Ontology Co-construction with an Adaptive Multi-Agent System: Principles and Case-Study (original) (raw)

Dynamic Ontology Co-construction based on Adaptive Multi-Agent Technology

2009

Ontologies have become an important means for structuring knowledge and defining semantic information retrieval systems. Ontology engineering requires a significant effort, and recent researches show that human language technologies are useful means to acquire or update ontologies from text. In this paper we present DYNAMO, a tool based on a Multi-Agent System, which aims at assisting ontologists during the ontology building and evolution processes. This work is carried out in the context of the DYNAMO project. The main novelty of the agent system is to take advantage of text extracted terms and lexical relations together with some quantitative features of the corpus to guide the agents when self-organizing. We exhibit the first experiment of ontology building that shows promising results, and helps us to identify key issues to be solved to the DYNAMO system behavior and the resulting ontology.

Dynamic Ontology Co-Evolution from Texts: Principles and Case Study

2007

As claimed in the Semantic Web project, a huge amount of physically distributed interacting software agents could find the semantic of available resources and answer more relevantly to users' requests if the content of these resources would be represented with formal semantic concepts defined in ontologies. Because Web information sources are highly dynamic and conceptually heterogeneous, one of the most challenging problems in the Semantic Web research is the proper and frequent ontology updating in keeping with knowledge changes. To tackle this problem, we have developed a selforganizing multi-agent system -Dynamo-able to create an ontology draft from automatic text processing. Because it is well-known that only a part of a domain description is explicitly described in texts, Dynamo enables an ontology coconstruction with a domain expert in a fully interactive way. In this paper, we present the principles of this approach and related experiments.

DYNAMO, un outil de construction et d’évolution d’ontologies à partir de textes

Techniques et sciences informatiques, 2012

Ontologies have become an important means for structuring knowledge and defining semantic information retrieval systems. Ontology engineering requires a significant effort, and recent researches show that human language technologies are useful means to acquire or update ontologies from text. In this paper we present DYNAMO, a tool based on a Multi-Agent System, which aims at assisting ontologists during the ontology building and evolution processes. This work is carried out in the context of the DYNAMO project. The main novelty of the agent system is to take advantage of text extracted terms and lexical relations together with some quantitative features of the corpus to guide the agents when self-organizing. We exhibit the first experiment of ontology building that shows promising results, and helps us to identify key issues to be solved to the DYNAMO system behavior and the resulting ontology.

Agent-based approach for building ontology from text

An ontology is an explicit specification of a conceptualization, the term is often linked with the Semantic Web, ontologies are used like representations of knowledge, to annotate web resources and also to communication between systems. This make them very important, but unfortunately their construction is expensive, and because they are representations of knowledge, we thought of using the enormous amount of information available under textual format to automate the process of ontology building, and since we deal with texts, the NLP (Natural Language Processing) is considered as the base for the ontology construction from text. In this paper our goal is to propose an agent-based approach to build ontology from text, and implement a multi-agent system guided by this approach, which start from a set of textual resources to give us an ontology in OWL (Ontology Web Language), using the Formal Concept Analysis FCA and Relational Concept Analysis RCA to move from the syntactic level to t...

A Multi-Agent System for Dynamic Ontologies

Journal of Logic and Computation, 2009

In the article, we present Dynamo (an acronym of DYNAMic Ontologies), a tool based on an adaptive multi-agent system to construct and maintain an ontology from a domain specific set of texts. The originality of our proposal is that the adaptative multi-agent system is used both to represent the ontology itself and to produce the ontology. This enables us to propose a system building and maintaining dynamically an ontology according to interactions with the user (also called the ontologist). We present our system and the mechanisms used to build and maintain the ontology from the texts and for the interactions with the ontologist. We also give results of the evaluation of our system.

An Agent-Based Approach to Dynamic Ontology Construction

1 Abstract—The building of exhaustive ontologies leads to well known problems such as terminology, scope, encoding and context, which can only be resolved in a process of intense communication of the potential users. We propose an environment that enables users to define rules, parameters, constraints for an agent-based system which sustains (self-) organization of small sets of concepts extracted from a specific set of user provided documents and their relations. The system allows users to build or train agents, which carry small ontologies together with specific sample documents, and a generic set of rules, which enables the agents to negotiate their local ontological relations with each other.

Automated ontology evolution in a multi-agent system

Proceedings of the 1st international conference on Scalable information systems - InfoScale '06, 2006

This research addresses the formation of new concepts and their corresponding ontology in a multiagent system where individual autonomous agents try to learn new concepts by consulting several other agents. In this research individual agents create and learn their distinct conceptualization and rather than a commitment to a common ontology they use their own ontologies. In this paper multi-agent supervised learning of concepts among individual agents with diverse conceptualization and different ontologies is introduced and demonstrated through an intuitive example in which supervisors are other agents rather than a human.

An intelligent system for semi-automatic evolution of ontologies

2010 5th IEEE International Conference Intelligent Systems, 2010

Ontologies are an important part of the Semantic Web as well as of many intelligent systems. However, the traditional expert-driven development of ontologies is time-consuming and often results in incomplete and inappropriate ontologies. In addition, since ontology evolution is not controlled by end users, it may take too long for a conceptual change in the domain to be reflected in the ontology. In this paper, we present a recommendation algorithm in a Web 2.0 platform that supports end users to collaboratively evolve ontologies by suggesting semantic relations between new and existing concepts. We use the Wikipedia category hierarchy to evaluate our algorithm and our experimental results show that the proposed algorithm produces high quality recommendations.

Semi-Automatic Ontology Development: Processes and Resources

2012

ABStRAct The collection of the specialized vocabulary of a particular domain (terminology) is an important initial step of creating formalized domain knowledge representations (ontologies). Terminology Extraction (TE) aims at automating this process by collecting the relevant domain vocabulary from existing lexical resources or collections of domain texts.