1,571 research outputs found
Automatic annotation of bioinformatics workflows with biomedical ontologies
Legacy scientific workflows, and the services within them, often present
scarce and unstructured (i.e. textual) descriptions. This makes it difficult to
find, share and reuse them, thus dramatically reducing their value to the
community. This paper presents an approach to annotating workflows and their
subcomponents with ontology terms, in an attempt to describe these artifacts in
a structured way. Despite a dearth of even textual descriptions, we
automatically annotated 530 myExperiment bioinformatics-related workflows,
including more than 2600 workflow-associated services, with relevant
ontological terms. Quantitative evaluation of the Information Content of these
terms suggests that, in cases where annotation was possible at all, the
annotation quality was comparable to manually curated bioinformatics resources.Comment: 6th International Symposium on Leveraging Applications (ISoLA 2014
conference), 15 pages, 4 figure
Reducing semantic complexity in distributed Digital Libraries: treatment of term vagueness and document re-ranking
The purpose of the paper is to propose models to reduce the semantic
complexity in heterogeneous DLs. The aim is to introduce value-added services
(treatment of term vagueness and document re-ranking) that gain a certain
quality in DLs if they are combined with heterogeneity components established
in the project "Competence Center Modeling and Treatment of Semantic
Heterogeneity". Empirical observations show that freely formulated user terms
and terms from controlled vocabularies are often not the same or match just by
coincidence. Therefore, a value-added service will be developed which rephrases
the natural language searcher terms into suggestions from the controlled
vocabulary, the Search Term Recommender (STR). Two methods, which are derived
from scientometrics and network analysis, will be implemented with the
objective to re-rank result sets by the following structural properties: the
ranking of the results by core journals (so-called Bradfordizing) and ranking
by centrality of authors in co-authorship networks.Comment: 12 pages, 4 figure
Hybrid human-AI driven open personalized education
Attaining those skills that match labor market demand is getting increasingly complicated as prerequisite knowledge, skills, and abilities are evolving dynamically through an uncontrollable and seemingly unpredictable process. Furthermore, people's interests in gaining knowledge pertaining to their personal life (e.g., hobbies and life-hacks) are also increasing dramatically in recent decades. In this situation, anticipating and addressing the learning needs are fundamental challenges to twenty-first century education. The need for such technologies has escalated due to the COVID-19 pandemic, where online education became a key player in all types of training programs. The burgeoning availability of data, not only on the demand side but also on the supply side (in the form of open/free educational resources) coupled with smart technologies, may provide a fertile ground for addressing this challenge. Therefore, this thesis aims to contribute to the literature about the utilization of (open and free-online) educational resources toward goal-driven personalized informal learning, by developing a novel Human-AI based system, called eDoer.
In this thesis, we discuss all the new knowledge that was created in order to complete the system development, which includes 1) prototype development and qualitative user validation, 2) decomposing the preliminary requirements into meaningful components, 3) implementation and validation of each component, and 4) a final requirement analysis followed by combining the implemented components in order develop and validate the planned system (eDoer).
All in all, our proposed system 1) derives the skill requirements for a wide range of occupations (as skills and jobs are typical goals in informal learning) through an analysis of online job vacancy announcements, 2) decomposes skills into learning topics, 3) collects a variety of open/free online educational resources that address those topics, 4) checks the quality of those resources and topic relevance using our developed intelligent prediction models, 5) helps learners to set their learning goals, 6) recommends personalized learning pathways and learning content based on individual learning goals, and 7) provides assessment services for learners to monitor their progress towards their desired learning objectives. Accordingly, we created a learning dashboard focusing on three Data Science related jobs and conducted an initial validation of eDoer through a randomized experiment. Controlling for the effects of prior knowledge as assessed by the pretest, the randomized experiment provided tentative support for the hypothesis that learners who engaged with personal eDoer recommendations attain higher scores on the posttest than those who did not. The hypothesis that learners who received personalized content in terms of format, length, level of detail, and content type, would achieve higher scores than those receiving non-personalized content was not supported as a statistically significant result
An Empirical Examination of the Associations between Social Tags and Web Queries
Introduction. We aim to discover the associations between social tags for a Web page and Web queries that would retrieve the same Webpage in three major search engines.
Method. 4,827 query terms were submitted to the three major search engines to acquire search engine results pages. A series of Perl scripts were written to read search engine results pages and to identify, analyse, and extract organic links
Analysis. Web pages from the organic links in search engine results pages were examined to see whether and how they had been tagged in Delicious. Only the Webpages tagged by at least 100 taggers were included in this study. The top thirty popular social tags used were harvested. The two sets of data were quantitatively analysed to investigate the research questions.
Results. At least 60% of search engines\u27 query terms overlapped with social tags in Delicious; higher ranked social tags were more likely to be used as query terms for the same Web resources; and the co-occurring pattern of query terms and social tags over social ranking resembled a power law distribution.
Conclusions. Socially tagged resources are likely to be highly ranked in search engine results pages. The findings can be applicable to the future study of Web resource related tasks such as Web searching and Web indexing
CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap
After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in
multimedia search engines, we have identified and analyzed gaps within European research effort during our second year.
In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio-
economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown
of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on
requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the
community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our
Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as
National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core
technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research
challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal
challenges
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