The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a worldwide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes regular releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and thus make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

There has recently been an increased interest in named entity recognition and disambiguation systems at major conferences such as WWW, SIGIR, ACL, KDD, etc. However, most work has focused on algorithms and evaluations, leaving little space for implementation details. In this paper, we discuss some implementation and data processing challenges we encountered while developing a new multilingual version of DBpedia Spotlight that is faster, more accurate and easier to configure. We compare our solution to the previous system, considering time performance, space requirements and accuracy in the context of the Dutch and English languages. Additionally, we report results for 9 additional languages among the largest Wikipedias. Finally, we present challenges and experiences to foment the discussion with other developers interested in recognition and disambiguation of entities in natural language text.

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The LOD2 Stack is an integrated distribution of aligned tools which support the whole life cycle of Linked Data from extraction, authoring/creation via enrichment, interlinking, fusing to maintenance. The LOD2 Stack comprises new and substantially extended existing tools from the LOD2 project partners and third parties. The stack is designed to be versatile; for all functionality we define clear interfaces, which enable the plugging in of alternative third-party implementations. The architecture of the LOD2 Stack is based on three pillars: (1) Software integration and deployment using the Debian packaging system. (2) Use of a central SPARQL endpoint and standardized vocabularies for knowledge base access and integration between the different tools of the LOD2 Stack. (3) Integration of the LOD2 Stack user interfaces based on REST enabled Web Applications. These three pillars comprise the methodological and technological framework for integrating the very heterogeneous LOD2 Stack components into a consistent framework. In this article we describe these pillars in more detail and give an overview of the individual LOD2 Stack components. The article also includes a description of a real-world usage scenario in the publishing domain.

Recognizing mentions of Adverse Drug Reactions (ADR) in social media is challenging: ADR mentions are contextdependent and include long, varied and unconventional descriptions as compared to more formal medical symptom terminology. We use the CADEC corpus to train a recurrent neural network (RNN) transducer, integrated with knowledge graph embeddings of DBpedia, and show the resulting model to be highly accurate (93.4 F1). Furthermore, even when lacking high quality expert annotations, we show that by employing an active learning technique and using purpose built annotation tools, we can train the RNN to perform well (83.9 F1).

Over the years, Twitter has become one of the largest communication platforms providing key data to various applications such as brand monitoring, trend detection, among others. Entity linking is one of the major tasks in natural language understanding from tweets and it associates entity mentions in text to corresponding entries in knowledge bases in order to provide unambiguous interpretation and additional con- text. State-of-the-art techniques have focused on linking explicitly mentioned entities in tweets with reasonable success. However, we argue that in addition to explicit mentions i.e. The movie Gravity was more ex- pensive than the mars orbiter mission entities (movie Gravity) can also be mentioned implicitly i.e. This new space movie is crazy. you must watch it!. This paper introduces the problem of implicit entity linking in tweets. We propose an approach that models the entities by exploiting their factual and contextual knowledge. We demonstrate how to use these models to perform implicit entity linking on a ground truth dataset with 397 tweets from two domains, namely, Movie and Book. Specifically, we show: 1) the importance of linking implicit entities and its value addition to the standard entity linking task, and 2) the importance of exploiting contextual knowledge associated with an entity for linking their implicit mentions. We also make the ground truth dataset publicly available to foster the research in this new research area.Comment: This paper was accepted at the Extended Semantic Web Conference 2016 as a full research track pape

The GARSA system is freely available under GPL license (http://www.biowebdb.org/garsa/). For download requests visit http://www.biowebdb.org/garsa/ or contact Dr Alberto Dávila.

Abstract-In this paper we discuss the collection, semantic annotation and analysis of real-time social signals from microblogging data. We focus on users interested in analyzing social signals collectively for sensemaking. Our proposal enables flexibility in selecting subsets for analysis, alleviating information overload. We define an architecture that is based on state-ofthe-art Semantic Web technologies and a distributed publishsubscribe protocol for real time communication. In addition, we discuss our method and application in a scenario related to the health care reform in the United States.