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Platforms: These days it seems like every business wants to either build or become one. Methodology and tools for Multilingual Linked Data generation hands-on slides. Here is an overview of the content, aims and methodology of the different sections of the tutorial:. Modelling lexical resources on the Web of Data: the lemon model. We will present the usage of the model and show some of its practical applications.
After presenting the lemon model, we will do a hands-on exercise in modelling a small lexicon for a popular vocabulary, e.
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We will show how it can be configured to induce lexica in three different languages German, English, Spanish. In a hands-on session we will provide participants with the opportunity to create a lexicon for their favourite ontology in three languages. Methodology and tools for Multilingual Linked Data generation. In this session we will focus on the process of generating and publishing LD from a domain specific multilingual resource. The goal is to present the main activities of the LD generation process and some tools that can be used for multilingual LD generation and linking. An extension of the lemon model to support the representation of linguistic translations will be shown.
In the hands-on session, we will guide the participants in completing the creation of a linked data version of a multilingual resource e.
In the hands-on session, we will show participants how to convert to NIF, how to build NIF services and use the converted RDF resources for validation, linking, querying and reasoning. The current language explosion on the Web requires the ability to automatically analyze and understand text written in any language.
This task however is affected by the lexical ambiguity of language, an issue addressed by two key tasks: Multilingual Word Sense Disambiguation WSD , aimed at assigning meanings to word occurrences within text, and Entity Linking EL , a recent task focused on finding mentions of entities within text and linking them to a knowledge base. In this session we will present the most recent work on the topic.
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In a hands-on session, we will present Babelfy, a unified, multilingual WSD and EL system based on BabelNet, and show participants how to disambiguate and link text written in different languages, also producing multilingual linked data as output. His research interests include ontology matching, semantic measures, and multilinguality on the Semantic Web. His main research areas are: Linked Data, Digital Libraries and multilingual data management and integration on the Web. Tiziano Flati is a Ph. His research interests lie in the fields of Word Sense Disambiguation and Induction, taxonomy induction and large-scale data mining.
In particular, these are some key benefits of Linguistic LD: 1. Provide enhanced and more sophisticated navigation through multi-language data sets, 2.
Increase the visibility of linguistic data, 3. Support easier integration of linguistic information into research documents and other digital objects, 4.
Support easier integration of linguistic information with LOD datasets, enhancing the natural language description of those datasets, 5. Describe language resources in RDF and make them indexable by standard semantic search engines, and 7. In particular, the tutorial will tackle the following questions: How to represent rich multilingual lexical information beyond rdfs:label and associate it to ontologies and linked data?
How to represent and publish multilingual texts, annotations and corpora as linked data? How to generate multilingual linked data from data silos?
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