RELEASE OF sar-graph 3.0

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Resource Announcement - Sar-graph release v3.0
http://sargraph.dfki.de
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Changes at a glance:
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- links between sar-graphs and FrameNet on the pattern level
- integration of new results from pattern curation efforts
- word-level links to WordNet 3.0, in addition to the already existing WSD to BabelNet 2.5
- Lemon variants for sar-graphs, in addition to the ones for patterns
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The resource is available 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 contain information from various levels of abstraction, including semantic arguments of a target relation, content words, word senses, etc.; all of them needed to express and recognize an instance of the target relation. The nodes are connected by syntactic dependency structure relations and, implicitly via BabelNet, lexical semantic relations. 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.

The current sar-graph release (version 3.0) provides the results of a long-term pattern verification effort (Hennig et al., 2015). Furthermore, the results of resource linking endeavors are included (Krause et al., 2015), i.e., we have added links to WordNet (for sar-graph vertices) and to FrameNet (for patterns).

Sar-graphs are useful for relation extraction, question answering, textual entailment, and summarization, as well as for related downstream applications like computer-assisted language learning.

For a more detailed description see:

Hans Uszkoreit and Feiyu Xu (2013)
From Strings to Things -- SAR-Graphs: A New Type of Resource for Connecting Knowledge and Language
1st International Workshop on NLP and DBpedia (NLP&DBPedia), volume 1064, Sydney, NSW, Australia, CEUR Workshop Proceedings, 10/2013

Leonhard Hennig, Hong Li, Sebastian Krause, Feiyu Xu, Hans Uszkoreit (2015)
A Web-based Collaborative Evaluation Tool for Automatically Learned Relation Extraction Patterns  
Annual Meeting of the Association for Computational Linguistics (ACL), System Demonstrations, 2015

Sebastian Krause, Leonhard Hennig, Aleksandra Gabryszak, Feiyu Xu, Hans Uszkoreit (2015)
Sar-graphs: A Linked Linguistic Knowledge Resource Connecting Facts with Language
Workshop on Linked Data in Linguistics: Resources and Applications, co-located with the Annual Meeting of the Association for Computational Linguistics (LDL @   ACL), 2015 

Sebastian Krause, Hong Li, Feiyu Xu, Hans Uszkoreit (2012)
Large-Scale Learning of Relation-Extraction Rules with Distant Supervision from the Web 
11th International Semantic Web Conference (ISWC 2012)


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Release 3.0 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: Lemon format and specific xml schema (DTD provided)
* Format of sar-graphs: Lemon format specific xml schema (DTD provided)
* Resource-external links: BabelNet 2.5, WordNet 3.0, FrameNet 1.5
* API supports: reading and storing patterns and sar-graphs, accessing vertex
 and edge information of DARE patterns and sar-graphs, pattern visualization

Download: http://sargraph.dfki.de/download.html
Statistics: http://sargraph.dfki.de/statistics.html
More references: http://sargraph.dfki.de/publications.html
Feedback via email: sargraph@dfki.de
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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.


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PD Dr. habil. Feiyu Xu
DFKI Research Fellow

Senior Researcher 
Research Group Leader
Text Analytics

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@dfki.de

homepage: http://www.dfki.de/~feiyu

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