Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discover tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out nonrelevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

Use of Semantic Technology to Create Curated Data Albums

One of the continuing challenges in any Earth science investigation is the discovery and access of useful science content from the increasingly large volumes of Earth science data and related information available online. Current Earth science data systems are designed with the assumption that researchers access data primarily by instrument or geophysical parameter. Those who know exactly the data sets they need can obtain the specific files using these systems. However, in cases where researchers are interested in studying an event of research interest, they must manually assemble a variety of relevant data sets by searching the different distributed data systems. Consequently, there is a need to design and build specialized search and discovery tools in Earth science that can filter through large volumes of distributed online data and information and only aggregate the relevant resources needed to support climatology and case studies. This paper presents a specialized search and discovery tool that automatically creates curated Data Albums. The tool was designed to enable key elements of the search process such as dynamic interaction and sense-making. The tool supports dynamic interaction via different modes of interactivity and visual presentation of information. The compilation of information and data into a Data Album is analogous to a shoebox within the sense-making framework. This tool automates most of the tedious information/data gathering tasks for researchers. Data curation by the tool is achieved via an ontology-based, relevancy ranking algorithm that filters out non-relevant information and data. The curation enables better search results as compared to the simple keyword searches provided by existing data systems in Earth science

Flabase: towards the creation of a flamenco music knowledge base

Online information about flamenco music is scattered overdifferent sites and knowledge bases. Unfortunately, thereis no common repository that indexes all these data. Inthis work, information related to flamenco music is gath-ered from general knowledge bases (e.g., Wikipedia, DB-pedia), music encyclopedias (e.g., MusicBrainz), and spe-cialized flamenco websites, and is then integrated into anew knowledge base called FlaBase. As resources fromdifferent data sources do not share common identifiers, aprocess of pair-wise entity resolution has been performed.FlaBase contains information about 1,174 artists, 76pa-los(flamenco genres), 2,913 albums, 14,078 tracks, and771 Andalusian locations. It is freely available in RDF andJSON formats. In addition, a method for entity recognitionand disambiguation for FlaBase has been created. The sys-tem can recognize and disambiguate FlaBase entity refer-ences in Spanish texts with an f-measure value of 0.77. Weapplied it to biographical texts present in Flabase. By usingthe extracted information, the knowledge base is populatedwith relevant information and a semantic graph is createdconnecting the entities of FlaBase. Artists relevance is thencomputed over the graph and evaluated according to a fla-menco expert criteria. Accuracy of results shows a highdegree of quality and completeness of the knowledge base

A semantic knowledge base for personal learning and cloud learning environments

Personal Learning Environments (PLEs) and Cloud Learning Environments (CLEs) have recently encountered a rapid growth, as a response to the rising demand of learners for multi-sourced content and environments targeting their needs and preferences. This paper introduces a semantic knowledge base that utilises a multi-layered architecture consisting of learning ontologies customized for certain aspects of PLEs and CLEs. A number of stakeholder clusters, including learners, educators, and domain experts, are identified and are assigned distinct roles for the collaborative management of this knowledge base

Current Challenges and Visions in Music Recommender Systems Research

Music recommender systems (MRS) have experienced a boom in recent years, thanks to the emergence and success of online streaming services, which nowadays make available almost all music in the world at the user's fingertip. While today's MRS considerably help users to find interesting music in these huge catalogs, MRS research is still facing substantial challenges. In particular when it comes to build, incorporate, and evaluate recommendation strategies that integrate information beyond simple user--item interactions or content-based descriptors, but dig deep into the very essence of listener needs, preferences, and intentions, MRS research becomes a big endeavor and related publications quite sparse. The purpose of this trends and survey article is twofold. We first identify and shed light on what we believe are the most pressing challenges MRS research is facing, from both academic and industry perspectives. We review the state of the art towards solving these challenges and discuss its limitations. Second, we detail possible future directions and visions we contemplate for the further evolution of the field. The article should therefore serve two purposes: giving the interested reader an overview of current challenges in MRS research and providing guidance for young researchers by identifying interesting, yet under-researched, directions in the field