Interactive Ontology-Based User Knowledge Acquisition: A Case Study

Aroyo, Lora; Denaux, Ronald; Dimitrova, Vania; Pye, Michael

doi:10.1007/11762256_41

Cited by 16 publications

(15 citation statements)

References 19 publications

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“…1. The formalization of the dialogue game can be expressed also as a 5-tuple (Aroyo et al 2005) < P, U, C, S, R > where -P are the pre-conditions necessary to trigger the game; -U are the strategies for selecting the concepts to exchange in the game; -Cis the focus, the set of all possible concepts that can be exchanged during the dialogue; -S are the scopes, a subset of focus concepts; -R are the post-conditions, the actions to perform at the end of the game.…”

Section: If (Belief < 07) Then {Communicativeacts_list = Addcommunimentioning

confidence: 99%

Integrating web service and semantic dialogue model for user models interoperability on the web

Cena

2010

J Intell Inf Syst

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Nowadays there is a great number of Web information systems that build a model of the user and adapt their services according to the needs and preferences maintained by the user model (UM). One of the most challenging issues of this scenario is the possibility to enable different systems to cooperate in order to exchange the available information about a user. Our aim is to create rich (and scalable) communication protocols and infrastructures to enable consumers and providers of UM data to interact. Our solution for dealing with such an issue is to exploit Web standards for interoperability (i.e. Semantic Web and Web Services) for implementing simple atomic communication, and a dialogue model for implementing enhanced communication capabilities. In particular, two systems can start a semantics-enhanced Dialogue Game as a form of negotiation to clarify the meaning of the requested concepts when a shared knowledge model does not exist, and to approximate the response when the exact one is not available. We propose a distributed semantic conversation framework based on the Sesame semantic environment for the exchange of user model knowledge on the Web. Systems have to expose their user model data as a Web Service, and to exploit a public dialogue knowledge base to start the dialogue. The main advantage of the approach is to allow systems to deal with difficult situations by starting an appropriate dialogue game instead of stopping the communication as in the traditional "all-or-nothing" Web Service approach. On the basis of a preliminary evaluation, the approach has shown an improvement of the adaptation results provided by the systems we tested.

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Section: If (Belief < 07) Then {Communicativeacts_list = Addcommunimentioning

confidence: 99%

Integrating web service and semantic dialogue model for user models interoperability on the web

Cena

2010

J Intell Inf Syst

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“…In education and training, the empowering role of Semantic Web technologies has been already acknowledged [1]. Since WWW is becoming a popular educational medium at schools, universities and professional training institutions, a prominent new stream of research on the Educational Semantic Web has been established.…”

Section: Introductionmentioning

confidence: 99%

“…Since WWW is becoming a popular educational medium at schools, universities and professional training institutions, a prominent new stream of research on the Educational Semantic Web has been established. Research studies already report semantic-based annotation and sharing of learning resources [1,11,12]. To further illustrate this potential, there is a need for implementing state-of-the-art Semantic Web technologies in large-scale applications that involve the semantic annotation of big collections of learning resources and their access and use from existing communities of users.…”

Section: Introductionmentioning

confidence: 99%

A Semantic Infrastructure to Support a Federation of Agricultural Learning Repositories

Manouselis

Abián

Carrión

et al. 2008

2008 Eighth IEEE International Conference on Advanced Learning Technologies

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“…There are adaptive systems designed to make personal recommendations by acquiring user's knowledge. In these systems, shortterm and long-term log of user actions were kept in layers of ontologies (Aroyo, Denaux, Dimitrova, & Pye, 2006;Aroyo, Dicheva, & Cristea, 2002). Using the information about content or user traces can be useful to make automated decisions on instruction, but this point of view misses a critical detail.…”

mentioning

confidence: 99%

“…Aroyo et al (2006, p. 573) discussed the need to "…focus on the development of a good classification of user's mismatches and patterns for clarification dialog based on systematic studies of empirical and computational approaches for ontology aligning and reconciliation. " Researchers strongly recommend future research on using ontology in E-learning systems and evaluating it with student input (Aroyo et al, 2006;Kholief, Nada, & Khedr, 2012;Kumaran & Sankar, 2013;Yalcinalp & Gulbahar, 2010). There are, however, no studies that have directly focused on addressing student achievement or competency by using actual data collected from end-users.…”

mentioning

confidence: 99%

Using Students’ Performance to Improve Ontologies for Intelligent E-Learning System

İçöz¹,

Sanalan²,

Özdemir³

et al. 2015

EDUC SCI-THEOR PRACT

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Ontologies have often been recommended for E-learning systems, but few efforts have successfully incorporated student data to represent knowledge conceptualizations. Defining key concepts and their relations between each other establishes the backbone of our E-learning system. The system guides an individual student through his/her course by evaluating their progress and suggesting instructional material to review based upon their answers. Three main tasks are performed within this framework: building ontologies for the course, measuring a student's understanding level for the concepts, and making personal suggestions to create an individualized learning environment. This paper presents: the integration of ontologies, assisted with student data, together with an intelligent Recommendation Module for the development of an E-learning system; the comparison and correction adaption of ontology from students' mind maps; and the assessment of students' actual weaknesses in comparison to what Recommendation Module suggests. The sample of 127 students, five classrooms, was conveniently selected among seventh grade students of a demographically average school in a major city in Turkey. The students' achievement was assessed and the scores for different questions were investigated for associations with concepts made in the students' minds. The results provided significant correlations among scores, and a fit model for the concepts represented by questions. The student suggested model slightly differed from the ontology map from the experts. Based on the data-supported model, the Recommendation Module more accurately determined the students' learning deficiencies and suggested concepts to be reviewed.

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Interactive Ontology-Based User Knowledge Acquisition: A Case Study

Cited by 16 publications

References 19 publications

Integrating web service and semantic dialogue model for user models interoperability on the web

Integrating web service and semantic dialogue model for user models interoperability on the web

A Semantic Infrastructure to Support a Federation of Agricultural Learning Repositories

Using Students’ Performance to Improve Ontologies for Intelligent E-Learning System

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