MECUREO ontology and modelling tools

Apted, Trent; Kay, Judy

doi:10.1504/ijceell.2004.004969

Cited by 7 publications

(3 citation statements)

References 15 publications

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“…For example, in personalised teaching systems, a similar approach might be used to capture the different conceptualisations held by a teacher and each of their students. We have explored ways to automatically extract ontologies from sources such as teaching metadata (Holden and Kay 2002) and online dictionaries (Apted and Kay 2004). In the latter case, we needed to adapt the ontology to the particular teaching needs within a course (Kay and Lum 2004).…”

Section: Discussionmentioning

confidence: 99%

PERSONAF: framework for personalised ontological reasoning in pervasive computing

Niu

Kay

2009

User Model User-Adap Inter

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Pervasive computing creates possibilities for presenting highly personalised information about the people, places and things in a building. One of the challenges for such personalisation is the creation of the system that can support ontological reasoning for several key tasks: reasoning about location; personalisation of information about location at the right level of detail; and personalisation to match each person's conceptions of the building based on their own use of it and their relationship to other people in the building. From pragmatic perspectives, it should be inexpensive to create the ontology for each new building. It is also critical that users should be able to understand and control pervasive applications. We created the PERSONAF (personalised pervasive scrutable ontological framework) to address these challenges. PERSONAF is a new abstract framework for pervasive ontological reasoning. We report its evaluation at three levels. First, we assessed the power of the ontology for reasoning about noisy and uncertain location information, showing that PERSONAF can improve location modelling. Notably, the best ontological reasoner varies across users. Second, we demonstrate the use of the PERSONAF framework in Adaptive Locator, an application built upon it, using our low cost mechanisms for non-generic layers of the ontology. Finally, we report a user study, which evaluated the PERSONAF approach as seen by users in the Adaptive Locator. We assessed both the personalisation performance and the understandability of explanations of the system reasoning. Together, these three evaluations show that the PERSONAF approach supports building of low cost ontologies, that can achieve flexible ontological reasoning 123 2 W. T. Niu, J. Kay about smart buildings and the people in them, and that this can be used to build applications which give personalised information that can provide understandable explanations of the reasoning underlying the personalisation.

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Section: Discussionmentioning

confidence: 99%

PERSONAF: framework for personalised ontological reasoning in pervasive computing

Niu

Kay

2009

User Model User-Adap Inter

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“…In Scenario 1, this means the system should be able to explain the meaning of the part of the model for the user's knowledge of the undo command. This goal motivated our work to create a scrutable lightweight ontology based on analysis of available online dictionaries [Apted and Kay 2004]. We used this to generate the ontology of a user model and for visualizing it [Apted et al 2003].…”

Section: Representation Of the User Modelmentioning

confidence: 99%

Creating personalized systems that people can scrutinize and control

Kay

Kummerfeld

2012

ACM Trans. Interact. Intell. Syst.

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Widespread personalized computing systems play an already important and fast-growing role in diverse contexts, such as location-based services, recommenders, commercial Web-based services, and teaching systems. The personalization in these systems is driven by information about the user, a user model. Moreover, as computers become both ubiquitous and pervasive, personalization operates across the many devices and information stores that constitute the user's personal digital ecosystem. This enables personalization, and the user models driving it, to play an increasing role in people's everyday lives. This makes it critical to establish ways to address key problems of personalization related to privacy, invisibility of personalization, errors in user models, wasted user models, and the broad issue of enabling people to control their user models and associated personalization. We offer scrutable user models as a foundation for tackling these problems.This article argues the importance of scrutable user modeling and personalization, illustrating key elements in case studies from our work. We then identify the broad roles for scrutable user models. The article describes how to tackle the technical and interface challenges of designing and building scrutable user modeling systems, presenting design principles and showing how they were established over our twenty years of work on the Personis software framework. Our contributions are the set of principles for scrutable personalization linked to our experience from creating and evaluating frameworks and associated applications built upon them. These constitute a general approach to tackling problems of personalization by enabling users to scrutinize their user models as a basis for understanding and controlling personalization.

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“…Many research projects tried to propose a solution for it with different degrees of completion (e.g. [1], [2], [3], [4]). Brusilovsky and Henze in [5] presented a comprehensive overview of the problem and draw the evolution of research addressing it.…”

Section: Introductionmentioning

confidence: 99%

Adaptation “in the Wild”: Ontology-Based Personalization of Open-Corpus Learning Material

Sosnovsky

Hsiao

Brusilovsky

2012

Lecture Notes in Computer Science

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Abstract.Teacher and students can use WWW as a limitless source of learning material for nearly any subject. Yet, such abundance of content comes with the problem of finding the right piece at the right time. Conventional adaptive educational systems cannot support personalized access to open-corpus learning material as they rely on manually constructed content models. This paper presents an approach to this problem that does not require intervention from a human expert. The approach has been implemented in an adaptive system that recommends students supplementary reading material and adaptively annotates it. The results of the evaluation experiment have demonstrated several significant effects of using the system on students' learning.

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MECUREO ontology and modelling tools

Cited by 7 publications

References 15 publications

PERSONAF: framework for personalised ontological reasoning in pervasive computing

PERSONAF: framework for personalised ontological reasoning in pervasive computing

Creating personalized systems that people can scrutinize and control

Adaptation “in the Wild”: Ontology-Based Personalization of Open-Corpus Learning Material

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