Ontology Evolution: A Survey and Future Challenges

Khattak, Asad Masood; Latif, Khalid; Lee, Songyoung; Lee, Young-Koo

doi:10.1007/978-3-642-10580-7_11

Cited by 26 publications

(47 citation statements)

References 8 publications

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“…Keeping in mind that it will appear new manufacturers and new users with new profiles, the key point of future work will be to provide to the ontology capabilities of evolution and versioning [26]. To achieve this target it could be used methodologies like DOGMA-MESS [27][28], in a native way or adapting to the global system, like in DiYSE project, to gradually enrich the ontology.…”

Section: Discussionmentioning

confidence: 99%

A Novel Context Ontology to Facilitate Interoperation of Semantic Services in Environments with Wearable Devices

Rubio

Serral

Castillejo

et al. 2012

On the Move to Meaningful Internet Systems: OTM 2012 Workshops

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Abstract. The LifeWear-Mobilized Lifestyle with Wearables (Lifewear) project attempts to create Ambient Intelligence (Ami) ecosystems by composing personalized services based on the user information, environmental conditions and reasoning outputs. Two of the most important benefits over traditional environments are 1) take advantage of wearable devices to get user information in a non-intrusive way and 2) integrate this information with other intelligent services and environmental sensors. This paper proposes a new ontology composed by the integration of users and services information, for semantically representing this information. Using an Enterprise Service Bus, this ontology is integrated in a semantic middleware to provide context-aware personalized and semantically annotated services, with discovery, composition and orchestration tasks. We show how these services support a real scenario proposed in the Lifewear project. IntroductionWearable devices are becoming more advanced, accurate and capable of sensing. To take full advantage of them is necessary to combine their information with the obtained from the context and the own user. The amount and heterogeneity of the information that comes into play means that architectures and data models must be equipped with the features necessary to develop applications increasingly customized to the user and facilitate interoperation and information sharing. The main concern has been to developed ontologies, middlewares and architectures to resolve problems related with ubiquitous computing, but, basically thinking in services provided to the web. With the advance of technology, new services based on the user information, environmental conditions and reasoning outputs are emerging in the context of Ambient Intelligence (Ami) ecosystems. Adding to these data a user

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Section: Discussionmentioning

confidence: 99%

A Novel Context Ontology to Facilitate Interoperation of Semantic Services in Environments with Wearable Devices

Rubio

Serral

Castillejo

et al. 2012

On the Move to Meaningful Internet Systems: OTM 2012 Workshops

View full text Add to dashboard Cite

show abstract

“…To represent the changes in CHL, there are four categories in the ontology to represent different components of the ontology that are subject to change (i.e., ClassChange, PropertyChange, IndividualChange, and OntologyChange [13]). Based on the above mentioned categories, we derive instances of class OntologyChange, represented with the symbol D, using the following axioms, for details see [3] and [13]:…”

Section: Change History Ontologymentioning

confidence: 99%

“…Ontology is a formal, explicit specification of a shared conceptualization, and use of ontology in systems dealing with information extraction from large complex structured information can provide valuable results [2]. The increasing use of ontology in information systems also increases the significance of ontology maintenance [3].…”

Section: Introductionmentioning

confidence: 99%

“…Falcon [2], MAFRA [5], H-Match [6], Lily [10], and TaxoMap [11] are amongst the best matching and mapping systems. These systems require substantial computational resources to consider large ontologies, such as Google Classification, 1 Wiki Classification, 2 ACM Classification Hierarchy, 3 and MSC Classification Hierarchy 4 for mapping. On the other hand, when one or both of the mapped ontologies change from one state to another, then the existing mappings are no longer reliable.…”

Section: Introductionmentioning

confidence: 99%

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Time efficient reconciliation of mappings in dynamic web ontologies

Khattak

Pervez

Latif

et al. 2012

Knowledge-Based Systems

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“…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.…”

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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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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Ontology Evolution: A Survey and Future Challenges

Cited by 26 publications

References 8 publications

A Novel Context Ontology to Facilitate Interoperation of Semantic Services in Environments with Wearable Devices

A Novel Context Ontology to Facilitate Interoperation of Semantic Services in Environments with Wearable Devices

Time efficient reconciliation of mappings in dynamic web ontologies

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

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