Objectives & Topics

The workshop aims to become a highly interactive research forum for exploring innovative approaches for extracting and correlating knowledge from degraded social media by exploiting the Web of Data. While the workshop’s general focus is on the creation of well-formed and well-interlinked structured data from highly unstructured Web content, its interdisciplinary scope will bring together researchers and practitioners from areas such as the semantic and social Web, text mining and NLP, multimedia analysis, data extraction and integration, and ontology and data mapping. The workshop will also look into innovative applications that exploit extracted knowledge in order to produce solutions to domain-specific needs.

We will welcome high-quality papers about current trends in the areas listed in the following, non-exhaustive list of topics. We will seek application-oriented, as well as more theoretical papers and position papers.

Knowledge detection and extraction (content perspective)

  • Knowledge extraction from text (NLP, text mining)
  • Dealing with scalability and performance issues with regard to large amounts of heterogeneous content
  • Multilinguality issues
  • Knowledge extraction from multimedia (image and video analysis)
  • Sentiment detection and opinion mining from text and audiovisual content
  • Detection and consideration of temporal and dynamics aspects
  • Dealing with degraded Web content

Knowledge enrichment, aggregation and correlation (data perspective)

  • Modelling of events and entities such as locations, organisations, topics, opinions
  • Representation of temporal and dynamics-related aspects
  • Data clustering and consolidation
  • Data enrichment based on linked data/semantic web
  • Using reference datasets to structure, cluster and correlate extracted knowledge
  • Evaluation of automatically extracted data

Exploitation of automatically extracted knowledge/data (application perspective)

  • Innovative applications which make use of automatically extracted data (e.g. for recommendation or personalisation of Web content)
  • Semantic search in annotated Web content
  • Entity-driven navigation of user-generated content
  • Novel navigation and visualisation of extracted knowledge/graphs and associated Web resources