[NLP2RDF] Release of sar-graph 1.0

Wed Jul 16 13:22:01 CEST 2014

FYI,
Sebastian

On 15.07.2014 17:18, Feiyu Xu wrote:
> Apologies for cross-posting
> Please forward this message to colleagues in the areas of interest
>
> =============================
>     RESOURCE ANNOUNCEMENT
>     RELEASE OF sar-graph 1.0
>     http://sargraph.dfki.de <http://sargraph.dfki.de>
> =============================
>
> The following resource will be released on META-SHARE and is already 
> available as a pre-release at http://sargraph.dfki.de.
>
> A *sar-graph* is a graph containing linguistic knowledge at syntactic 
> and lexical semantic levels for a given language and target relation. 
> a sar-graph for a targeted relation assembles many linguistic patterns 
> that are used in texts to mention this relation.  The 
> term semantically associated relations graph was chosen since 
> the patterns may either express the target relation directly or by 
> expressing a semantically associated relation. The nodes in a 
> sar-graph are either semantic arguments of a target relation or 
> content words (to be more exact, their word senses) needed 
> to express/recognize an instance of the target relation. The nodes are 
> connected by two kinds of edges: syntactic dependency 
> structure relations and lexical semantic relations. Thus they are 
> labelled with dependency-structure tags provided by a parser or 
> lexical-semantic relation tags. A definition can be found in 
> (Uszkoreit and Xu, 2013). The individual patterns are assembled in one 
> graph per target relation for an easier combination of mentions 
> gathered across sentences, but all patterns could also be employed 
> individually.
>
> *From Strings to Things
> SAR-Graphs: A New Type of Resource for Connecting Knowledge and Language*
> Hans Uszkoreit and Feiyu Xu (2013)
> In Proceedings of 1st International Workshop on NLP and DBpedia 
> (NLP&DBPedia),  volume 1064, Sydney, NSW, Australia, CEUR 
> Workshop Proceedings, 10/2013
>
> The current sar-graph version 1.0 contains syntactic dependency 
> relations between content words. In future versions, we will integrate 
> lexical semantic relations between word senses.
>
> In the current version, the patterns have been automatically learned 
> by the web-scale version *Web-DARE* (Krause et al., 2012) of the 
> relation extraction system *DARE *(Xu et al., 2007) from dependency 
> structures obtained by parsing sentential  mentions of the target 
> relation. DARE patterns contain the content words that signal the 
> mentioned (semantically associated) relation and by the syntactic 
> dependencies that combine these words and link them with the phrases 
> representing the arguments of the target relation. Thus, a sar-graph 
> is composed of syntactic dependency graphs. Their edges 
> denote dependency relations. Each edge is labeled with the tag 
> the parser has assigned to the dependency. Vertices come in 
> two flavors: One type of vertices denotes a regular node in a 
> dependency structure, thus it is labeled with a word. Vertices of the 
> second type represent the slots for the arguments of the target 
> relation, instead of a word, they are labeled by the name of the 
> argument, e.g. Person_1. Several dependency parsers have been 
> employed, but the current set of sar-graphs is built from parsing 
> results of the MALT parser.
>
> Applications of sar-graphs are information extraction, question 
> answering and summarisation.
> The resource might also be useful for research on paraphrases, textual 
> entailment and syntactic variation within a language.
>
> Release 1.0 has the following properties:
>
> Language: English
> Number of target relations: 25
> Arity of relations: n-ary relations (2≤n≤5)
> Domains of relations: biographic information, corporations, awards
> Format of patterns: DARE patterns in lemon format and specific xml 
> schema (DTD provided)
> Format of sar-graphs: specific xml schema  (DTD provided)
> APIs: java api for reading and storing patterns and sar-graphs,
> java api for various use cases: getting and searching for vertex, edge 
> information of a DARE pattern and a sar-graph,
> java api for pattern visualization
>
> *Download *is available at: http://sargraph.dfki.de/download.html
> More *statistics* are available at: 
> http://sargraph.dfki.de/statistics.html
> More *references* can be found at: 
> http://sargraph.dfki.de/publications.html
> *Feedback* via email: sargraph at dfki.de <mailto:sargraph at dfki.de>
>
> sar-graphs were conceived and defined at DFKI LT-Lab Berlin and then 
> realized in
> a collaboration between DFKI LT-Lab and the BabelNet group at Sapienza 
> University of Rome.
>
> The development of sar-graphs is partially supported by
> • the German Federal Ministry of Education and Research (BMBF) through 
> the project Deependance (contract 01IW11003)
> • the project LUcKY, a Google Focused Research Award in the area of 
> Natural Language Understanding.
>
>
> Enjoy!
>
>
> Feiyu Xu
>
>
>
> ----------------------------------
> Dr. Feiyu Xu
>
> Senior Researcher
> Project Leader
>
> DFKI  Projektbüro Berlin
> Alt Moabit 91c
> D-10559 Berlin
> Germany
> Phone +49-30-23895-1812
> Sek      +49-30-23895-1800
> Fax      +49-30-23895-1810
>
>
> E-mail: feiyu at dfki.de <mailto:feiyu at dfki.de>
>
> homepage: http://www.dfki.de/~feiyu <http://www.dfki.de/%7Efeiyu>
>
>
> ------------------------------------------------------------
>
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>
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>
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>
> ------------------------------------------------------------
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-- 
Sebastian Hellmann
AKSW/NLP2RDF research group
Insitute for Applied Informatics (InfAI) and DBpedia Association
Events:
* *Sept. 1-5, 2014* Conference Week in Leipzig, including
** *Sept 2nd*, MLODE 2014
** *Sept 3rd*, 2nd DBpedia Community Meeting
** *Sept 4th-5th*, SEMANTiCS (formerly i-SEMANTICS) <http://semantics.cc/>
Venha para a Alemanha como PhD: http://bis.informatik.uni-leipzig.de/csf
Projects: http://dbpedia.org, http://nlp2rdf.org, 
http://linguistics.okfn.org, https://www.w3.org/community/ld4lt 
<http://www.w3.org/community/ld4lt>
Homepage: http://aksw.org/SebastianHellmann
Research Group: http://aksw.org
Thesis:
http://tinyurl.com/sh-thesis-summary
http://tinyurl.com/sh-thesis
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