|Full Title:||Workshop on Natural Language Processing and Linked Open Data|
|Start Date:||12-Sep-2013 - 12-Sep-2013|
|Meeting Email:||click here to access email|
|Meeting Description:||Workshop on Natural Language Processing and Linked Open Data (NLP&LOD) (http://www.bultreebank.org/NLP&LOD/)
Collocated with RANLP 2013 (http://www.lml.bas.bg/ranlp2013/)
12 September 2013
In the last decade, the mainstream research in Natural Language Processing (NLP) both - in academic and industrial contexts - has focused primarily on statistical approaches, which have proved very competitive in view of textual data becoming vast in quantity, web-based in availability, highly semantic in representation, and dynamic in nature.
A somewhat less mainstream but still quite visible trend has focused on knowledge-rich approaches for NLP; this trend has typically complemented statistical approaches. Examples include using domain knowledge to enhance learning for high-quality automatic NLP in a given domain, adaptation of statistical modules to knowledge-rich structures, and hybrid mechanisms for language analysis and generation.
The Linked Open Data (LOD), understood as published structured data, which is interlinked and which builds upon standard Web technologies, such as HTTP and URIs, as well as on RDF-presented world facts datasets in various domains, has become a necessary component within all modern NLP-related tasks and applications since it provides large quantities of useful knowledge about people, facts, organizations, events, etc.
In a long-term development, we might expect that richer world knowledge would be available even beyond the current Linked Open Data (LOD) with respect to larger structured and interconnected data. This would include semantics that are richer in world facts and dynamic conceptual knowledge, on the one hand. On the other hand, the trends in NLP tools development show a strong movement from knowledge-poor towards knowledge-rich and hybrid language processing using deep grammars, deep language resources and handling big knowledge bases, such as DBPedia, FreeBase, GeoNames, FOAF, etc.
In this workshop, we build on the complementarity of the two pillars of Natural Language Processing - symbolic and probabilistic, further reinforced by exploring the recent advances in the area of Linked Open Data (LOD). Many contemporary applications rely on the mapping of big amounts of texts to world fact databases and ontologies. They also rely on explicating the various important relations among entities and events depending on the specific task and domain for research and industrial usage. Last, but not least, there exist semantic repositories and management systems, such as OWLIM (http://www.ontotext.com/owlim), that are highly scalable and support inference within big data.
Christian Chiarcos, Goethe-Universität Frankfurt am Main, Germany
Borislav Popov, Ontotext AD, Bulgaria
|Linguistic Subfield:||Computational Linguistics|
| This is a session of the following meeting:
Recent Advances in Natural Language Processing
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