{"id":890,"date":"2018-01-22T13:34:39","date_gmt":"2018-01-22T12:34:39","guid":{"rendered":"\/?page_id=890"},"modified":"2018-05-09T15:46:57","modified_gmt":"2018-05-09T13:46:57","slug":"tutorials-workshops","status":"publish","type":"page","link":"\/program\/tutorials-workshops\/","title":{"rendered":"Tutorials & Workshops"},"content":{"rendered":"
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Tutorial:\u00a0Executing Knowledge Graph Initiatives in Organizations – A Field Guide<\/h2>\n
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Author:\u00a0<\/strong>Panos Alexopoulos<\/p>\n

\u0410bstract:\u00a0<\/strong>Ever since Google announced that \u201ctheir knowledge graph allowed searching for things, not strings\u201d, the term \u201cknowledge graph\u201d has been widely adopted to denote any graph-like network of interrelated typed entities and concepts that can be used to integrate, share and exploit data and knowledge in one or more domains.\u00a0 Apart from Google, knowledge graphs are found and developed within several prominent companies, including Microsoft, Apple, LinkedIn, Amazon and others, as an enabling technology for data integration and analytics, semantic search and question answering, and other cognitive applications. In this tutorial I describe the technical, business and organizational dimensions and challenges that Knowledge Graph Architects need to be aware of before launching a Knowledge Graph initiative in an organization. More importantly, I provide a framework to guide the successful execution of a knowledge graph project, combining state-of-the-art techniques with practical advice and lessons learned from real-world case studies.<\/p>\n

website:<\/strong> http:\/\/www.panosalexopoulos.com\/executing-knowledge-graph-initiatives-in-organizations-a-field-guide\/<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div><\/div><\/div>

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Tutorial: Music Knowledge Graph and Deep-Learning Based Recommender Systems<\/h2>\n
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Author:\u00a0<\/strong>Pasquale Lisena and Rapha\u00ebl Troncy<\/p>\n

\u0410bstract:\u00a0<\/strong>Music information can be very complex. Describing a classical masterpiece in all its form (the composition, the score, the various publications, a performance, a recording, the derivative works, etc.) is a complex activity. In the context of the DOREMUS research project, we develop tools and methods to exploit music catalogues on the web using semantic web technologies.
\nIn the first part of this tutorial, we will present models and vocabularies for representing fine-grained information about music, making it a powerful resource for answering music specific questions which are of interest for musicologists, librarians, concert hall or programmers. In the second part of this tutorial, we will present methods and datasets for training recommendation engines. From a music information point of view, we will touch topics like how to build entity embeddings, how to select similarity measures, how to tune recommender systems and provide explanation of the recommendation to the final user. During the tutorial, we will propose several hands-on for the audience to play with the DOREMUS datasets and tools.<\/p>\n

website:\u00a0<\/strong>https:\/\/doremus-anr.github.io\/eswc18_tutorial\/<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div><\/div><\/div>

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Tutorial: How to build a Question Answering system overnight<\/h2>\n
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Author:\u00a0<\/strong>Andreas Both, Denis Lukovnikov, Gaurav Maheshwari, Ioanna Lytra, Jens Lehmann, Kuldeep Singh, Mohnish Dubey, Priyansh Trivedi<\/p>\n

\u0410bstract:\u00a0<\/strong>With this tutorial, we aim to provide the participants with an overview of the field of Question Answering, insights into commonly faced problems, its recent trends, and developments. At the end of the tutorial, the audience would have hands-on experience of developing two working QA<\/p>\n

systems- one based on rule-based semantic parsing, and another, a deep learning based method. In doing so, we hope to provide a suitable entry point for the people new to this field and ease their process of making informed decisions while creating their own QA systems.<\/p>\n

website:\u00a0<\/strong>http:\/\/qatutorial.sda.tech\/<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div><\/div><\/div>

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Tutorial: From heterogeneous data to RDF graphs and back<\/h2>\n

Author:\u00a0<\/strong>Olivier Corby, Catherine Faron Zucker, Maxime Lefran\u00e7ois and Antoine Zimmermann<\/p>\n

\u0410bstract:\u00a0<\/strong>It is commonly understood by developers that the adoption of the Semantic Web models and technologies are enablers for semantic interoperability on the Web and the Web of Things, but that their adoption is bound to that of RDF data formats. True, the RDF data model may be used as a lingua franca to reach semantic interoperability and integration and querying of data having heterogeneous formats. The topic of this tutorial is SPARQL-Generate<\/a>\u00a0and STTL<\/a> (), that both contribute to making the choice of a data format and that of a data model orthogonal.<\/p>\n

SPARQL-Generate is an extension of SPARQL for querying not only RDF datasets but also documents in arbitrary formats. It offers a simple template-based option to generate RDF Graphs from documents in heterogeneous formats. SPARQL Template Transformation Language (STTL) is an extension of SPARQL which enables Semantic Web developers to support the many cases where they need to transform RDF data. It enables them to write specific yet compact RDF transformers toward other languages and formats, including RDF itself. Combining SPARQL-Generate and STTL enables users to develop a new variety of applications where RDF is used as a pivot language in Web applications requiring heterogeneous data transformation processes.<\/p>\n

website:<\/strong>\u00a0https:\/\/eswc2018-sparql-ext.github.io\/tutorial\/<\/a><\/p>\n<\/div>\n\t\t<\/div><\/div><\/div><\/div>

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Workshop: 2nd Workshop on Semantic Web solutions for large-scale biomedical data analytics (SeWeBMeDA)<\/h2>\n
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Authors:\u00a0<\/strong>Ali Hasnain, Oya Beyan, Stefan Decker and Dietrich Rebholz-Schuhmann<\/p>\n

\u0410bstract:\u00a0<\/strong>The life sciences domain has been an early adopter of linked data and, a considerable portion of the Linked Open Data cloud is composed of life sciences data sets. The deluge of inflowing biomedical data, partially driven by high-throughput gene sequencing technologies, is a key contributor and motor to these developments. The available data sets require integration according to international standards, large-scale distributed infrastructures, specific techniques for data access, and offer data analytics benefits for decision support.\u00a0 Especially in combination with Semantic Web and Linked Data technologies, these promises to enable the processing of large as well as semantically heterogeneous data sources and the capturing of new knowledge from those.<\/p>\n

This workshop invites papers for life sciences and biomedical data processing, as well as the amalgamation with Linked Data and Semantic Web technologies for better data analytics, knowledge discovery and user-targeted applications. This research contribution should provide useful information for the Knowledge Acquisition research community as well as the working Data Scientist. This workshop at the Extended Semantic Web Conference (ESWC) seeks original contributions describing theoretical and practical methods and techniques that present the anatomy of large scale linked data infrastructure, which covers: the distributed infrastructure to consume, store and query large volumes of heterogeneous linked data; using indexes and graph aggregation to better understand large linked data graphs, query federation to mix internal and external data-sources, and linked data visualisation tools for health care and life sciences. It will further cover topics around data integration, data profiling, data curation, querying, knowledge discovery, ontology mapping \/ matching \/ reconciliation and data \/ ontology visualisation, applications \/ tools \/ technologies \/ techniques for life sciences and biomedical domain. SeWeBMeDA aims to provide researchers in biomedical and life science, an insight and awareness about large scale data technologies for linked data, which are becoming increasingly important for knowledge discovery in the life sciences domain.<\/p>\n

Topics of interest include, but are not limited to Semantic Web and Linked Data technologies in the following areas:<\/p>\n

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