Semi Automatic Ontology Instantiation in the domain of Risk Management

Makki, Jawad; Alquier, Anne-Marie; Prince, Violaine

doi:10.1007/978-0-387-87685-6_30

Cited by 4 publications

(5 citation statements)

References 7 publications

(6 reference statements)

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“…It consists of adding new instances of concepts and relations into an existing ontology. This process usually starts after the conceptual model of ontology is built [7].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Way to Classify Physical Effects for Ontology Instantiation

Zhang¹,

Cavallucci

Zanni-Merk

2019

IFIP Advances in Information and Communication Technology

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As one of the most important knowledge sources of TRIZ, the collection of Physical effects is currently searched in a very basic way. In order to enhance its use, different proposals have been brought to the community to classify effects into different categories. Among them, a rule-based approach classified the collection of physical effects into four categories and built a set of rules to facilitate its direct use. However, this approach is not robust enough due to the lack of instances of physical effects. In this paper, we propose a new approach to classify physical effects in order instantiate the existing ontology. In addition, preliminary results are presented to demonstrate the feasibility of the approach. The results brought us evidences that we facilitate the direct access to the collection of physical effects.

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“…It consists of adding new instances of concepts and relations into an existing ontology. This process usually starts after the conceptual model of ontology is built [7].…”

Section: Literature Reviewmentioning

confidence: 99%

A New Way to Classify Physical Effects for Ontology Instantiation

Zhang¹,

Cavallucci

Zanni-Merk

2019

IFIP Advances in Information and Communication Technology

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“…For example, generic ontologies are used in multi-agent systems, permitting agents to interoperate and cooperate through a common ontology (Su et al, 2003). PRIMA is another generic ontology which contains a set of concepts and relations describing risk and its incorporation in various work processes (Makki et al, 2008). Also generic ontologies are used for creating models for policy-based regulations, which incorporate different domains like Food and Drug Administration (FDA), financial regulation, contracts and individuals conducting business (El Kharbili & Stolarski, 2009).…”

Section: Generic Ontologiesmentioning

confidence: 99%

“…Thus, it is mostly semi-automatic, involving a human expert combined with statistical, syntactic and semantic techniques (Makki et al, 2008). Kohli et al (2012) manually extracted information using literature reviews, interviews and surveys from experts to develop the generic slum ontology (GSO), which comprises of concept that refer to the morphology of the environment.…”

Section: Generic Ontologiesmentioning

confidence: 99%

Towards knowledge modeling and manipulation technologies: A survey

Bimba

Al-Hunaiyyan

Mahmud

et al. 2016

International Journal of Information Management

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A system which represents knowledge is normally referred to as a knowledge based system (KBS). This article focuses on surveying publications related to knowledge base modelling and manipulation technologies, between the years 2000-2015. A total of 185 articles excluding the subject descriptive articles which are mentioned in the introductory parts, were evaluated in this survey. The main aim of this study is to identify different knowledge base modelling and manipulation techniques based on 4 categories; 1) linguistic knowledge base; 2) expert knowledge base; 3) ontology and 4) cognitive knowledge base. This led to the proposition of 8 research questions, which focused on the different categories of knowledge base modelling technologies, their underlying theories, knowledge representation technique, knowledge acquisition technique, challenges, applications, development tools and development languages. A part of the findings from this survey is the high dependence of linguistic knowledge base, expert knowledge base and ontology on volatile expert knowledge. A promising technique for knowledge-based business management and other knowledge related applications is also discussed.

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“…Therefore, several authors have extensively studied them in different areas: risk management (73,74), product formulation, unit operation model integration (18), determination of CQAs and CPPs (75), mathematical model storage, use, and solving (19), regulatory compliance (76), manufacturing execution systems (77) and drug discovery (78). …”

Section: Promising Km Tools For Pharmaceutical Production Systemsmentioning

confidence: 99%

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

et al. 2016

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Knowledge Management (KM) is a key enabler for achieving quality in a lifecycle approach for production of biopharmaceuticals. Due to the important role that it plays towards successful implementation of Quality by Design (QbD), an analysis of KM solutions is needed. This work provides a comprehensive review of the interface between KM and QbD-driven biopharmaceutical production systems as perceived by academic as well as industrial viewpoints. A comprehensive set of 356 publications addressing the applications of KM tools to QbD-related tasks were screened and a query to gather industrial inputs from 17 major biopharmaceutical organizations was performed. Three KM tool classes were identified as having high relevance for biopharmaceutical production systems and have been further explored: knowledge indicators, ontologies, and process modeling. A proposed categorization of 16 distinct KM tool classes allowed for the identification of holistic technologies supporting QbD. In addition, the classification allowed for addressing the disparity between industrial and academic expectations regarding the application of KM methodologies. This is a first of a kind attempt and thus we think that this paper would be of considerable interest to those in academia and industry that are engaged in accelerating development and commercialization of biopharmaceuticals.Electronic supplementary materialThe online version of this article (doi:10.1007/s11095-016-2043-9) contains supplementary material, which is available to authorized users.

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Semi Automatic Ontology Instantiation in the domain of Risk Management

Cited by 4 publications

References 7 publications

A New Way to Classify Physical Effects for Ontology Instantiation

A New Way to Classify Physical Effects for Ontology Instantiation

Towards knowledge modeling and manipulation technologies: A survey

Role of Knowledge Management in Development and Lifecycle Management of Biopharmaceuticals

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