Subsumption Relation

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V. Mascardi - One of the best experts on this subject based on the ideXlab platform.

  • CooL-AgentSpeak: Endowing AgentSpeak-DL Agents with Plan Exchange and Ontology Services
    2014
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, M. Barbieri, A. Ricci
    Abstract:

    In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for an ontologically relevant plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating also take advantage of ontological reasoning and matching

  • CooL-AgentSpeak: Enhancing AgentSpeak-DL Agents with Plan Exchange and Ontology Services
    'Institute of Electrical and Electronics Engineers (IEEE)', 2011
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, A. Ricci
    Abstract:

    In this paper we present CooL-Agent Speak, an extension of Agent Speak-DL with plan exchange and ontology services. In CooL-Agent Speak, the search for a plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating take advantage of ontological reasoning and matching as well

  • Cool-agentspeak: Enhancing agentspeak-dl agents with plan exchange and ontology services
    2011
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, Ro Ricci
    Abstract:

    Abstract—In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for a plan is no longer limited to the agent’s local plan library but is carried out in the other agents ’ libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating take advantage of ontological reasoning and matching as well. Keywords-AgentSpeak, cooperation, plan exchange, ontology matching I

A. Ricci - One of the best experts on this subject based on the ideXlab platform.

  • CooL-AgentSpeak: Endowing AgentSpeak-DL Agents with Plan Exchange and Ontology Services
    2014
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, M. Barbieri, A. Ricci
    Abstract:

    In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for an ontologically relevant plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating also take advantage of ontological reasoning and matching

  • CooL-AgentSpeak: Enhancing AgentSpeak-DL Agents with Plan Exchange and Ontology Services
    'Institute of Electrical and Electronics Engineers (IEEE)', 2011
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, A. Ricci
    Abstract:

    In this paper we present CooL-Agent Speak, an extension of Agent Speak-DL with plan exchange and ontology services. In CooL-Agent Speak, the search for a plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating take advantage of ontological reasoning and matching as well

Deepak P - One of the best experts on this subject based on the ideXlab platform.

  • Learning Concept Hierarchies through Probabilistic Topic Modeling
    arXiv: Artificial Intelligence, 2016
    Co-Authors: V S Anoop, S Asharaf, Deepak P
    Abstract:

    With the advent of semantic web, various tools and techniques have been introduced for presenting and organizing knowledge. Concept hierarchies are one such technique which gained significant attention due to its usefulness in creating domain ontologies that are considered as an integral part of semantic web. Automated concept hierarchy learning algorithms focus on extracting relevant concepts from unstructured text corpus and connect them together by identifying some potential Relations exist between them. In this paper, we propose a novel approach for identifying relevant concepts from plain text and then learns hierarchy of concepts by exploiting Subsumption Relation between them. To start with, we model topics using a probabilistic topic model and then make use of some lightweight linguistic process to extract semantically rich concepts. Then we connect concepts by identifying an "is-a" Relationship between pair of concepts. The proposed method is completely unsupervised and there is no need for a domain specific training corpus for concept extraction and learning. Experiments on large and real-world text corpora such as BBC News dataset and Reuters News corpus shows that the proposed method outperforms some of the existing methods for concept extraction and efficient concept hierarchy learning is possible if the overall task is guided by a probabilistic topic modeling algorithm.

V S Anoop - One of the best experts on this subject based on the ideXlab platform.

  • Learning Concept Hierarchies through Probabilistic Topic Modeling
    arXiv: Artificial Intelligence, 2016
    Co-Authors: V S Anoop, S Asharaf, Deepak P
    Abstract:

    With the advent of semantic web, various tools and techniques have been introduced for presenting and organizing knowledge. Concept hierarchies are one such technique which gained significant attention due to its usefulness in creating domain ontologies that are considered as an integral part of semantic web. Automated concept hierarchy learning algorithms focus on extracting relevant concepts from unstructured text corpus and connect them together by identifying some potential Relations exist between them. In this paper, we propose a novel approach for identifying relevant concepts from plain text and then learns hierarchy of concepts by exploiting Subsumption Relation between them. To start with, we model topics using a probabilistic topic model and then make use of some lightweight linguistic process to extract semantically rich concepts. Then we connect concepts by identifying an "is-a" Relationship between pair of concepts. The proposed method is completely unsupervised and there is no need for a domain specific training corpus for concept extraction and learning. Experiments on large and real-world text corpora such as BBC News dataset and Reuters News corpus shows that the proposed method outperforms some of the existing methods for concept extraction and efficient concept hierarchy learning is possible if the overall task is guided by a probabilistic topic modeling algorithm.

  • unsupervised concept hierarchy learning a topic modeling guided approach
    Procedia Computer Science, 2016
    Co-Authors: V S Anoop, S Asharaf, P Deepak
    Abstract:

    Abstract This paper proposes an efficient and scalable method for concept extraction and concept hierarchy learning from large unstructured text corpus which is guided by a topic modeling process. The method leverages “concepts” from statistically discovered “topics” and then learns a hierarchy of those concepts by exploiting a Subsumption Relation between them. Advantage of the proposed method is that the entire process falls under the unsupervised learning paradigm thus the use of a domain specific training corpus can be eliminated. Given a massive collection of text documents, the method maps topics to concepts by some lightweight statistical and linguistic processes and then probabilistically learns the Subsumption hierarchy. Extensive experiments with large text corpora such as BBC News dataset and Reuters News corpus shows that our proposed method outperforms some of the existing methods for concept extraction and efficient concept hierarchy learning is possible if the overall task is guided by a topic modeling process.

R. H. Bordini - One of the best experts on this subject based on the ideXlab platform.

  • CooL-AgentSpeak: Endowing AgentSpeak-DL Agents with Plan Exchange and Ontology Services
    2014
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, M. Barbieri, A. Ricci
    Abstract:

    In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for an ontologically relevant plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating also take advantage of ontological reasoning and matching

  • CooL-AgentSpeak: Enhancing AgentSpeak-DL Agents with Plan Exchange and Ontology Services
    'Institute of Electrical and Electronics Engineers (IEEE)', 2011
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, A. Ricci
    Abstract:

    In this paper we present CooL-Agent Speak, an extension of Agent Speak-DL with plan exchange and ontology services. In CooL-Agent Speak, the search for a plan is no longer limited to the agent's local plan library but is carried out in the other agents' libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating take advantage of ontological reasoning and matching as well

  • Cool-agentspeak: Enhancing agentspeak-dl agents with plan exchange and ontology services
    2011
    Co-Authors: V. Mascardi, D. Ancona, R. H. Bordini, Ro Ricci
    Abstract:

    Abstract—In this paper we present CooL-AgentSpeak, an extension of AgentSpeak-DL with plan exchange and ontology services. In CooL-AgentSpeak, the search for a plan is no longer limited to the agent’s local plan library but is carried out in the other agents ’ libraries too, according to a cooperation strategy, and it is not based solely on unification and on the Subsumption Relation between concepts, but also on ontology matching. Belief querying and updating take advantage of ontological reasoning and matching as well. Keywords-AgentSpeak, cooperation, plan exchange, ontology matching I

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