A relationship extraction task requires the detection and classification of semantic relationship mentions within a set of artifacts, typically from text or XML documents. The task is very similar to that of information extraction (IE), but IE additionally requires the removal of repeated relations (disambiguation) and generally refers to the extraction of many different relationships.
One approach to this problem involves the use of domain ontologies. Another approach 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 uses only Wikipedia and search engine page count for acquiring coarse-grained relations to construct lightweight ontologies.
The relationships can be represented using a variety of formalisms/languages. One such representation language for data on the Web is RDF.
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.
- Text analytics
- Semantic analytics
- Semantic role labeling
- Information extraction
- Business Intelligence 2.0
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