Finding the Most Similar Concepts in Two Different Ontologies

Guzmán-Arenas, Adolfo; Olivares-Ceja, Jesús-Manuel

doi:10.1007/978-3-540-24694-7_14

Cited by 7 publications

(1 citation statement)

References 9 publications

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“…Each concept is characterized by a set of feature/value pairs. To measure how similar O A is to O B , we first need to know foreach concept C A in O A the most similar concept C B in O B .This can be done by dealing with feature/value pairs, parent and grandparent of concept C A[24] and compare it to all concepts Cs in ontology O B . Ontology O B tries to find the most similar concept C B to the concept C A .…”

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confidence: 99%

Calculating the strength of ties of a social network in a semantic search system using hidden Markov models

El-Sherif

Eberlein

Far

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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The Web of information has grown to millions of independently evolved decentralized information repositories. Decentralization of the web has advantages such as no single point of failure and improved scalability. Decentralization introduces challenges such as ontological, communication and negotiation complexity. This has given rise to research to enhance the infrastructure of the Web by adding semantic to the search systems. In this research we view semantic search as an enabling technique for the general Knowledge Management (KM) solutions. We argue that, semantic integration, semantic search and agent technology are fundamental components of an efficient KM solution. This research aims to deliver a proofof-concept for semantic search. A prototype agent-based semantic search system supported by ontological concept learning and contents annotation is developed. In this prototype, software agents, deploy ontologies to organize contents in their corresponding repositories; improve their own search capability by finding relevant peers and learn new concepts from each other; conduct search on behalf of and deliver customized results to the users; and encapsulate complexity of search and concept learning process from the users. A unique feature of this system is that the semantic search agents form a social network. We use Hidden Markov Model (HMM) to calculate the tie strengths between agents and their corresponding ontologies. The query will be forwarded to those agents with stronger ties and relevant documents are returned. We have shown that this will improve the search quality. In this paper, we illustrate the factors that affect the strength of the ties and how these factors can be used by HMM to calculate the overall tie strength.

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confidence: 99%

Calculating the strength of ties of a social network in a semantic search system using hidden Markov models

El-Sherif

Eberlein

Far

2011

2011 IEEE International Conference on Systems, Man, and Cybernetics

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Graduated Errors in Approximate Queries Using Hierarchies and Ordered Sets

Guzmán-Arenas

Levachkine

2004

MICAI 2004: Advances in Artificial Intelligence

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Abstract. Often, qualitative values have an ordering, such as (veryshort, short, medium-height, tall) or a hierarchical level, such as (TheWorld, Europe, Spain, Madrid), which are used by people to interpret mistakes and approximations among these values. Confusing Paris with Madrid yields an error smaller than confusing Paris with Australia, or Paris with Abraham Lincoln. And the "difference" between very cold and cold is smaller than that between very cold and warm.Methods are provided to measure such confusion, and to answer approximate queries in an "intuitive" manner. Examples are given. Hierarchies are a simpler version of ontologies, albeit very useful.Queries have a blend of errors by order and errors by hierarchy level, such as "what is the error in confusing very cold with tall?" or "give me all people who are somewhat like (John (plays baseball) (travels-by water-vehicle) (lives-in North-America))." Thus, retrieval of approximate objects is possible, as illustrated here.

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Concept Similarity Measures the Understanding Between Two Agents

Olivares-Ceja

Guzmán-Arenas

2004

Natural Language Processing and Information Systems

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Finding the Most Similar Concepts in Two Different Ontologies

Cited by 7 publications

References 9 publications

Calculating the strength of ties of a social network in a semantic search system using hidden Markov models

Calculating the strength of ties of a social network in a semantic search system using hidden Markov models

Graduated Errors in Approximate Queries Using Hierarchies and Ordered Sets

Concept Similarity Measures the Understanding Between Two Agents

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