Building Domain Ontologies from Text for Educational Purposes

Zouaq, Amal; Nkambou, Roger; Frasson, Claude

doi:10.1007/978-3-540-75195-3_28

Cited by 20 publications

(22 citation statements)

References 11 publications

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“…Furthermore, all the information can be represented by a directed graph, enabling the creation of both a limpid visual representation for humans and a canonical data structure for software. Moreover, it maps straightforwardly on to semantic web representations such as OWL and RDF triples, as exploited in (Zouaq et al, 2006;Zouaq et al, 2007).…”

Section: Comparison With Gr and Parcmentioning

confidence: 99%

The Stanford typed dependencies representation

Marneffe

Manning

2008

Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation - CrossParser '08

751

621

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This paper examines the Stanford typed dependencies representation, which was designed to provide a straightforward description of grammatical relations for any user who could benefit from automatic text understanding. For such purposes, we argue that dependency schemes must follow a simple design and provide semantically contentful information, as well as offer an automatic procedure to extract the relations. We consider the underlying design principles of the Stanford scheme from this perspective, and compare it to the GR and PARC representations. Finally, we address the question of the suitability of the Stanford scheme for parser evaluation.

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Section: Comparison With Gr and Parcmentioning

confidence: 99%

The Stanford typed dependencies representation

Marneffe

Manning

2008

Coling 2008: Proceedings of the Workshop on Cross-Framework and Cross-Domain Parser Evaluation - CrossParser '08

751

621

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“…Case-based reasoning (CBR) can represent a solution to the problem of handling unknown input. One of the strengths [10] of this approach is that it is capable of providing a conclusion even for unknown cases due to a similarity function.…”

Section: Knowledge To Be Extractedmentioning

confidence: 99%

An Overview: Semantic Web based Education

Mathews¹,

Rao²

2011

IJCA

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The present educational systems are trying to incorporate the semantic web technologies, so as to provide an adaptable, personalized and an intelligent learning environment. The long term vision of Education is to provide AAAL: Anytime, Anywhere, Anybody Learning. This paper discusses the architecture of semantic web for the purpose education system which satisfies AAAL .It contains an overview of how ontology can represent the knowledge base which forms the backbone of the Semantic Web based Educational system, thus making the system useful to the learners. It also contains methods on how ontology construction can take place for a particular domain.

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“…Further, it should be restricted to a given domain of interest and therefore model concepts and relations that are relevant to a particular task or application domain. [14] This formal conceptualization can either be created manually [57,75] or automatically [95], typically by natural language processing or machine-learning techniques, and is used to reason about matching learning objects to a curriculum sequence.…”

Section: Metadata Approachesmentioning

confidence: 99%

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

Champaign

2010

Advances in Artificial Intelligence

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In this thesis, we present an artificial intelligence approach for tutoring students in environments where there is a large repository of possible learning objects (e.g. texts, videos). In particular, we advocate that students learn on the basis of past experiences of peers. This aligns with McCalla's proposed ecological approach for intelligent tutoring, where a learning object's history-of-use is retained and leveraged to instruct future students. We offer three distinct models that serve to deliver the required intelligent tutoring: (i) a curriculum sequencing algorithm selecting which learning objects to present to students based on benefits to knowledge obtained by similar peers (ii) a framework for peers to provide commentary on the learning objects they've experienced (annotations) together with an algorithm for reasoning about which annotations to present to students that incorporates modeling trust in annotators (i.e. their reputation) and ratings provided by students (votes for and against) for the annotations they have been shown (iii) an opportunity for peers to guide the growth of the corpus by proposing divisions of current objects, together with an algorithm for reasoning about which of these new objects should be offered to students in order to enhance their learning. All three components are validated as beneficial in improving the learning of students. This is first of all achieved through a novel approach of simulated student learning, designed to enable the tracking of the experiences of a very large number of peers with an extensive repository of objects, through the effective modeling of knowledge gains. This is also coupled with a preliminary study with human participants that confirms the value of our framework. In all, we offer a rich and varied role for peers in guiding the learning of students in intelligent tutoring environments, made possible by careful modeling of the students who are being taught and of the potential benefits to learning that would be derived with the selection of appropriate tutorial content.iii Acknowledgements First and foremost I would like to thank my supervisor, Professor Robin Cohen, for guiding me through the completion of this dissertation and my PhD studies. Robin overwhelmed me with the amount of time and effort she put in to helping me complete my doctoral work. My committee members, Professor Edward Lank and Professor Peter van Beek, have provided detailed and helpful feedback throughout the process, which I greatly appreciate. Thanks as well to Professor Gord McCalla for his helpful advice. My appreciation goes out to the anonymous reviewers who considered earlier versions of this work and provided feedback during conference and journal submissions over the last 4 years.I would like to acknowledge Ahmed Ataullah, Simina Brânzei, John Doucette and Lachlan Dufton for being wonderful officemates: always willing to talk through issues I was working on, go for coffee and share a laugh. I greatly enjoyed working in the AI Group at the University of ...

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Building Domain Ontologies from Text for Educational Purposes

Cited by 20 publications

References 11 publications

The Stanford typed dependencies representation

The Stanford typed dependencies representation

An Overview: Semantic Web based Education

Peer-Based Intelligent Tutoring Systems: A Corpus-Oriented Approach

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