Annotation & Labeling

Thursday, September 13, 2018 - 15:00 to 16:00
Danube Suite 1


Geo-semantic labelling of Open Data

In the past years Open Data has become a trend among governments to increase transparency and public engagement by opening up national, regional, and local datasets. However, while many of these datasets come in semi-structured file formats, they use different schemata and lack geo-references or semantically meaningful links and descriptions of the corresponding geo-entities. We aim to address this by detecting and establishing links to geo-entities in the datasets found in Open Data catalogs and their respective metadata descriptions and link them to a knowledge graph of geo-entities.

Temporal Role Annotation for Named Entities

Natural language understanding tasks are key to extracting structured and semantic information from text. One of the most chal- lenging problems in natural language is ambiguity and resolving such ambiguity based on context including temporal information. This paper, focuses on the task of extracting temporal roles from text, e.g. CEO of an organization or head of a country. A temporal role has a domain, which may resolve to different entities depending on the context and especially on temporal information, e.g. CEO of Microsoft in 2000.