Ontology Evolution with Evolva

Zablith, Fouad; Sabou, Marta; d’Aquin, Mathieu; Motta, Enrico

doi:10.1007/978-3-642-02121-3_80

Cited by 26 publications

(19 citation statements)

References 7 publications

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“…It combines semantic relatedness measures, automatic pattern acquisition techniques, named entity extraction algorithm (GATE), and NLP techniques to extract missing knowledge from the Web. Zablith et al in [7,8] present an ontology evolution framework, called Evolva, that uses structured and unstructured sources as background knowledge to reduce or even eliminate user involvement in exploring new concepts to add to an ontology. Additionally, it uses external sources such as WordNet and Semantic Web ontologies to discover relations between new concepts and others already exist in the ontology.…”

Section: Related Workmentioning

confidence: 99%

“…Ontology evolution means modifying or upgrading the ontology when there is a certain need for change or there comes a change in the domain knowledge [1]. The current ontology evolution techniques such as in [5][6][7][8][9][10][19][20][21][22][23] have several hidden weaknesses such as automating ontology evolution and resolving inconsistencies after applying new changes. The automation is important because human intervention is time consuming and error prone.…”

Section: Introductionmentioning

confidence: 99%

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BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

Soliman¹,

Salah²,

Hefny³

2017

IJIES

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Knowledge gives a strong support to autonomous agents in multi-agent systems and thus the evolution of agent's knowledge needs a great attention since it has a control on agents' behaviors and has effect on their decisions making. The problem is to allow agents to detect and decide whether they need more domain knowledge and allow their knowledge to evolve consistently and automatically. This paper utilizes ontologies to represent the internal knowledge of agents instead of utilizing them only as a shared conceptualization. Consequently, the paper proposes a model of bottom-up instance-driven ontology evolution that allows the internal ontologies of agents to evolve automatically and consistently in run time based on agents' interactions. Experiments are designed and implemented to evaluate our model in different situations. One of its results shows that an empty internal ontology of one agent could evolve automatically in runtime by 88.3% through its interactions with other agents. Moreover, a comparison between the proposed approach and literature review approaches is presented to compare between their different features and techniques. This paper is considered a step forward to automate ontology evolution for agents in multiagent environment.

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Section: Related Workmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

Soliman¹,

Salah²,

Hefny³

2017

IJIES

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“…Some of the important frameworks are CONCORDIA ( [6]), CHAO ( [7]), Evolva ( [8], [9]), GOMMA ( [10]), COnto-Diff ( [11]) and CHO (Change History Ontology) ( [12], [13], [14]). However, only a few of the frameworks address the change propagation phase, such as [2], [6], [7], [15], [16] and [17].…”

Section: Related Workmentioning

confidence: 99%

Ontology-based Change Propagation in Shareable Health Information Applications

Sari¹,

Rahayu²

2015

ijacsa

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Abstract-One of the most important challenges to be addressed when establishing an integrated smart health environment is the availability of shareable health data and knowledge which standardize the interoperability of components within the environment. Health ontologies are commonly utilized to enable interoperability between applications in such environment. However, the dynamic nature of health knowledge causes the need for frequent changes in health ontologies which then must be propagated to the relevant applications. A change propagation method that can efficiently streamline the change management from an ontology to all the applications which reference to it is proposed. A component called a mapper is used to manage the mapping between application terms and ontology concepts. The mapper is aimed to maintain the applications' access to the most up-to-date ontology concepts and to improve the semantic mapping between the application terms and the ontology concepts. Some rules are developed for the change propagation process. The evaluation of the method shows that the mapper can improve the mapping list in terms of: (i) correctness, by proposing a new mapping entry to substitute an existing one which is not valid anymore because ontology concept is deleted or changed; (ii) currency maintenance, by recommending a better mapping between an application term and a new ontology concept based on the similarity value between the term and the new concept.

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“…EVOLVA [44] contributes in an original way to the evolution of ontologies from text within the NEON platform. As in our approach, the inputs of EVOLVA are the results of text analysis, i.e., a filtered list of candidate terms (single words) extracted from text.…”

Section: Evolving Ontologies From Textmentioning

confidence: 99%

DYNAMO-MAS: a Multi-Agent System for Ontology Evolution from Text

Sellami¹,

Camps²,

Aussenac-Gilles³

2013

J Data Semant

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Manual ontology development and evolution are complex and time-consuming tasks, even when textual documents are used as knowledge sources in addition to human expertise or existing ontologies. Processing natural language in text produces huge amounts of linguistic data that need to be filtered out and structured. To support both of these tasks, we have developed DYNAMO-MAS, an interactive tool based on an adaptive multi-agent system (adaptive MAS or AMAS) that builds and evolves ontologies from text. DYNAMO-MAS is a partner system to build ontologies; the ontologist interacts with the system to validate or modify its outputs. This paper presents the architecture of DYNAMO-MAS, its operating principles and its evaluation on three case studies.

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Ontology Evolution with Evolva

Cited by 26 publications

References 7 publications

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

BOEM: A Model for Automating Detection and Evolution of Distributed Ontologies in Multi-Agent Environment

Ontology-based Change Propagation in Shareable Health Information Applications

DYNAMO-MAS: a Multi-Agent System for Ontology Evolution from Text

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