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The role that ontologies play or can play in designing and employing semantic technologies has been widely acknowledged by the Semantic Web and Linked Data communities. But the level of collaboration between these communities and the Applied Ontology community has been much less than expected. And ontologies and ontological techniques appear to be of marginal use in Big Data and its applications. To understand this situation and foster greater collaboration, Ontology Summit 2014 brought together representatives from the Semantic Web, Linked Data, Big Data and Applied Ontology communities, to address three basic problems involving applied ontology and these communities: (1) The role of ontologies [in these communities], (2) Current uses of ontologies in these communities, and (3) Engineering of ontologies and semantic integration. The intent was to identify and understand: (a) causes and challenges (e.g. scalability) that hinder reuse of ontologies in SW and LD, (b) solutions that can reduce the differences between ontologies on and off line, and (c) solutions to overcome engineering bottlenecks in current Semantic Web and Big Data applications. Over the past four months, presentations from, and discussions with, representatives of the Semantic Web, Linked Data, and Applied Ontology communities have taken place across four tracks. Each Track focused on different aspects of this year?s Summit topic: (Track A) Investigation of sharable and reusable ontologies; (Track B) Tools, services and techniques for a comprehensive and effective use of ontologies; (Track C) Investigation of the engineering bottlenecks and the ways to prevent and overcome them; (Track D) Enquiry on the variety problem in Big Data. In addition to the four Tracks? activities there was a Hackathon. Six different Hackathon projects took place, all available at their individual project public repositories. An online Community Library and an online Ontology Repository have been created as freely accessible Community resources. This Ontology Summit 2014 Communique presents a summary of the results, original in its attempt both to merge different communities? discourses and to achieve consensus across the Summit participants with respect to open problems and recommendations to address them. Fil: Obrst, Leo. The MITRE Corporation; Estados Unidos Fil: Gruninger, Michael. University Of Toronto; Canadá Fil: Baclawski, Ken. Northeastern University; Estados Unidos Fil: Bennett, Mike. Hypercube Ltd.; Reino Unido Fil: Brickley, Dan. Google; Reino Unido Fil: Berg Cross, Gary. Knowledge Strategies; Estados Unidos Fil: Hitzler, Pascal. Wright State University; Estados Unidos Fil: Janowicz, Krzysztof. University of California; Estados Unidos Fil: Kapp, Christine. Hypercube Ltd; Estados Unidos Fil: Kutz, Oliver. Otto von Guericke University Magdeburg; Alemania Fil: Lange, Christoph. University of Bonn; Alemania Fil: Levenchuk, Anatoly. TechInvestLab.ru; Rusia Fil: Quattri, Francesca. The Hong Kong Polytechnic University; Hong Kong Fil: Rector, Alan. University Of Manchester; Reino Unido Fil: Schneider, Todd. PDS, Inc.; Estados Unidos Fil: Spero, Simon. University Of North Carolina; Estados Unidos Fil: Thessen, Anne. Arizona State University; Estados Unidos Fil: Vegetti, Maria Marcela. Consejo Nacional de Investigaciones Científicas y Técnicas. Centro Científico Tecnológico Santa Fe. Instituto de Desarrollo y Diseño (i); Argentina Fil: Vizedom, Amanda. Criticollab; Estados Unidos Fil: Westerinen, Andrea. Nine Points Solutions; Estados Unidos Fil: West, Matthew. Information Junction; Reino Unido Fil: Yim, Peter. CIM Engineering, Inc.; Estados Unidos

Symbolic Systems in Artificial Intelligence which are based on formal logic and deductive reasoning are fundamentally different from Artificial Intelligence systems based on artificial neural networks, such as deep learning approaches. The difference is not only in their inner workings and general approach, but also with respect to capabilities. Neural-symbolic Integration, as a field of study, aims to bridge between the two paradigms. In this paper, we will discuss neural-symbolic integration in its relation to the Semantic Web field, with a focus on promises and possible benefits for both, and report on some current research on the topic.

Knowledge graph embeddings are now a widely adopted approach to knowledge representation in which entities and relationships are embedded in vector spaces. In this chapter, we introduce the reader to the concept of knowledge graph embeddings by explaining what they are, how they can be generated and how they can be evaluated. We summarize the state-of-the-art in this field by describing the approaches that have been introduced to represent knowledge in the vector space. In relation to knowledge representation, we consider the problem of explainability, and discuss models and methods for explaining predictions obtained via knowledge graph embeddings.