Ontology-Based Natural Language Parser for E-Marketplaces

Coppi, S.; Noia, Tommaso Di; Sciascio, Eugenio Di; Donini, Francesco M.; Pinto, Agnese

doi:10.1007/11504894_39

Cited by 5 publications

(4 citation statements)

References 6 publications

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“…9. Di Noia, Di Sciascio, Donini, & Pinto, 2005;Wang, Xiong, Zhou, & Yu, 2007;Saba, 2007). Writing more complicated queries (i.e., closer to the natural language used by humans) induces an increase of fuzziness giving and, maybe, the possibility to reduce the gap of fuzzy precision and fuzzy recall between fuzzy and crisp cases.…”

Section: Analytic Study Of the Experimentsmentioning

confidence: 98%

Object‐fuzzy concept network: An enrichment of ontologies in semantic information retrieval

Calegari

Sanchez

2008

J. Am. Soc. Inf. Sci.

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This article shows how a fuzzy ontology-based approach can improve semantic documents retrieval. After formally defining a fuzzy ontology and a fuzzy knowledge base, a special type of new fuzzy relationship called (semantic) correlation, which links the concepts or entities in a fuzzy ontology, is discussed. These correlations, first assigned by experts, are updated after querying or when a document has been inserted into a database. Moreover, in order to define a dynamic knowledge of a domain adapting itself to the context, it is shown how to handle a tradeoff between the correct definition of an object, taken in the ontology structure, and the actual meaning assigned by individuals. The notion of a fuzzy concept network is extended, incorporating database objects so that entities and documents can similarly be represented in the network. Information retrieval (IR) algorithm, using an object-fuzzy concept network (O-FCN), is introduced and described. This algorithm allows us to derive a unique path among the entities involved in the query to obtain maxima semantic associations in the knowledge domain. Finally, the study has been validated by querying a database using fuzzy recall, fuzzy precision, and coefficient variant measures in the crisp and fuzzy cases.

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Section: Analytic Study Of the Experimentsmentioning

confidence: 98%

Object‐fuzzy concept network: An enrichment of ontologies in semantic information retrieval

Calegari

Sanchez

2008

J. Am. Soc. Inf. Sci.

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“…It is doubtful they will be used as they are in Semantic Web oriented marketplaces. We envisage a marketplace, populated with semantic-endowed advertisements as proposed in [6], where users -or their agents-issue their requests of products and services as natural language advertisements, which are then automatically translated in structured DL concepts, using approaches such as the ones recently proposed in [19,9,20]. This appears necessary if we expect common users to practically use such systems, as it does not appear feasible that users have to become both logic and domain experts to post advertisements and to submit queries.…”

Section: The E-marketplacementioning

confidence: 99%

Knowledge elicitation for query refinement in a semantic-enabled e-marketplace

Colucci

Noia

Sciascio

et al. 2005

Proceedings of the 7th International Conference on Electronic Commerce - ICEC '05

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In this paper we present a knowledge-based approach to the elicitation of information from advertisements, in the framework of a semantic-enabled marketplace. The elicited information can be used for advertisements enriching and refining, without requiring users thorough knowledge of the domain, and to determine a logicbased exact match. The approach exploits non-standard inference services in Description Logics, namely Abduction and Contraction, to tackle a typical problem of semantic-enabled marketplaces, that is the difficulty the average or casual user has in exploiting all the knowledge expressed in an e-commerce domain, which appears necessary to issue requests. We present an algorithm, which returns the set of concepts not included in the request -that can be used for query refinement-and more interesting what is still missing for each available supply, to obtain an exact, bidirectional, match.

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“…The system has been designed within the MAMAS framework and implements a natural language parser that translates demand/supply advertisements into structured DL expressions, automatically mapping sentences with concepts and roles of an ontology. The ontology has to be specific for each domain [20].…”

Section: System and Experimentsmentioning

confidence: 99%

Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace

Colucci

Noia

Sciascio

et al. 2004

Proceedings of the 6th International Conference on Electronic Commerce - ICEC '04

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In this paper, we present a Description Logic approach -fully compliant with the Semantic web vision and technologies -to extended matchmaking between demands and supplies in a semantic-enabled Electronic Marketplace, which allows the semantic-based treatment of negotiable and strict requirements in the demand/supply descriptions. To this aim, we exploit two novel non-standard Description Logic inference services, Concept Contraction -which extends satisfiability -and Concept Abduction -which extends subsumption. Based on these services, we devise algorithms, which allow to find negotiation spaces and to determine the quality of a possible match, also in the presence of a distinction between strictly required and optional elements. Both the algorithms and the semantic-based approach are novel, and enable a mechanism to boost logic-based discovery and negotiation stages within an e-marketplace. A set of simple experiments confirm the validity of the approach.

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Ontology-Based Natural Language Parser for E-Marketplaces

Cited by 5 publications

References 6 publications

Object‐fuzzy concept network: An enrichment of ontologies in semantic information retrieval

Object‐fuzzy concept network: An enrichment of ontologies in semantic information retrieval

Knowledge elicitation for query refinement in a semantic-enabled e-marketplace

Concept abduction and contraction for semantic-based discovery of matches and negotiation spaces in an e-marketplace

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