2007
DOI: 10.2753/jec1086-4415120205
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A Nonmonotonic Approach to Semantic Matchmaking and Request Refinement in E-Marketplaces

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Cited by 43 publications
(39 citation statements)
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“…We propose an RFID-based delivery management system extending classic supply chain organization and shipment models using techniques and technologies for smart tagging [16] and a semantic-based decision support [4]. Due to space constraints, the reader is referred to cited works for an explanation of used Descritpion Logic languages and algorithms.…”
Section: Framework and Approachmentioning
confidence: 99%
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“…We propose an RFID-based delivery management system extending classic supply chain organization and shipment models using techniques and technologies for smart tagging [16] and a semantic-based decision support [4]. Due to space constraints, the reader is referred to cited works for an explanation of used Descritpion Logic languages and algorithms.…”
Section: Framework and Approachmentioning
confidence: 99%
“…The encoded product annotation and contextual parameters (depending on the specific application) are stored, instead, within the User memory bank. This is possible by adopting increasingly available models of passive EPCglobal UHF Class 1 Generation 2 RFID tags with several kilobits of available memory, such as TEGOTag 4 or Intelleflex IF602 5 . The air interface protocol for Gen 2 RFID systems is exploited but neither new commands nor modifications to existing ones are introduced, thus keeping full backward-compatibility with current RFID readers.…”
Section: Rfidmentioning
confidence: 99%
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“…Finally, a request for matchmaking is addressed to the reasoner running in background. 4 Case study: where are you going?…”
Section: Map Enhancementmentioning
confidence: 99%
“…Based on the formal semantics of such descriptions, an explanation of the matchmaking outcome is then provided to the user to foster further interaction. This is accomplished by using a lightweight version of non-standard inference algorithms, i.e., Concept Abduction, Concept Contraction and Bonuses Calculation [5,4].…”
Section: Introductionmentioning
confidence: 99%