The figure below gives an overview of the specific research topics organised by research area, showing a graphical overview of their inter-relations:

  • Entity relatedness is a research topic in the knowledge extraction area that exploits both unstructured information and structured information, combining distributional semantics relatedness derived from large amounts of text with relatedness information embedded on the DBpedia Ontology.
  • The taxonomy extraction research topic is concerned with analysing large amounts of unstructured text corpora to produce taxonomic structures that can be used for LOD organisation, generation, and enrichment.
  • Linked data profiling deals with the problem of producing comprehensive descriptions of LD datasets in order to improve the identification of suitable datasets for specific application scenarios.
  • Ontology translation is concerned with applying and advancing statistical machine translation techniques for translating relatively limited linguistic descriptions of domain specific concepts that are available in monolingual or multilingual ontologies.
  • Term translation aims to advance statistical term translation models using information available in the LOD cloud in resources such as DBpedia or BabelNet.
  • The suggestion mining research topic aims at analysing unstructured social media textual content to extract structured suggestions from reviews or other types of user generated content.
  • Emotion analysis deals with extracting a more nuanced and dimensional representation of emotions from text, going beyond simply analysing positive or negative sentiment.


Linguistic Linked Data

Linked-Data Based NLP Architecture

Deep Learning