Paper Info
Title
Relation- and Phrase-level Linking of FrameNet with Sar-graphs.
Abstract
Recent research shows the importance of linking linguistic knowledge resources for the creation of large-scale linguistic data. We describe our approach for combining two English resources, FrameNet and sar-graphs, and illustrate the benefits of the linked data in a relation extraction setting. While FrameNet consists of schematic representations of situations, linked to lexemes and their valency patterns, sar-graphs are knowledge resources that connect semantic relations from factual knowledge graphs to the linguistic phrases used to express instances of these relations. We analyze the conceptual similarities and differences of both resources and propose to link sar-graphs and FrameNet on the levels of relations/frames as well as phrases. The former alignment involves a manual ontology mapping step, which allows us to extend sar-graphs with new phrase patterns from FrameNet. The phrase-level linking, on the other hand, is fully automatic. We investigate the quality of the automatically constructed links and identify two main classes of errors.
Year
Venue
Keywords
2016
LREC 2016 - TENTH INTERNATIONAL CONFERENCE ON LANGUAGE RESOURCES AND EVALUATION
Linking linguistic resources,knowledge graphs,relation extraction
Field
DocType
Citations 
Graph,Computer science,Phrase,Speech recognition,Natural language processing,Artificial intelligence,FrameNet
Conference
0
PageRank 
References 
Authors
0.34
0
5
Name
Order
Citations
PageRank
aleksandra gabryszak152.79
Sebastian Krause2265.04
Leonhard Hennig37210.62
Feiyu Xu445048.79
Hans Uszkoreit51010164.57