Formalizing ontology reconciliation techniques as a basis for meaningful mediation in service-related tasks

Silva, Patrício de Alencar; Ribeiro, Claudia M. F. A.; Schiel, Ulrich

doi:10.1145/1316874.1316898

Cited by 4 publications

(3 citation statements)

References 15 publications

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“…Ontology description language OWL has a strong ability of knowledge representation and reasoning. The grammar is independent, scalable and applicable to the knowledge representation of distributed systems [15], [16]. However, it is not in an intuitive and graphical way to display knowledge, and there is no relationship between the resources and the related concepts contained.…”

Section: Relate Workmentioning

confidence: 99%

Visual Knowledge Structure Reasoning with Intelligent Topic Map

Bao

Chen

2010

IEICE Trans. Inf. & Syst.

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SUMMARYThis paper presents a visual knowledge structure reasoning method using Intelligent Topic Map which extends the conventional Topic Map in structure and enhances its reasoning functions. Visual knowledge structure reasoning method integrates two types of knowledge reasoning: the knowledge logical relation reasoning and the knowledge structure reasoning. The knowledge logical relation reasoning implements knowledge consistency checking and the implicit associations reasoning between knowledge points. We propose a Knowledge Unit Circle Search strategy for the knowledge structure reasoning. It implements the semantic implication extension, the semantic relevant extension and the semantic class belonging confirmation. Moreover, the knowledge structure reasoning results are visualized using ITM Toolkit. A prototype system of visual knowledge structure reasoning has been implemented and applied to the massive knowledge organization, management and service for education.

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Section: Relate Workmentioning

confidence: 99%

Visual Knowledge Structure Reasoning with Intelligent Topic Map

Bao

Chen

2010

IEICE Trans. Inf. & Syst.

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“…FCA-Merge [10] used the FCA (Formal Concept Analysis) method to merge between ontologies sharing a set of instances. Ontology reconciliation techniques such as merging, alignment and integration were focused on providing a formal description [11].…”

Section: Related Workmentioning

confidence: 99%

Extended topic map: Knowledge collaborative building for distributed knowledge resource

Bao

Chen

2010

2010 IEEE Network Operations and Management Symposium - NOMS 2010

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The characteristics of information resources are distributed, autonomous, heterogeneous and full of semantics in complex knowledge-based organizations at the era of knowledge economy. In order to realize efficient knowledge acquisition, representation, exchange, and sharing, we propose a novel concept of extended topic map, which extends the conventional topic map in structure. It embodies the multi-level, multigranularity and other inherent relevant characteristics of knowledge. With the extended topic map as infrastructure, the framework of knowledge collaborative building in distributed scenario is achieved by the following four stages: knowledge extraction, local extended topic map generation, similarity calculation and global extended topic map generation. We give the key technologies of each stage, which take advantage of extended topic map-based techniques such as comprehensive information theory based similarity measure and merging strategies. Finally, an example of multi-resource knowledge integration based on extended topic map is given to elaborate the knowledge collaborative building process.

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“…XML provides a flexible, general, rich structured information representation and convenient for the cooperative processing of heterogeneous knowledge [7]. RDF is an effective means of semantic information description [8]. Ontology establishes a classified hierarchy by defining the concepts and the relevance between them, and thus to build the semantic space of concepts [9].…”

Section: Introductionmentioning

confidence: 99%

Study on Visual Knowledge Structure Reasoning

Lu¹,

Hu²,

Liu³

2011

JSW

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Intelligent Topic Map (ITM) embodies the multi-level, multi-granularity and the inherent relevant characteristics of knowledge. With ITM as infrastructure, this paper presents a visual knowledge structure reasoning method integrates the logic-based knowledge reasoning and the structure-based knowledge reasoning. The logic-based knowledge reasoning implements knowledge consistency checking and the implicit associations reasoning between knowledge points, it can help us obtain the optimal description of knowledge. In order to construct the complete knowlege structure, a Knowledge Unit Circle Search strategy for structure-based knowledge reasoning is proposed, by which more detailed semantic association of knowledge is provided and the inherent relevant characteristics of knowledge is obtained. The knowledge reasoning results are visualized by ITM, which provides a visual knowledge map. It is available for users to acquire the knowledge and associations among them. A prototype system has been implemented and applied to the massive knowledge organization, management and service for education

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Formalizing ontology reconciliation techniques as a basis for meaningful mediation in service-related tasks

Cited by 4 publications

References 15 publications

Visual Knowledge Structure Reasoning with Intelligent Topic Map

Visual Knowledge Structure Reasoning with Intelligent Topic Map

Extended topic map: Knowledge collaborative building for distributed knowledge resource

Study on Visual Knowledge Structure Reasoning

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