Autonomous Evolution of Access to Information in Institutional Decision-Support Systems Using Agent and Semantic Web Technologies

Polajnar, Desanka; Polajnar, Jernej; Zubayer, Mohammad

doi:10.1007/978-3-642-35016-0_6

Cited by 1 publication

(6 citation statements)

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“…To this end, Semantic Query Access System (SQAS) (Polajnar et al , 2012(Polajnar et al , , 2014 proposes an innovative distributed agent-oriented architecture with intelligent software agents which develop a domain ontology with their know-how of ontology building process. In SQAS , a user can execute sophisticated semantic queries against an RDB using a customized ontology which is built from the user 's definitions of domain concepts.…”

Section: Ds Based On Knowledge Representationmentioning

confidence: 99%

“…In SQAS , a user can execute sophisticated semantic queries against an RDB using a customized ontology which is built from the user 's definitions of domain concepts. An important aspect of that proposal is that an enterprise decisionsupport system like SQAS could be developed within the organization itself, and evolve with its needs, without modifying the underlying RDB (Polajnar et al , 2014).…”

Section: Ds Based On Knowledge Representationmentioning

confidence: 99%

“…SQAS (Polajnar et al, 2012(Polajnar et al, , 2014) is a qualified intelligent decision support system which develop domain ontology with intelligent agents to support semantic queries over the RDB. It employs D2RQ Mapping Language to facilitate the conversion from the RDB data to RDF triples.…”

Section: 4 U Sing Mas For Ontology D E Velopmentmentioning

confidence: 99%

“…With the background, context, and related work presented in the previous chapter, we now revisit the thesis objectives stated in the Introduction. Essentially, we address the agent-assisted ontology building (AAOB) , as introduced in SQAS (Polajnar et al , 2012(Polajnar et al , , 2014 , from complete multiagent system design perspective, with prototype implementation of key AAOB functions. The overall design of the multiagent system, including the agent team structure and various supporting modules and utilities , is embodied in the architecture of the Ontology Building Multiagent System (OBMAS) .…”

Section: Chapter 3 Research Objectivesmentioning

confidence: 99%

“…The potential for advancement of intelligent decision support through synergy of multiagent and Semantic Web technologies has been recognized and elaborated in the description of the Semantic Query Access System (SQAS) by Polajnar et al (2012Polajnar et al ( , 2014. It envisions an architecture of a distributed system, integrated through agentoriented middleware, with server nodes containing reference ontologies constructed on top of relational databases for relevant knowledge domains, and client nodes that provide user-specific access to servers through a layer of locally developed custom ontology.…”

Section: Introductionmentioning

confidence: 99%

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A software system for agent-assisted ontology building

Mumbaiwala¹

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This thesis investigates how one can design a team of intelligent software agents that helps its human partner develop a formal ontology from a relational database and enhance it with higher-level abstractions. The resulting efficiency of ontology development could facilitate the building of intelligent decision support systems that allow:high-level semantic queries on legacy relational databases; autonomous implementation within a host organization; and incremental deployment without affecting the underlying database or its conventional use. We introduce a set of design principles, formulate the prototype system requirements and architecture, elaborate agent roles and interactions , develop suitable design techniques, and test the approach through practical implementation of selected features. We endow each agent with model metaontology, which enables it to reason and communicate about ontology, and planning meta-ontology, which captures the role-specific know-how of the ontology building method. We also assess the maturity of development tools for a larger-scale implementation. Contents Abstract List of Figures vi Acknowledgements viiiDedication 1x

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Section: Ds Based On Knowledge Representationmentioning

confidence: 99%

Section: Ds Based On Knowledge Representationmentioning

confidence: 99%

Section: 4 U Sing Mas For Ontology D E Velopmentmentioning

confidence: 99%

Section: Chapter 3 Research Objectivesmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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