KEER2022

Avanttítol: KEER2022. DiversitiesDescripció del recurs: 25 juliol 202

A Generic architecture for semantic enhanced tagging systems

The Social Web, or Web 2.0, has recently gained popularity because of its low cost and ease of use. Social tagging sites (e.g. Flickr and YouTube) offer new principles for end-users to publish and classify their content (data). Tagging systems contain free-keywords (tags) generated by end-users to annotate and categorise data. Lack of semantics is the main drawback in social tagging due to the use of unstructured vocabulary. Therefore, tagging systems suffer from shortcomings such as low precision, lack of collocation, synonymy, multilinguality, and use of shorthands. Consequently, relevant contents are not visible, and thus not retrievable while searching in tag-based systems. On the other hand, the Semantic Web, so-called Web 3.0, provides a rich semantic infrastructure. Ontologies are the key enabling technology for the Semantic Web. Ontologies can be integrated with the Social Web to overcome the lack of semantics in tagging systems. In the work presented in this thesis, we build an architecture to address a number of tagging systems drawbacks. In particular, we make use of the controlled vocabularies presented by ontologies to improve the information retrieval in tag-based systems. Based on the tags provided by the end-users, we introduce the idea of adding “system tags” from semantic, as well as social, resources. The “system tags” are comprehensive and wide-ranging in comparison with the limited “user tags”. The system tags are used to fill the gap between the user tags and the search terms used for searching in the tag-based systems. We restricted the scope of our work to tackle the following tagging systems shortcomings: - The lack of semantic relations between user tags and search terms (e.g. synonymy, hypernymy), - The lack of translation mediums between user tags and search terms (multilinguality), - The lack of context to define the emergent shorthand writing user tags. To address the first shortcoming, we use the WordNet ontology as a semantic lingual resource from where system tags are extracted. For the second shortcoming, we use the MultiWordNet ontology to recognise the cross-languages linkages between different languages. Finally, to address the third shortcoming, we use tag clusters that are obtained from the Social Web to create a context for defining the meaning of shorthand writing tags. A prototype for our architecture was implemented. In the prototype system, we built our own database to host videos that we imported from real tag-based system (YouTube). The user tags associated with these videos were also imported and stored in the database. For each user tag, our algorithm adds a number of system tags that came from either semantic ontologies (WordNet or MultiWordNet), or from tag clusters that are imported from the Flickr website. Therefore, each system tag added to annotate the imported videos has a relationship with one of the user tags on that video. The relationship might be one of the following: synonymy, hypernymy, similar term, related term, translation, or clustering relation. To evaluate the suitability of our proposed system tags, we developed an online environment where participants submit search terms and retrieve two groups of videos to be evaluated. Each group is produced from one distinct type of tags; user tags or system tags. The videos in the two groups are produced from the same database and are evaluated by the same participants in order to have a consistent and reliable evaluation. Since the user tags are used nowadays for searching the real tag-based systems, we consider its efficiency as a criterion (reference) to which we compare the efficiency of the new system tags. In order to compare the relevancy between the search terms and each group of retrieved videos, we carried out a statistical approach. According to Wilcoxon Signed-Rank test, there was no significant difference between using either system tags or user tags. The findings revealed that the use of the system tags in the search is as efficient as the use of the user tags; both types of tags produce different results, but at the same level of relevance to the submitted search terms

A reference architecture for adaptive hypermedia applications

x+173hlm.;24c

Experts on e-learning: insights gained from listening to the student voice!

The Student Experience of e-Learning Laboratory (SEEL) project at the University of Greenwich was designed to explore and then implement a number of approaches to investigate learners’ experiences of using technology to support their learning. In this paper members of the SEEL team present initial findings from a University-wide survey of nearly a 1000 students. A selection of 90 ‘cameos’, drawn from the survey data, offer further insights into personal perceptions of e-learning and illustrate the diversity of students experiences. The cameos provide a more coherent picture of individual student experience based on the totality of each person’s responses to the questionnaire. Finally, extracts from follow-up case studies, based on interviews with a small number of students, allow us to ‘hear’ the student voice more clearly. Issues arising from an analysis of the data include student preferences for communication and social networking tools, views on the ‘smartness’ of their tutors’ uses of technology and perceptions of the value of e-learning. A primary finding and the focus of this paper, is that students effectively arrive at their own individualised selection, configuration and use of technologies and software that meets their perceived needs. This ‘personalisation’ does not imply that such configurations are the most efficient, nor does it automatically suggest that effective learning is occurring. SEEL reminds us that learners are individuals, who approach learning both with and without technology in their own distinctive ways. Hearing, understanding and responding to the student voice is fundamental in maximising learning effectiveness. Institutions should consider actively developing the capacity of academic staff to advise students on the usefulness of particular online tools and resources in support of learning and consider the potential benefits of building on what students already use in their everyday lives. Given the widespread perception that students tend to be ‘digital natives’ and academic staff ‘digital immigrants’ (Prensky, 2001), this could represent a considerable cultural challenge

Improving Recall of Browsing Sets in Image Retrieval from a Semiotics Perspective

The purpose of dissertation is to utilize connotative messages for enhancing image retrieval and browsing. By adopting semiotics as a theoretical tool, this study explores problems of image retrieval and proposes an image retrieval model. The semiotics approach conceptually demonstrates that: 1) a fundamental reason for the dissonance between retrieved images and user needs is representation of connotative messages, and 2) the image retrieval model which makes use of denotative index terms is able to facilitate users to browse connotatively related images effectively even when the users' needs are potentially expressed in the form of denotative query. Two experiments are performed for verifying the semiotic-based image retrieval model and evaluating the effectiveness of the model. As data sources, 5,199 records are collected from Artefacts Canada: Humanities by Canadian Heritage Information Network, and the candidate terms of connotation and denotation are extracted from Art & Architecture Thesaurus. The first experiment, by applying term association measures, verifies that the connotative messages of an image can be derived from denotative messages of the image. The second experiment reveals that the association thesaurus which is constructed based on the associations between connotation and denotation facilitates assigning connotative terms to image documents. In addition, the result of relevant judgments presents that the association thesaurus improves the relative recall of retrieved image documents as well as the relative recall of browsing sets. This study concludes that the association thesaurus indicating associations between connotation and denotation is able to improve the accessibility of the connotative messages. The results of the study are hoped to contribute to the conceptual knowledge of image retrieval by providing understandings of connotative messages within an image and to the practical design of image retrieval system by proposing an association thesaurus which can supplement the limitations of the current content-based image retrieval systems (CBIR)

INSAM Journal of Contemporary Music, Art and Technology 2

The subject of machine learning and creativity, as well as its appropriation in arts is the focus of this issue with our Main theme of – Artificial Intelligence in Music, Arts, and Theory. In our invitation to collaborators, we discussed our standing preoccupation with the exploration of technology in contemporary theory and artistic practice. The invitation also noted that this time we are encouraged and inspired by Catherine Malabou’s new observations regarding brain plasticity and the metamorphosis of (natural and artificial) intelligence. Revising her previous stance that the difference between brain plasticity and computational architecture is not authentic and grounded, Malabou admits in her new book, Métamorphoses de l'intelligence: Que faire de leur cerveau bleu? (2017), that plasticity – the potential of neuron architecture to be shaped by environment, habits, and education – can also be a feature of artificial intelligence. “The future of artificial intelligence,” she writes, “is biological.” We wanted to provoke a debate about what machines can learn and what we can learn from them, especially regarding contemporary art practices. On this note, I am happy to see that our proposition has provoked intriguing and unique responses from various different disciplines including: theory of art, aesthetics of music, musicology, and media studies. The pieces in the (Inter)view section deal with machine and computational creativity, as well as the some of the principles of contemporary art. Reviews give us an insight into a couple of relevant reading points for this discussion and a retrospective of one engaging festival that also fits this theme

31th International Conference on Information Modelling and Knowledge Bases

Information modelling is becoming more and more important topic for researchers, designers, and users of information systems.The amount and complexity of information itself, the number of abstractionlevels of information, and the size of databases and knowledge bases arecontinuously growing. Conceptual modelling is one of the sub-areas ofinformation modelling. The aim of this conference is to bring together experts from different areas of computer science and other disciplines, who have a common interest in understanding and solving problems on information modelling and knowledge bases, as well as applying the results of research to practice. We also aim to recognize and study new areas on modelling and knowledge bases to which more attention should be paid. Therefore philosophy and logic, cognitive science, knowledge management, linguistics and management science are relevant areas, too. In the conference, there will be three categories of presentations, i.e. full papers, short papers and position papers