An Ontology-based Personalized Retrieval Model Using Case Base Reasoning

Zidi, Amir; Bouhana, Amna; Abed, Mourad; Fekih, Afef

doi:10.1016/j.procs.2014.08.101

Cited by 9 publications

(3 citation statements)

References 25 publications

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“…On the other hand, the calculation of the similarity between ontology concepts by the clinical distance or the semantic distance increases the intelligence of the CBR system (Ko¨hler et al, 2009;Melton et al, 2006). Intelligent case retrieval depends on the existence of a domain ontology and an encoded case-based (Zidi et al, 2014). The coding of data supports the CBR semantic case retrieval in many forms.…”

Section: Related Workmentioning

confidence: 99%

An encoding methodology for medical knowledge using SNOMED CT ontology

El–Sappagh

Elmogy

2016

Journal of King Saud University - Computer and Information Scie

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Knowledge-Intensive Case Based Reasoning (KI-CBR) systems mainly depend on ontology. Using ontology as domain knowledge supports the implementation of semanticallyintelligent case retrieval algorithms. The case-based knowledge must be encoded with the same concepts of the domain ontology. Standard medical ontologies, such as SNOMED CT (SCT), can play the role of domain ontology to enhance case representation and retrieval. This study has three stages. First, we propose an encoding methodology using SCT. Second, this methodology is used to encode the case-based knowledge. Third, all the used SCT concepts are collected in a reference set, and an OWL2 ontology of 550 pre-coordinated concepts is proposed. A diabetes diagnosis is chosen as a case study of our proposed framework. SCT is used to provide a pre-coordination concept coverage of $75% for diabetes diagnosis terms. Whereas, the uncovered concepts in SCT are proposed. The resulting OWL2 ontology will be used as domain knowledge representation in diabetes diagnosis CBR systems. The proposed framework is tested by using 60 real cases. Ó 2015 The Authors. Production and hosting by Elsevier B.V. on behalf of King Saud University. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).

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Section: Related Workmentioning

confidence: 99%

An encoding methodology for medical knowledge using SNOMED CT ontology

El–Sappagh

Elmogy

2016

Journal of King Saud University - Computer and Information Scie

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“…This approach had a lot of limitations. Creating profile explicitly means, it may happen that sometimes user may give wrong information, which hampers user profile and also it is a time taking process Amir Zidia,*, Amna Bouhanab, Mourad Abeda, Afef Fekihc7 [2]proposed model using the Case Base Reasoning (CBR) tool called as ontology-Based Personalized Retrieval model. The proposed system combines the advantages of two methods, a content-based method (ontology) for representing data and a case-based method (CBR) making search process personalized and for giving users with alternate documents recommendations.…”

Section: Literature Reviewmentioning

confidence: 99%

Query Expansion Based on a Personalized Mobile Search Engine-A Review

2016

IJSR

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Day by day internet is becoming very crowdies with no. of web pages and no. of its users. So due to that it is becoming difficult to find exact resultant web pages for particular query in web search. Though recent developments on web search technologies, still for many situations users obtains the non-relevant search results from the search engines. A solution on this problem is personalization. Second thing, limitation of mobile search engine are, small parts of mobile like small screen, tiny keypad. due to these limiting factors user has to give short and ambiguous query. and in this condition also for getting relevant result for query, personalization is needed. Personalization means creating user profile inbuilt form user's previous clickthrough collector. This paper focuses on the survey of many efficient personalized Web search approaches and personalized mobile search approaches which were proposed by many authors.

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“…Wang and Shao (2000) made the weight of each attribute flexible for different problems by introducing rough set theory into the CBR system. Zidi et al (2014) proposed a personalization method to provide an efficient personalized search result and improve the user's satisfaction based on a combination of domain ontology and CBR tools. Wei et al (2015) designed an Architable system to search for architectural cases according to the rooms and functional layout input by the user.…”

Section: Introductionmentioning

confidence: 99%

A clustering-based method of typical architectural case mining for architectural innovation

Liu

Zou

Zhang

2020

Journal of Asian Architecture and Building Engineering

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Architectural innovation is important for improving the built environment. In recent years, an increasing number of architects have focused on this field. A comprehensive understanding of existing building design patterns contributes to reasonable innovation. Based on the large number of architectural cases on the internet in the big data era, this study proposes a threestage case mining method, containing case collection, case analysis and case study, to find typical architectural cases, discover existing design patterns and create new design patterns by using cluster analysis of architectural cases. An extensive architectural design case mining system and a case clustering program are developed to assist in the case analysis. An agglomerative hierarchical clustering algorithm is applied to support the case clustering program. The example shows the complete application process and practical effect of the proposed method. With this intelligent method, architects can make more reasonable innovations in projects. The proposed typical case mining method is also expected to be useful for engineers and planners with similar needs.

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An Ontology-based Personalized Retrieval Model Using Case Base Reasoning

Cited by 9 publications

References 25 publications

An encoding methodology for medical knowledge using SNOMED CT ontology

An encoding methodology for medical knowledge using SNOMED CT ontology

Query Expansion Based on a Personalized Mobile Search Engine-A Review

A clustering-based method of typical architectural case mining for architectural innovation

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