Combining weights with fuzziness for intelligent semantic web search

Jin, Hai; Ning, Xiaomin; Jia, Weijia; Wu, Hao; Lu, Guilin

doi:10.1016/j.knosys.2008.03.040

Cited by 15 publications

(6 citation statements)

References 36 publications

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“…NFP-based simple ordering proposals [7] or multicriteria ranking approaches [8,9] that offer simpler preference modeling and more efficient ranking mechanisms can be directly applied to evaluate this kind of preference. In turn, more expressive approaches, such as those based on utility functions [10,11] or fuzzy logics [12,13], are less suitable because they exhibit a lower ranking performance, in general, though they provide more complex preference modeling facilities.…”

Section: Challenges In Semantic Web Service Rankingmentioning

confidence: 99%

Integrating semantic Web services ranking mechanisms using a common preference model

García

Junghans

Ruiz

et al. 2013

Knowledge-Based Systems

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Service ranking has been long-acknowledged to play a fundamental role in helping users to select the best offerings among services retrieved from a search request. There exist many ranking mechanisms, each one providing ad hoc preference models that offer different levels of expressiveness. Consequently, applying a single mechanism to a particular scenario constrains the user to define preferences based on that mechanism's facilities. Furthermore, a more flexible solution that uses several independent mechanisms will face interoperability issues because of the differences between preference models provided by each ranking mechanism. In order to overcome these issues, we propose a Preferencebased Universal Ranking Integration (PURI) framework that enables the combination of several ranking mechanisms using a common, holistic preference model. Using PURI, different ranking mechanisms are seamlessly and transparently integrated, offering a single façade to define preferences using highly expressive facilities that are not only decoupled from the concrete mechanisms that perform the ranking process, but also allow to exploit synergies from the combination of integrated mechanisms. We also thoroughly present a particular application scenario in the SOA4All EU project and evaluate the benefits and applicability of PURI in further domains.

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Section: Challenges In Semantic Web Service Rankingmentioning

confidence: 99%

Integrating semantic Web services ranking mechanisms using a common preference model

García

Junghans

Ruiz

et al. 2013

Knowledge-Based Systems

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“…Semantic Web consists of machine readable content defined and encoded in a way that it can be used by machines not just for display purposes, but for automation and interoperability of content across various applications (Jin et al, 2008). The development of the Semantic Web depends on a shared understanding with structured mark-up languages using formally defined ontology encoding (Dadzie et al, 2009, Benn et al, 2008, Chen et al, 2008.…”

Section: Contentmentioning

confidence: 99%

A dynamic knowledge management framework for the high value manufacturing industry

Piorkowski

Gao

Martin

2013

International Journal of Production Research

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Dynamic Knowledge Management (KM) is a combination of cultural and technological factors, including the cultural factors of people and their motivations, technological factors of content and infrastructure and, where these both come together, interface factors. In this paper a Dynamic KM framework is described in the context of employees being motivated to create profit for their company through product development in high value manufacturing. It is reported how the framework was discussed during a meeting of the collaborating company's (BAE Systems) project stakeholders. Participants agreed the framework would have most benefit at the start of the product lifecycle before key decisions were made. The framework has been designed to support organisational learning and to reward employees that improve the position of the company in the market place.

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“…Jin and others [38], proposed a novel approach which enables intelligent semantic web search for best satisfying users search intensions. The proposed approach combines the user's subjective weighting importance over multiple search properties together with fuzziness to represent search requirements.…”

Section: Related Workmentioning

confidence: 99%

Personalized Semantic Retrieval and Summarization of Web Based Documents

Babek¹,

Fouad²,

Arshad³

2013

IJACSA

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-The current retrieval methods are essentially based on the string-matching approach lacking of semantic information and can't understand the user's query intent and interest very well. These methods do regard as the personalization of the users. Semantic retrieval techniques are performed by interpreting the semantic of keywords. Using the text summarization allows a user to get a sense of the content of a full-text, or to know its information content, without reading all sentences within the full-text.In this paper, a semantic personalized information retrieval (IR) system is proposed, oriented to the exploitation of Semantic Web technology and WordNet ontology to support semantic IR capabilities in Web documents. In a proposed system, the Web documents are represented in concept vector model using WordNet. Personalization is used in a proposed system by building user model (UM). Text summarization in a proposed system is based on extracting the most relevant sentences from the original document to form a summary using WordNet.The examination of the proposed system is performed by using three experiments that are based on relevance based evaluation. The results of the experiment shows that the proposed system, which is based on Semantic Web technology, can improve the accuracy and effectiveness for retrieving relevant Web documents.

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Combining weights with fuzziness for intelligent semantic web search

Cited by 15 publications

References 36 publications

Integrating semantic Web services ranking mechanisms using a common preference model

Integrating semantic Web services ranking mechanisms using a common preference model

A dynamic knowledge management framework for the high value manufacturing industry

Personalized Semantic Retrieval and Summarization of Web Based Documents

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