) that is used to match ontological concepts and instances in the context of ontology evolution. We combine five out of thirteen well-tested semantic and syntactic algorithms to produce SRS. Specifically we focus on the issue of ontology upgrade and highlight how our hybrid matching algorithm produces higher precision and reliability compared to existing syntactical approaches. Managing evolution amongst shared ontologies is a laborious affair.

Towards a new ontology matching system through a multi-agent architecture

This paper presents a new method of ontology matching to improve semantic interoperability. This method takes as input ontologies described in XML, RDF Schema and OWL format. The proposed matching process involves several stages through the analysis of ontologies entities sources, calculates the terminological similarity with several matchers to maximize the discovery of many similar couples. Once the mapping hypotheses are generated, a filtering system is in place to ensure the quality of alignments. The system architecture is based on a multi agent system, each agent has its own behavior and communicates with the common environment to produce mappings between ontologies source.

An ontology-driven similarity algorithm

2004

Abstract. This paper presents our similarity algorithm between relations in a user query written in FOL (first order logic) and ontological relations. Our similarity algorithm takes two graphs and produces a mapping between elements of the two graphs (ie graphs associated to the query, a subsection of ontology relevant to the query). The algorithm assesses structural similarity and concept similarity. An evaluation of our algorithm using the KMi Planet ontology 1 is presented.

Computation of Ontology Resemblance Coefficients for Improving Semantic Interoperability

In open and dynamic environments, where various heterogeneous agents need to communicate, a shared ontology that explicitly and formally describes the whole domain of interest, or an alignment that provides semantically related entities among distinct ontologies, can be employed. The former case is infeasible, because a unique conceptual view of a domain is not widely accepted. Hence, the case, usually adopted, is the latter, where independently developed heterogeneous ontologies exist. A challenging issue is how an agent, charged with the task of carrying out the alignment, should select a suitable execution of matchers, to establish correspondences between ontology entities, in the fastest and most efficient way. A solution to this challenge is to use metrics for estimating the resemblance of a given pair of ontologies. To this end, we propose two metrics, as similarity coefficients, to estimate the lexical or structural resemblance of a given pair of ontologies.

Descriptive information is easy to understand and communicate in natural language. Examples in the biological realm include the cellular functions of proteins and the phenotypes exhibited by organisms. Large latent stores of such descriptive data are stored in databases that can be mined, but even more still reside only in the scientific literature. Although such information has traditionally been opaque to computers, in recent years significant efforts have gone into exposing descriptive information to computation through the development of ontologies and associated tools. A host of software applications now employ simple reasoning over Gene Ontology annotated data to help interpret experimental findings in genomics in terms of protein function. In the domain of biological phenotypes, the combination of entity terms from taxonspecific anatomy ontologies with quality terms from generic ontologies such as PATO have been used to construct semantically precise and contextualized descriptions. It is natural for multiple semantic descriptions to pertain to single instances in the real world, as in the case of both protein functions and organismal phenotypes. However, applications for ontology-based annotations that go beyond simple knowledge organization, and that exploit sets of semantic descriptions, are puzzlingly rare. In particular, we argue that there is wide applicability, and a sore need, for tools that can satisfy the simple, common use case of identifying statistically improbable similarity between sets of semantic descriptions. Several metrics have been proposed for this task in the literature, but not yet fully evaluated, explored, and adopted. The requirements for semantic similarity tools tailored to sets of semantic descriptions would include speed, scalability to large numbers of sets, demonstrated statistical and biological validity, and ease of use.

An ontology-based software agent system case study

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.

Ontology Engineering Group, Departamento de Inteligencia Artificial

2010

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.

Alternative Approaches for Ontology Matching

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.

150. Finding the Most Similar Concepts In Two Different Ontologies

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.""""

Combining Semantic Web technologies with Multi-Agent Systems for integrated access to biological resources

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.

Integrating Ontologies in Mobile Agents (original) (raw)

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