Improving Ontology Recommendation and Reuse in WebCORE by Collaborative Assessments

In this work, we present an extension of CORE [8], a tool for Collaborative Ontology Reuse and Evaluation. The system receives an informal description of a specific semantic domain and determines which ontologies from a repository are the most appropriate to describe the given domain. For this task, the environment is divided into three modules. The first component receives the problem description as a set of terms, and allows the user to refine and enlarge it using WordNet. The second module applies multiple automatic criteria to evaluate the ontologies of the repository, and determines which ones fit best the problem description. A ranked list of ontologies is returned for each criterion, and the lists are combined by means of rank fusion techniques. Finally, the third component uses manual user evaluations in order to incorporate a human, collaborative assessment of the ontologies. The new version of the system incorporates several novelties, such as its implementation as a web application; the incorporation of a NLP module to manage the problem definitions; modifications on the automatic ontology retrieval strategies; and a collaborative framework to find potential relevant terms according to previous user queries. Finally, we present some early experiments on ontology retrieval and evaluation, showing the benefits of our system

Cultural consequences of computing technology

Computing technology is clearly a technical revolution, but will most probably bring about a cultural revolution\ud as well. The effects of this technology on human culture will be dramatic and far-reaching. Yet, computers and\ud electronic networks are but the latest development in a long history of cognitive tools, such as writing and printing.\ud We will examine this history, which exhibits long-term trends toward an increasing democratization of culture,\ud before turning to today's technology. Within this framework, we will analyze the probable effects of computing on\ud culture: dynamical representations, generalized networking, constant modification and reproduction. To address the\ud problems posed by this new technical environment, we will suggest possible remedies. In particular, the role of\ud social institutions will be discussed, and we will outline the shape of new electronic institutions able to deal with the\ud information flow on the internet

Ontology-based process for recommending health websites

Website content quality is particularly relevant in the health domain. A common user needs to retrieve health information that is precise, reliable and relevant to his/her profile. Website recommendation systems are an aid to get high quality health-related web sites according to the user's needs. However, in practice, it is not always evident how to describe recommendation criteria for health website. The goal of this paper is to describe, by an ontology network, the criteria used by a health website recommendation process. This ontology network conceptualizes the different domains that are involved in the Salus Recommendation Project as a set of interrelated ontologies.Publicado en IFIP Advances in Information and Communication Technology book series (IFIPAICT, vol. 341).Laboratorio de Investigación y Formación en Informática Avanzad

Adaptive Information Visualization for Personalized Access to Educational Digital Libraries

Personalization is one of the emerging ways to increase the power of modern Digital Libraries. The Knowledge Sea II system presented in this paper explores social navigation support, an approach for providing personalized guidance within the open corpus of educational resources. Following the concepts of social navigation we have attempted to organize a personalized navigation support that is based on past learners’ interaction with the system. The study indicates that Knowledge Sea II became the students' primary tool for accessing the open corpus documents used in a programming course. The social navigation support implemented in this system was considered useful by students participating in the study of Knowledge Sea II. At the same time, some user comments indicated the need to provide more powerful navigational support, such as the ability to rank the usefulness of a page

Customer empowerment in tourism through Consumer Centric Marketing (CCM)

We explain Consumer Centric Marketing (CCM) and adopt this new technique to travel context. Benefits and disadvantages of the CCM are outlined together with warnings of typical caveats Value: CCM will be expected as the norm in the travel industry by customers of the future, yet it is only the innovators who gain real tangible benefits from this development. We outline current and future opportunities to truly place your customer at the centre and provide the organisation with some real savings/gains through the use of ICT Practical Implications: We offer tangible examples for travel industry on how to utilise this new technology. The technology is already available and the ICT companies are keen to establish ways how consumers can utilise it, i.e. by providing ‘content’ for these ICT products the travel industry can fully gain from these developments and also enhance consumers’ gains from it. This can result in more satisfied customers for the travel (as well as ICT) companies thus truly adopting the basic philosophy of marketin

Influence of Social Circles on User Recommendations

Recommender systems are powerful tools that filter and recommend content relevant to a user. One of the most popular techniques used in recommender systems is collaborative filtering. Collaborative filtering has been successfully incorporated in many applications. However, these recommendation systems require a minimum number of users, items, and ratings in order to provide effective recommendations. This results in the infamous cold start problem where the system is not able to produce effective recommendations for new users. In recent times, with escalation in the popularity and usage of social networks, people tend to share their experiences in the form of reviews and ratings on social media. The components of social media like influence of friends, users\u27 interests, and friends\u27 interests create many opportunities to develop solutions for sparsity and cold start problems in recommender systems. This research observes these patterns and analyzes the role of social trust in baseline social recommender algorithms SocialMF - a matrix factorization-based model, SocialFD - a model that uses distance metric learning, and GraphRec - an attention-based deep learning model. Through extensive experimentation, this research compares the performance and results of these algorithms on datasets that these algorithms were tested on and one new dataset using the evaluations metrics such as root mean squared error (RMSE) and mean absolute error (MAE). By modifying the social trust component of these datasets, this project focuses on investigating the impact of trust on performance of these models. Experimental results of this research suggest that there is no conclusive evidence on how trust propagation plays a major part in these models. Moreover, these models show slightly improved performance when supplied with modified trust data

Video Recommendation Using Social Network Analysis and User Viewing Patterns

With the meteoric rise of video-on-demand (VOD) platforms, users face the challenge of sifting through an expansive sea of content to uncover shows that closely match their preferences. To address this information overload dilemma, VOD services have increasingly incorporated recommender systems powered by algorithms that analyze user behavior and suggest personalized content. However, a majority of existing recommender systems depend on explicit user feedback in the form of ratings and reviews, which can be difficult and time-consuming to collect at scale. This presents a key research gap, as leveraging users' implicit feedback patterns could provide an alternative avenue for building effective video recommendation models, circumventing the need for explicit ratings. However, prior literature lacks sufficient exploration into implicit feedback-based recommender systems, especially in the context of modeling video viewing behavior. Therefore, this paper aims to bridge this research gap by proposing a novel video recommendation technique that relies solely on users' implicit feedback in the form of their content viewing percentages

STUDY: Socially Aware Temporally Causal Decoder Recommender Systems

Recommender systems are widely used to help people find items that are tailored to their interests. These interests are often influenced by social networks, making it important to use social network information effectively in recommender systems. This is especially true for demographic groups with interests that differ from the majority. This paper introduces STUDY, a Socially-aware Temporally caUsal Decoder recommender sYstem. STUDY introduces a new socially-aware recommender system architecture that is significantly more efficient to learn and train than existing methods. STUDY performs joint inference over socially connected groups in a single forward pass of a modified transformer decoder network. We demonstrate the benefits of STUDY in the recommendation of books for students who are dyslexic, or struggling readers. Dyslexic students often have difficulty engaging with reading material, making it critical to recommend books that are tailored to their interests. We worked with our non-profit partner Learning Ally to evaluate STUDY on a dataset of struggling readers. STUDY was able to generate recommendations that more accurately predicted student engagement, when compared with existing methods.Comment: 15 pages, 5 figure

NCBO Ontology Recommender 2.0: An Enhanced Approach for Biomedical Ontology Recommendation

Biomedical researchers use ontologies to annotate their data with ontology terms, enabling better data integration and interoperability. However, the number, variety and complexity of current biomedical ontologies make it cumbersome for researchers to determine which ones to reuse for their specific needs. To overcome this problem, in 2010 the National Center for Biomedical Ontology (NCBO) released the Ontology Recommender, which is a service that receives a biomedical text corpus or a list of keywords and suggests ontologies appropriate for referencing the indicated terms. We developed a new version of the NCBO Ontology Recommender. Called Ontology Recommender 2.0, it uses a new recommendation approach that evaluates the relevance of an ontology to biomedical text data according to four criteria: (1) the extent to which the ontology covers the input data; (2) the acceptance of the ontology in the biomedical community; (3) the level of detail of the ontology classes that cover the input data; and (4) the specialization of the ontology to the domain of the input data. Our evaluation shows that the enhanced recommender provides higher quality suggestions than the original approach, providing better coverage of the input data, more detailed information about their concepts, increased specialization for the domain of the input data, and greater acceptance and use in the community. In addition, it provides users with more explanatory information, along with suggestions of not only individual ontologies but also groups of ontologies. It also can be customized to fit the needs of different scenarios. Ontology Recommender 2.0 combines the strengths of its predecessor with a range of adjustments and new features that improve its reliability and usefulness. Ontology Recommender 2.0 recommends over 500 biomedical ontologies from the NCBO BioPortal platform, where it is openly available.Comment: 29 pages, 8 figures, 11 table