Topic
Knowledge representation and reasoning
About: Knowledge representation and reasoning is a research topic. Over the lifetime, 20078 publications have been published within this topic receiving 446310 citations. The topic is also known as: KR & KR².
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01 Dec 2010
TL;DR: It is concluded that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between the scientific work and the running system has made Bibsonomy a valuable platform for demonstrating and evaluating Web 2.0 research.
Abstract: Social resource sharing systems are central elements of the Web 2.0 and use the same kind of lightweight knowledge representation, called folksonomy. Their large user communities and ever-growing networks of user-generated content have made them an attractive object of investigation for researchers from different disciplines like Social Network Analysis, Data Mining, Information Retrieval or Knowledge Discovery. In this paper, we summarize and extend our work on different aspects of this branch of Web 2.0 research, demonstrated and evaluated within our own social bookmark and publication sharing system BibSonomy, which is currently among the three most popular systems of its kind. We structure this presentation along the different interaction phases of a user with our system, coupling the relevant research questions of each phase with the corresponding implementation issues. This approach reveals in a systematic fashion important aspects and results of the broad bandwidth of folksonomy research like capturing of emergent semantics, spam detection, ranking algorithms, analogies to search engine log data, personalized tag recommendations and information extraction techniques. We conclude that when integrating a real-life application like BibSonomy into research, certain constraints have to be considered; but in general, the tight interplay between our scientific work and the running system has made BibSonomy a valuable platform for demonstrating and evaluating Web 2.0 research.
121 citations
TL;DR: In this paper, a general-purpose knowledge integration framework that employs Bayesian networks in integrating both low-level and semantic features is presented, and the efficacy of this framework is demonstrated via three applications involving semantic understanding of pictorial images.
121 citations
TL;DR: Two applications of ANATOM-TUTOR’s user model are described: tailoring hypertext to the level of knowledge of the individual user; and generating explanations and questions in a simulated examination situation, also taking into consideration the individual users’level of knowledge.
Abstract: This article is a comparative description of the user modelling component of ANATOM-TUTOR, an intelligent anatomy tutoring system for use at university level. We introduce ITSs in general, discussing some of the psychological and pedagogical issues involved in using computers in education, and ANATOM-TUTOR in parlicular, and locate ANATOM-TUTOR’s user modelling component in the field of existing user models. Details of the user model’s construction and maintenance, the knowledge representation techniques used in it, and its relation to the domain knowledge base are then discussed. Two applications of ANATOM-TUTOR’s user model are described: (1) tailoring hypertext to the level of knowledge of the individual user; and (2) generating explanations and questions in a simulated examination situation, also taking into consideration the individual user’s level of knowledge.
121 citations
TL;DR: A personalized ontology model is proposed for knowledge representation and reasoning over user profiles that learns ontological user profiles from both a world knowledge base and user local instance repositories.
Abstract: As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.
121 citations
TL;DR: How the ontology languages of the Semantic Web can lead directly to more powerful agent-based approaches to using services offered on the Web-that is, to the realization of that speaker's "science fiction" vision is shown.
Abstract: At a recent colloquium, a speaker referred to a "science fiction vision" that consisted of sets of agents running around the Web performing complex actions for their users. He argued that we were far from the day when this would be real and that the infrastructure was not in place to make this happen. While his latter assessment is accurate, the former is far too pessimistic. Furthermore, a crucial component of this infrastructure, a "standardized" Web ontology language, is starting to emerge. In this article, the author provides a few pointers to this emerging area and shows how the ontology languages of the Semantic Web can lead directly to more powerful agent-based approaches to using services offered on the Web-that is, to the realization of that speaker's "science fiction" vision.
121 citations