A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

Valencia‐García, Rafael; Fernández‐Breis, Jesualdo Tomás; Ruiz-Martínez, Juana María; García‐Sánchez, Francisco; Martı́nez-Béjar, Rodrigo

doi:10.1111/j.1468-0394.2008.00464.x

Cited by 32 publications

(19 citation statements)

References 26 publications

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“…Furthermore, the challenges associated with traditional search tools as well as matching between user requirement and advertised services by providers can be eliminated through ontologies and semantic technologies [25]. It is important in the area of information integration as seen in the work of [26], Knowledge management [3], Information retrieval and question answering [6] and Recommendation [27].…”

Section: Related Workmentioning

confidence: 99%

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Cloudysme: An Ontological Framework for Aiding SMEs Adoption of SaaS in a Cloud Environment

Otuka¹,

Tawil²,

Al-Nemrat³

2017

JCC

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Adoption techniques are widely applied in and for cloud service usage to improve the slow acceptance rate of cloud services by SMEs. In such context, a well-understood problem is finding a suitable service from the vast number of services offering similar packages to satisfy user requirements such as security, cost, trust and operating systems compatibility has become a big challenge. However, a major drawback of existing techniques such as frameworks, web search, decision support tools, management models, ontology models and agent technology is that they are restricted to a specific task or they replicate service provider offerings. In this paper, we present Cloudysme a cloud service adoption solution, a middleware that is capable of aiding the decision making process for SMEs adoption of cloud services. Using a case study of SaaS storage services offerings by cloud providers, we introduce a new formalism for judging the superiority of one service attribute over another, we propose an extended version of pairwise comparison and Analytical hierarchical Process (AHP) which is a traditional multi-criteria decision method (MCDM) in solving complex comparisons. We solve the issue of service recommendation by introducing an acceptable standard for each service attribute and propose a protocol using rational relationships for aiding cloud service ranking process. We tackle the issue of specific tasking by using a set of concepts and associated semantic rules to rank and retrieve user requirements. We promote a knowledge engineering approach for natural language processing by using terms and conditions in translating human sentences to machine readable language. Finally, we implement our system using 30 SMEs as a pivotal study. We prove that the use of semantic rules within an ontology can tackle the issue of specific tasking.

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Section: Related Workmentioning

confidence: 99%

“…[3] [4] [5] proposed an ontology of cloud service knowledge management. [6] proposed a cloud service recommendation ontology. [7] proposed an ontology based on service discovery in different service layers.…”

Section: Introductionmentioning

confidence: 99%

Cloudysme: An Ontological Framework for Aiding SMEs Adoption of SaaS in a Cloud Environment

Otuka¹,

Tawil²,

Al-Nemrat³

2017

JCC

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“…As Table 8 represents, the second application is the use of domain ontologies for the assessment of data quality in the querying requirements (Mabotuwana and Warren 2009), extracting knowledge from natural language documents (Valencia-Garcia et al 2008), and data expression (Preece et al 2008). The majority of these studies used precision and recall as metrics to assess the accuracy and validate the ontological approaches (Brank et al 2005;Brewster et al 2004;Euzenat 2007;Gangemi et al 2006;Li 2010;Min et al 2009;Pathak et al 2012aPathak et al , 2012bSpasic and Ananiadou, 2005;Stvilia et al 2009;Valencia-Garcia et al 2008;Wang et al 2007).…”

Section: %mentioning

confidence: 99%

“…The majority of these studies used precision and recall as metrics to assess the accuracy and validate the ontological approaches (Brank et al 2005;Brewster et al 2004;Euzenat 2007;Gangemi et al 2006;Li 2010;Min et al 2009;Pathak et al 2012aPathak et al , 2012bSpasic and Ananiadou, 2005;Stvilia et al 2009;Valencia-Garcia et al 2008;Wang et al 2007).…”

Section: %mentioning

confidence: 99%

“…Defined purpose Assessed of fitness for purpose using DQ Context Assessment (7 papers) (Jacquelinet et al 2003) To develop semantic data interoperability -Apply an ontological tool to develop semantic data interoperability through domain terminologies using quantitative analysis of the existing coding information system and a qualitative analysis checking completeness, consistency, ambiguity and implicitness of terms Failure, dialysis and transplant datasets from National information system in France -Represent DQ factors such as completeness of data, appropriated terms, structured thesaurus, and terminology standard -Authors state usefulness of ontology based approach to support the processing of texts, and extending a terminological basis for medical experts (Maragoudakis et al 2008 Gene expression data which involve the use of microarrays in UK -Guide the development and use of metrics to measure the complexity and cohesion of ontologies -Authors state that ontology helps to allow a practical division of the work between providers and consumers, in order to minimize the costs to all concerned 2012b; Spasic and Ananiadou 2005;Stvilia et al 2009;Valencia-Garcia et al 2008;Wang et al 2007). Table 9 illustrates various definitions to identify the most common criteria to assess validity of ontologies and data models.…”

Section: Ontology Functions Referencesmentioning

confidence: 99%

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Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

et al. 2014

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This systematic review examined the current state of conceptualization and specification of data quality and the role of ontology based approaches to develop data quality based on "fitness for purpose" within the health context. A literature review was conducted of all English language studies, from January 2000-March 2013, which addressed data/information quality, fitness for purpose of data, used and implemented ontology-based approaches. Included papers were critically appraised with a "context-mechanism-impacts/outcomes" overlay. We screened 315 papers, excluded 36 duplicates, 182 on abstract review and 46 on full-text review; leaving 52 papers for critical appraisal. Six papers conceptualized data quality within the "fitness for purpose" definition. While most agree with a multidimensional definition of DQ, there is little consensus on a conceptual framework. We found no reports of systematic and comprehensive ontological approaches to DQ based on fitness for purpose or use. However, 16 papers used ontology-specified implementations in DQ improvement, with most of them focusing on some dimensions of DQ such as completeness, accuracy, correctness, consistency and timeliness. The majority of papers described the processes of the development of DQ in various information systems. There were few evaluative studies, including any comparing ontological with non-ontological approaches, on the assessment of clinical data quality and the performance of the application.

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A novel approach for question answering and automatic diagnosis based on pervasive agent ontology in medicine

Guo

2010

Int. J. Intell. Syst.

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With question answering system in medicine, users could use sentences in daily life to raise questions. The question answering system will analyze and comprehend these questions and return answers to users directly. Aiming at the problems in automatic diagnosis for medicine, such as low precision of question answering, imperfect expression of domain knowledge, low reuse rate, and lack of reasonable theory reference models, we put forward the information integration method of semantic Web based on pervasive agent ontology (SWPAO method) in medicine, which will integrate, analyze, and process enormous Web information and extract answers on the basis of semantics. A novel approach for automatic diagnosis in medicine based on ontology and fuzzy rough set is brought forward. The data mining algorithm for automatic diagnosis rules in medicine is brought forward: (1) computing the measurement matrix of effect; (2) extracting rules; (3) computing the importance of rules; (4) shearing the rules by genetic algorithm. In this paper, rough sets method is used to take potential diagnosis rule from the decision-making table in medicine. These rules can offer effective automatic diagnosis service. With the SWPAO method as the clue, we mainly study the method of concept extraction based on uniform semantic term mining, pervasive agent ontology construction method on account of multipoints and the answer extraction in view of semantic inference. Meanwhile, we present the structural model of the question answering system applying ontology, which adopts OWL language to describe domain knowledge base from where it infers and extracts answers by Jena inference engine, thus the precision of question answering in QA system could be improved. In the system testing, the precision has reached 86% and the recalling rate is 93%. The experiment indicates that this method is feasible, and it has the signiÞcance of reference and value of further study for the question answering systems in medicine. C

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A knowledge acquisition methodology to ontology construction for information retrieval from medical documents

Cited by 32 publications

References 26 publications

Cloudysme: An Ontological Framework for Aiding SMEs Adoption of SaaS in a Cloud Environment

Cloudysme: An Ontological Framework for Aiding SMEs Adoption of SaaS in a Cloud Environment

Ontological specification of quality of chronic disease data in EHRs to support decision analytics: a realist review

A novel approach for question answering and automatic diagnosis based on pervasive agent ontology in medicine

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