The KRAFT architecture for knowledge fusion and transformation

Preece, Alun; Hui, Kit; Gray, Alex; Marti, Philippe; Bench‐Capon, Trevor; Jones, Dean M.; Cui, Zhan

doi:10.1016/s0950-7051(00)00052-6

Cited by 71 publications

(13 citation statements)

References 5 publications

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“…To overcome problems due to semantic heterogeneity, it is useful to use ontologies (Wache et al, 2001). Depending on how ontologies are used, data integration systems can adopt different approaches, such as single ontology (adopted in SIMS (Arens et al, 1993;Arens, Hsu & Knoblock, 1996)), multiple ontology (adopted in OBSERVER (Mena et al, 1996b;Mena et al, 1996a)) and hybrid (adopted in KRAFT (Preece, Hui & Gray, 1999;Preece et al, 2000) and COIN (Goh et al, 1999)).…”

Section: Related Workmentioning

confidence: 99%

An approach for semantic integration of heterogeneous data sources

Fusco

2020

PeerJ Computer Science

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Integrating data from multiple heterogeneous data sources entails dealing with data distributed among heterogeneous information sources, which can be structured, semi-structured or unstructured, and providing the user with a unified view of these data. Thus, in general, gathering information is challenging, and one of the main reasons is that data sources are designed to support specific applications. Very often their structure is unknown to the large part of users. Moreover, the stored data is often redundant, mixed with information only needed to support enterprise processes, and incomplete with respect to the business domain. Collecting, integrating, reconciling and efficiently extracting information from heterogeneous and autonomous data sources is regarded as a major challenge. In this paper, we present an approach for the semantic integration of heterogeneous data sources, DIF (Data Integration Framework), and a software prototype to support all aspects of a complex data integration process. The proposed approach is an ontology-based generalization of both Global-as-View and Local-as-View approaches. In particular, to overcome problems due to semantic heterogeneity and to support interoperability with external systems, ontologies are used as a conceptual schema to represent both data sources to be integrated and the global view.

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Section: Related Workmentioning

confidence: 99%

An approach for semantic integration of heterogeneous data sources

Fusco

2020

PeerJ Computer Science

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“…Finally, ontologies can serve to support knowledge level interoperability among heterogeneous knowledge sources [19,20]. They provide a layer between an agent (human or artificial) and the physical knowledge sources, by formally defining the domain knowledge and explicitly specifying the content of the knowledge sources using the concepts of the ontology (meta-models).…”

Section: Ontologiesmentioning

confidence: 99%

Exploitation of a priori knowledge for information fusion

Bossé¹,

Valin²,

Boury-Brisset³

et al. 2006

Information Fusion

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“…Common ontology approach is to develop a global ontology (shared vocabulary) to identify and associate semantics in information resources explicitly or implicitly, and the user query is issued according to the global ontology. This approach was taken by many projects [12] , such as SIMS [21] , TSIMMIS [18] , Infosleuth [3] , KRAFT [1] ,PICSEL [5] , DWQ [4] , Ontobroker [19] , SHOE [8] , MECOTA [7] and BUSTER [20] . A global ontology is developed by extracting the implicit or explicit semantics from the data resources, but in general the semantics in the data sources cannot be preserved during the ontology extraction and integration process with current approaches due to the arbitrariness that seems in existence in many cases in the developing process of the global ontology.…”

Section: Related Workmentioning

confidence: 99%

An Information Flow Based Approach to Semantic Integration of Distributed Digital Museums

Liu

Bao

et al. 2006

2006 International Conference on Machine Learning and Cybernetics

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The distribution of data and knowledge of a huge amount of cultural heritage and the desire to make them available readily to people geographically scattered gives rise to the challenge of semantic integration of distributed digital museums. In order to make this difficult task tractable, how the participant museums are linked conceptually must be looked at first before any implementation issues for links of more a physical nature can be considered. One promising avenue to this end seems to characterize what we call 'informational relationship' between participant museums, which are seen within a (notional) distributed system. And this characterization including reasoning about this relationship should be carried out in both a meaningful and a principled way. In this paper, we describe how to derive semantics in legacy relational database to form local ontology, and present an information flow based approach to mathematically model the relationship among multiple distributed local ontologies in terms of an information channel construction, which involves the construction of classifications and local logics and infomorphisms between them, thus global ontology construction and token connection discovery, To help the establishment of semantic correspondences between various components of the distributed system, which is necessary for the task, the cultural heritage domain ontology CIDOC Conceptual Reference Model (CRM) is deployed as a bridge ontology. The soundness and completeness of the local ontologies is preserved in the global ontology developed in this way.

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The KRAFT architecture for knowledge fusion and transformation

Cited by 71 publications

References 5 publications

An approach for semantic integration of heterogeneous data sources

An approach for semantic integration of heterogeneous data sources

Exploitation of a priori knowledge for information fusion

An Information Flow Based Approach to Semantic Integration of Distributed Digital Museums

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