Supporting domain experts to construct conceptual ontologies: A holistic approach

Denaux, Ronald; Dolbear, Catherine; Hart, Glen; Dimitrova, Vania; Cohn, Anthony G.

doi:10.1016/j.websem.2011.02.001

Cited by 31 publications

(11 citation statements)

References 3 publications

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“…2 STyLE-OLM communication medium (Dimitrova 2003). The diagrammatic representation shows the domain statement, the illocutions are indicated as sentence openers corresponding to the dialogue moves selected by the buttons on the right knowledge (Denaux et al 2011(Denaux et al , 2012. Another way to extend the communication means could be to explore various ontology visualisation languages (c.f.…”

Section: Style-olm: Interactive Open Learner Modellingmentioning

confidence: 99%

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

Dimitrova

Brna

2015

Int J Artif Intell Educ

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STyLE-OLM (Dimitrova 2003 International Journal of Artificial Intelligence in Education, 13, 35-78) presented a framework for interactive open learner modelling which entails the development of the means by which learners can inspect, discuss and alter the learner model that has been jointly constructed by themselves and the system. This paper outlines the STyLE-OLM framework and reflects on the key challenges it addressed: (a) the design of an appropriate communication medium; this was addressed by proposing a structured language using diagrammatic representations of conceptual graphs; (b) the management of the interaction with the learner; this was addressed by designing a framework for interactive open learner modelling dialogue utilising dialogue games; (c) the accommodation of different beliefs about the learner's domain model; this was addressed with a mechanism for maintaining different views about the learner beliefs which adapted belief modal logic operators; and (d) the assessment of any resulting improvements in learner model accuracy and learner reflection; this was addressed in a user study with an instantiation of STyLE-OLM for diagnosing a learner's knowledge of finance concept, as part of a larger project that developed an intelligent system to assist with learning domain terminology in a foreign language. Reviewing follow on work, we refer to projects by the authors' students and colleagues leading to further extension and adoption of STyLE-OLM, as well as relevant approaches in open learner modelling which have cited the STyLE-OLM framework. The paper points at outstanding research challenges and outlines future a research direction to extend interactive open learner modelling towards mentor-like intelligent learning systems.

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Section: Style-olm: Interactive Open Learner Modellingmentioning

confidence: 99%

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

Dimitrova

Brna

2015

Int J Artif Intell Educ

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“…Our work is the first attempt at building such a computer‐processable model for cultural variations in interpersonal communication. The AMOn+ ontology is implemented with the state‐of‐the‐art tools such as ROO (Denaux, Dolbear, Hart, Dimitrova, & Cohn, ) and Protégé (Knublauch, Fergerson, Noy, & Musen, ) and published with well‐established W3C recommendations such as Web Ontology Language (OWL; McGuinness & Van Harmelen, ). Encoding of ontologies using OWL allows knowledge engineers to create extensible ontologies that are easy to integrate with different applications, primarily due to its XML‐like syntax, and web‐centric approach.…”

Section: Relevant Workmentioning

confidence: 99%

Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge

Thakker

Karanasios

Blanchard³

et al. 2017

Asso for Info Science & Tech

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The domain of cultural variations in interpersonal communication is becoming increasingly important in various areas, including human–human interaction (e.g., business settings) and human–computer interaction (e.g., during simulations, or with social robots). User‐generated content (UGC) in social media can provide an invaluable source of culturally diverse viewpoints for supporting the understanding of cultural variations. However, discovering and organizing UGC is notoriously challenging and laborious for humans, especially in ill‐defined domains such as culture. This calls for computational approaches to automate the UGC sensemaking process by using tagging, linking, and exploring. Semantic technologies allow automated structuring and qualitative analysis of UGC, but are dependent on the availability of an ontology representing the main concepts in a specific domain. For the domain of cultural variations in interpersonal communication, no ontological model exists. This paper presents the first such ontological model, called AMOn+, which defines cultural variations and enables tagging culture‐related mentions in textual content. AMOn+ is designed based on a novel interdisciplinary approach that combines theoretical models of culture with crowdsourced knowledge (DBpedia). An evaluation of AMOn+ demonstrated its fitness‐for‐purpose regarding domain coverage for annotating culture‐related concepts mentioned in text corpora. This ontology can underpin computational models for making sense of UGC.

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“…In particular, ontology-based approaches have been widely used for context modeling and management [8][9][10][11][12]. A variety of ontologies have been adopted to model knowledge about subject domains, users, resources, and other contextual elements of the user surroundings [8,[29][30][31][32][33][34]. In such setups, reasoning techniques are usually applied on metadata derived from a single ontology.…”

Section: Background and Related Workmentioning

confidence: 99%

Context-aware recommender for mobile learners

Benlamri

Zhang

2014

Hum. Cent. Comput. Inf. Sci.

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As mobile technologies become widespread, new challenges are facing the research community to develop lightweight learning services adapted to the learner's profile, context, and task at hand. This paper attempts to solve some of these challenges by proposing a knowledge-driven recommender for mobile learning on the Semantic Web. The contribution of this work is an approach for context integration and aggregation using an upper ontology space and a unified reasoning mechanism to adapt the learning sequence and the learning content based on the learner's activity, background, used technology, and surrounding environment. Whenever context change occurs, the system identifies the new contextual features and translates them into new adaptation constraints in the operating environment. The proposed system has been implemented and tested on various mobile devices. The experimental results show many learning scenarios to demonstrate the usefulness of the system in practice.

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Supporting domain experts to construct conceptual ontologies: A holistic approach

Cited by 31 publications

References 3 publications

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

From Interactive Open Learner Modelling to Intelligent Mentoring: STyLE-OLM and Beyond

Ontology for cultural variations in interpersonal communication: Building on theoretical models and crowdsourced knowledge

Context-aware recommender for mobile learners

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