DBpedia is an international crowd-sourced community effort to extract structured information from Wikipedia, Wikidata and Wikimedia Commons and to make this information available on the Web as Linked Open Data. More than 150 DBpedia enthusiasts joined the 11th Community Meeting, which was co-located with the SEMANTiCS 2017 in Amsterdam. The success of the last community meetings and the increasing number of specific language chapters proves that the DBpedia community is constantly growing and gaining more and more significance and impact in the Semantic Web Community.
Harald Sack is Professor for Information Services Engineering at two of the most renowned research institutions in Europe: FIZ Karlsruhe and AIFB. He is a part of SEMANTiCS' research and innovation track program committee as well as of the conference's permanent advisory board. His publications include more than 130 papers in international journals and conferences and three standard textbooks on networking technologies. In this interview he speaks about the limited capabilities of search engines, the necessity of data being open and the coffee culture in Vienna. You have been working in many research areas such as semantic web technologies, knowledge representations, multimedia analysis & retrieval.
DBpedia is an international crowd-sourced community effort to extract structured information from Wikipedia, Wikidata and Wikimedia Commons and to make this information available on the Web as Linked Open Data. More than 120 DBpedia enthusiasts joined the 12th Community Meeting, which was co-located with the SEMANTiCS 2018 in Vienna, Austria. The success of the last community meetings and the increasing number of specific language chapters proves that the DBpedia community is constantly growing and gaining more and more significance and impact in the Semantic Web Community.
Are you hearing the term "Semantic Web" as often as you may have in the past? There's no denying the importance of the technologies, standards, concepts, and collaborations that define the Semantic Web proper and all that is affiliated with it or grown out of it. But if anything, the terms "Semantic Web" or "Semantic Web technologies" are receiving less attention, points out Amit Sheth, educator, researcher, and entrepreneur whose roles include being the executive director of Kno.e.sis--the Ohio Center of Excellence in Knowledge-enabled Computing. As we head into 2017, DATAVERSITY wanted to follow up the state of the Semantic Web and Semantic technologies (both standards-body related and not). In addition to Sheth, Michael Bergman, co-founder of knowledge-based Artificial Intelligence startup Cognonto (see our recent article here) and CEO of Structured Dynamics, and David Wood, CTO of 3 Round Stones, Director of Technology at Ephox TinyMCE and author of books including Linking Enterprise Data, also participated.
This paper presents REWOrD, an approach to compute semantic relatedness between entities in the Web of Data representing real word concepts. REWOrD exploits the graph nature of RDF data and the SPARQL query language to access this data. Through simple queries, REWOrD constructs weighted vectors keeping the informativeness of RDF predicates used to make statements about the entities being compared. The most informative path is also considered to further refine informativeness. Relatedness is then computed by the cosine of the weighted vectors. Differently from previous approaches based on Wikipedia, REWOrD does not require any prepro- cessing or custom data transformation. Indeed, it can lever- age whatever RDF knowledge base as a source of background knowledge. We evaluated REWOrD in different settings by using a new dataset of real word entities and investigate its flexibility. As compared to related work on classical datasets, REWOrD obtains comparable results while, on one side, it avoids the burden of preprocessing and data transformation and, on the other side, it provides more flexibility and applicability in a broad range of domains.