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LIDER: Linked Data as an enabler of cross-media and multilingual content analytics for enterprises across Europe (LIDER)
Start date: Nov 1, 2013, End date: Dec 31, 2015 PROJECT  FINISHED 

The explosive growth of content in volume, velocity and variety on the Web demands new approaches to content analytics, addressing issues in large scale analysis and interpretation of heterogeneous data sets, originating in different media, human languages, jurisdictions, etc. Among these, language diversity in particular has become a ubiquitous aspect of the Web in light of increasing globalization. Recently, semantic-level, i.e., language- and media-independent, data analysis and representation methods such as those provided by Linked Data and Semantic Web technologies, have been introduced to provide innovative content analytics solutions for such heterogeneous, multilingual and multimedia content. An important missing component however, is the representation of language- and media-specific information that will be needed for interpreting such data correctly - across different media and across the increasing variety of human languages used nowadays on the Web. In this proposal we therefore aim at addressing this missing component by providing an ecosystem for the establishment of a new Linked Open Data (LOD) based ecosystem of free, interlinked, and semantically interoperable language (corpora, dictionaries, lexical and syntactic metadata, etc.) and media (image, video, etc. metadata) resources that will allow for free and open exploitation of such resources in multilingual, cross-media content analytics across the EU and beyond, with specific use cases in different industries.LIDER addresses three key objectives:1. To support multilingual and multimedia content analytics in enterprises.2. To provide a discussion platform for the development of novel LOD based multilingual, cross-media content analytics systems that will be able to use Free, Open and Interoperable language and media resources and services.3. To reach consensus on short and long term goals in LOD-based multilingual, cross-media content analytics in enterprises.
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