Open Knowledge Extraction Challenge Award
In this paper, we report on the participation of ADEL, an adaptive entity recognition and linking framework, to the OKE 2017 challenge. In particular, we propose an hybrid approach that combines various extraction methods to improve the recognition level and an efficient knowledge base indexing process to increase the efficiency of the linking step. We detail how we deal with finegrained entity types, either generic (e.g. Activity, Competition, Animal for the task 2) or domain specific (e.g. MusicArtist, SignalGroup, MusicalWork for the task 3). We also show how ADEL can flexibly disambiguate entities from different knowledge bases (DBpedia and MusicBrainz). We obtain promising results
on the OKE 2017 challenge training dataset for the first three tasks.
Type:
Conférence
City:
Portoroz
Date:
2017-05-28
Department:
Data Science
Eurecom Ref:
5231
Copyright:
© Springer. Personal use of this material is permitted. The definitive version of this paper was published in and is available at :
See also:
