OntoBrowse: A World of Knowledge

This paper describes the creation and function of OntoBrowse, a domain-independent ontology browser system that was developed to provide generic access to any triplestore ontology without the need to create a bespoke interface. It features support for accessing multiple triplestores in one query session, bookmarks, Rendezvous sharing of bookmarks, multiple tabs, multiple windows, namespace caching and automatic generation of RDQL queries. OntoBrowse automatically loads images when referenced by URI and has a fully customisable user interface. In addition, the CIA World Factbook was asserted into a triplestore in order to gain a conceptual understanding of knowledge systems and for use as a controllable testing ground for the ontology browser

Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions: supplementary materials

These supplementary materials accompany the report ‘Web 2.0 technologies for learning: the current landscape – opportunities, challenges and tensions’, which is the first report from research commissioned by Becta into Web 2.0 technologies for learning at Key Stages 3 and 4. This report describes findings from the commissioned literature review of the then current landscape concerning learner use of Web 2.0 technologies and the implications for teachers, schools, local authorities and policy makers

User modeling for exploratory search on the Social Web. Exploiting social bookmarking systems for user model extraction, evaluation and integration

Exploratory search is an information seeking strategy that extends be- yond the query-and-response paradigm of traditional Information Retrieval models. Users browse through information to discover novel content and to learn more about the newly discovered things. Social bookmarking systems integrate well with exploratory search, because they allow one to search, browse, and filter social bookmarks. Our contribution is an exploratory tag search engine that merges social bookmarking with exploratory search. For this purpose, we have applied collaborative filtering to recommend tags to users. User models are an im- portant prerequisite for recommender systems. We have produced a method to algorithmically extract user models from folksonomies, and an evaluation method to measure the viability of these user models for exploratory search. According to our evaluation web-scale user modeling, which integrates user models from various services across the Social Web, can improve exploratory search. Within this thesis we also provide a method for user model integra- tion. Our exploratory tag search engine implements the findings of our user model extraction, evaluation, and integration methods. It facilitates ex- ploratory search on social bookmarks from Delicious and Connotea and pub- lishes extracted user models as Linked Data

Colombus: providing personalized recommendations for drifting user interests

The query formulationg process if often a problematic activity due to the cognitive load that it imposes to users. This issue is further amplified by the uncertainty of searchers with regards to their searching needs and their lack of training on effective searching techniques. Also, given the tremendous growth of the world wide web, the amount of imformation users find during their daily search episodes is often overwhelming. Unfortunatelly, web search engines do not follow the trends and advancements in this area, while real personalization features have yet to appear. As a result, keeping up-to-date with recent information about our personal interests is a time-consuming task. Also, often these information requirements change by sliding into new topics. In this case, the rate of change can be sudden and abrupt, or more gradual. Taking into account all these aspects, we believe that an information assistant, a profile-aware tool capable of adapting to users’ evolving needs and aiding them to keep track of their personal data, can greatly help them in this endeavor. Information gathering from a combination of explicit and implicit feedback could allow such systems to detect their search requirements and present additional information, with the least possible effort from them. In this paper, we describe the design, development and evaluation of Colombus, a system aiming to meet individual needs of the searchers. The system’s goal is to pro-actively fetch and present relevant, high quality documents on regular basis. Based entirely on implicit feedback gathering, our system concentrates on detecting drifts in user interests and accomodate them effectively in their profiles with no additional interaction from their side. Current methodologies in information retrieval do not support the evaluation of such systems and techniques. Lab-based experiments can be carried out in large batches but their accuracy often questione. On the other hand, user studies are much more accurate, but setting up a user base for large-scale experiments is often not feasible. We have designed a hybrid evaluation methodology that combines large sets of lab experiments based on searcher simulations together with user experiments, where fifteen searchers used the system regularly for 15 days. At the first stage, the simulation experiments were aiming attuning Colombus, while the various component evaluation and results gathering was carried out at the second stage, throughout the user study. A baseline system was also employed in order to make a direct comparison of Colombus against a current web search engine. The evaluation results illustrate that the Personalized Information Assistant is effective in capturing and satisfying users’ evolving information needs and providing additional information on their behalf

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest in the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web

BlogForever D5.2: Implementation of Case Studies

This document presents the internal and external testing results for the BlogForever case studies. The evaluation of the BlogForever implementation process is tabulated under the most relevant themes and aspects obtained within the testing processes. The case studies provide relevant feedback for the sustainability of the platform in terms of potential users’ needs and relevant information on the possible long term impact

Information Outlook, June 1997

Volume 1, Issue 6https://scholarworks.sjsu.edu/sla_io_1997/1005/thumbnail.jp

Semantic enrichment for enhancing LAM data and supporting digital humanities. Review article

With the rapid development of the digital humanities (DH) field, demands for historical and cultural heritage data have generated deep interest the data provided by libraries, archives, and museums (LAMs). In order to enhance LAM data’s quality and discoverability while enabling a self-sustaining ecosystem, “semantic enrichment” becomes a strategy increasingly used by LAMs during recent years. This article introduces a number of semantic enrichment methods and efforts that can be applied to LAM data at various levels, aiming to support deeper and wider exploration and use of LAM data in DH research. The real cases, research projects, experiments, and pilot studies shared in this article demonstrate endless potential for LAM data, whether they are structured, semi-structured, or unstructured, regardless of what types of original artifacts carry the data. Following their roadmaps would encourage more effective initiatives and strengthen this effort to maximize LAM data’s discoverability, use- and reuse-ability, and their value in the mainstream of DH and Semantic Web