[6][7] There is also the approach that involves visual detection of meaningful relationships in parametric values of objects listed on a data table that shift positions as the table is permuted automatically as controlled by the software user.
The poor coverage, rarity and development cost related to structured resources such as semantic lexicons (e.g. WordNet, UMLS) and domain ontologies (e.g. the Gene Ontology) has given rise to new approaches based on broad, dynamic background knowledge on the Web.
For instance, the ARCHILES technique[8] uses only Wikipedia and search engine page count for acquiring coarse-grained relations to construct lightweight ontologies.
More recently, end-to-end systems which jointly learn to extract entity mentions and their semantic relations have been proposed with strong potential to obtain high performance.
[11] Researchers have constructed multiple datasets for benchmarking relationship extraction methods.