-
Ergun Biçici.
Consensus Ontology Generation in a Socially Interacting MultiAgent System.
In 30th Annual International Computer Software and Applications Conf. (COMPSAC'06),
Chicago, IL, USA,
pages 279-284,
9 2006.
IEEE Computer Society.
Note: Presentation.
[PDF]
[doi:10.1109/COMPSAC.2006.126]
Keyword(s): Consensus Ontology.
Abstract: |
This paper presents an approach for building consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other’s performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent’s conceptualization in a given domain. As the expertise of agents change after a number of interactions, the consensus ontology that we build based on the agents’ individual views evolves. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies. |
@inproceedings{Bicici:COMPSAC2006,
author = {Ergun Bi\c{c}ici},
title = {Consensus Ontology Generation in a Socially Interacting MultiAgent System},
booktitle = {30th Annual International Computer Software and Applications Conf. (COMPSAC'06)},
year = {2006},
pages = {279--284},
address = {Chicago, IL, USA},
month = {9},
publisher = {IEEE Computer Society},
keywords = {Consensus Ontology},
pdf = {http://bicici.github.io/publications/2006/COMPSAC2006Extended.pdf},
doi = {10.1109/COMPSAC.2006.126},
abstract = {This paper presents an approach for building consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other’s performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent’s conceptualization in a given domain. As the expertise of agents change after a number of interactions, the consensus ontology that we build based on the agents’ individual views evolves. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies.},
note = {Presentation}
}
-
Ergun Biçici.
Generating Consensus Ontologies among Socially Interacting Agents.
In International Workshop on Agents and Multiagent Systems, from Theory to Application (AMTA 2006),
Québec City, Canada,
6 2006.
[PDF]
Keyword(s): Consensus Ontology.
Abstract: |
This paper presents an approach for building consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other’s performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent’s conceptualization in a given domain. As the expertise of agents change after a number of interactions, the consensus ontology that we build based on the agents’ individual views evolves. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies. |
@inproceedings{Bicici:AMTA2006,
author = {Ergun Bi\c{c}ici},
title = {Generating Consensus Ontologies among Socially Interacting Agents},
booktitle = {International Workshop on Agents and Multiagent Systems, from Theory to Application (AMTA 2006)},
year = {2006},
address = {Qu\'ebec City, Canada},
month = {6},
type = {workshop},
keywords = {Consensus Ontology},
pdf = {http://bicici.github.io/publications/2006/AMTA2006Extended.pdf},
abstract = {This paper presents an approach for building consensus ontologies from the individual ontologies of a network of socially interacting agents. Each agent has its own conceptualization of the world. The interactions between agents are modeled by sending queries and receiving responses and later assessing each other’s performance based on the results. This model enables us to measure the quality of the societal beliefs in the resources which we represent as the expertise in each domain. The dynamic nature of our system allows us to model the emergence of consensus that mimics the evolution of language. We present an algorithm for generating the consensus ontologies which makes use of the authoritative agent’s conceptualization in a given domain. As the expertise of agents change after a number of interactions, the consensus ontology that we build based on the agents’ individual views evolves. We evaluate the consensus ontologies by using different heuristic measures of similarity based on the component ontologies.},
}
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Ergun Biçici and Deniz Yuret.
Clustering Word Pairs to Answer Analogy Questions.
In Fifteenth Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN '06),
Akyaka, Mugla,
pages 277-284,
6 2006.
Note: Presentation.
[PDF]
Keyword(s): Natural Language Processing.
Abstract: |
We focus on answering word analogy questions by using clustering techniques. The increased performance in answering word similarity questions can have many possible applications, including question answering and information retrieval. We present an analysis of clustering algorithms’ performance on answering word similarity questions. This paper’s contributions can be summarized as: (i) casting the problem of solving word analogy questions as an instance of learning clusterings of data and measuring the effectiveness of prominent clustering techniques in learning semantic relations; (ii) devising a heuristic approach to combine the results of different clusterings for the purpose of distinctly separating word pair semantics; (iii) answering SAT-type word similarity questions using our technique. |
@InProceedings{BiciciTAINN06,
title = {Clustering Word Pairs to Answer Analogy Questions},
author = {Ergun Bi\c{c}ici and Deniz Yuret},
year = {2006},
booktitle = {Fifteenth Turkish Symposium on Artificial Intelligence and Neural Networks (TAINN '06)},
pages = {277--284},
year = {2006},
month = {6},
address = {Akyaka, Mugla},
keywords = {Natural Language Processing},
pdf = {http://bicici.github.io/publications/2006/LAWSQ-LNCS.pdf},
abstract = {We focus on answering word analogy questions by using clustering techniques. The increased performance in answering word similarity questions can have many possible applications, including question answering and information retrieval. We present an analysis of clustering algorithms’ performance on answering word similarity questions. This paper’s contributions can be summarized as: (i) casting the problem of solving word analogy questions as an instance of learning clusterings of data and measuring the effectiveness of prominent clustering techniques in learning semantic relations; (ii) devising a heuristic approach to combine the results of different clusterings for the purpose of distinctly separating word pair semantics; (iii) answering SAT-type word similarity questions using our technique.},
note = {Presentation}
}