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Dealing with Open Domain Data

Keynote at DBpedia day, part of Semantics 2018, 10/09/2018

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Dealing with Open Domain Data

  1. 1. Dealing with open domain data Mathieu d’Aquin - @mdaquin Data Science Institute Insight Centre for Data Analytics NUI Galway
  2. 2. Dealing with open domain data Mathieu d’Aquin - @mdaquin Data Science Institute Insight Centre for Data Analytics NUI Galway
  3. 3. Typical knowledge-based systems are not open domain d’Aquin et al. "Knowledge editing and maintenance tools for a semantic portal in oncology." International journal of human-computer studies 62, no. 5 (2005): 619-638. Lieber et al.. "Modeling adaptation of breast cancer treatment decision protocols in the KASIMIR project." Applied Intelligence 28, no. 3 (2008) d'Aquin et al., "Towards a semantic portal for oncology using a description logic with fuzzy concrete domains." In Capturing Intelligence, vol. 1, pp. 379-393. Elsevier, 2006.
  4. 4. Open domain: When we don’t know what we are going to be asked Typically, question-answering or semantic search, but also many others. Lopez et al. "Scaling up question-answering to linked data." In International Conference on Knowledge Engineering and Knowledge Management, pp. 193-210. Springer, Berlin, Heidelberg, 2010. d'Aquin, Mathieu, Marta Sabou, Enrico Motta, Sofia Angeletou, Laurian Gridinoc, Vanessa Lopez, and Fouad Zablith. "What can be done with the Semantic Web? An Overview of Watson-based Applications." In CEUR Workshop Proceedings, vol. 426. 2008.
  5. 5. Based on KMi Watson Ontology search engine and semantic web gateway d'Aquin and Motta. "Watson, more than a semantic web search engine." Semantic Web 2, no. 1 (2011): 55-63.
  6. 6. (over simplified) Core assumption (at the time, i.e. ~2007) If the Semantic Web carries on growing in the same way as it dies, it will end up knowing everything
  7. 7. That did not really happen, but... Open domain, intelligent applications based on the semantic web still do
  8. 8. A recent example The AFEL (Analytics for Everyday Learning) project: http://afel-project.eu @afelProject d’Aquin et al. "AFEL: Towards Measuring Online Activities Contributions to Self-Directed Learning.", ARTEL 2017 workshop at EC-TEL. d'Aquin et al. "AFEL-Analytics for Everyday Learning." In Companion of the The Web Conference 2018.
  9. 9. The AFEL process AFEL Data Platform InputAPIs OutputAPIs Target platform AFEL Mobile app AFEL Visual Analytics AFEL Rec. Services enriched activity data and indicators enriched activity data and indicators recommendations activity data resource text and metatada resources and activities
  10. 10. Detecting learning scopes (i.e. topics) in activity streams Zainab and d’Aquin, Detection of Online Learning Activity Scopes, AFEL workshop at EC-TEL 2018
  11. 11. Detecting learning scopes (i.e. topics) in activity streams Zainab and d’Aquin, Detection of Online Learning Activity Scopes, AFEL workshop at EC-TEL 2018 DBpedia Spotlight Abstraction throughDBpedia categories Similar to k-means Based on TF.IDF of DBpedia ent./cats
  12. 12. Revised assumption The Semantic Web/DBpedia might not know everything, but they might know something about almost everything
  13. 13. For something more clever: Explain patterns Tiddi et al. "Data patterns explained with linked data." In ECML/PKDD 2015 Tiddi et al. "Dedalo: Looking for clusters explanations in a labyrinth of linked data." In ESWC 2014
  14. 14. Or finding biases in datasets Using DBpedia as a reference dataset, found for example that: - A dataset about places in Finland had a significant focus on Finland, and the longitudes/latitudes in Finland (obvious) - A dataset about (mostly UK-based) writers had a significant focus on authors of novels and poetry, and also who had suicide as cause of death Tiddi et al. "Quantifying the bias in data links." K-CAP 2014
  15. 15. Conclusions The notion of what the semantic web and linked data can achieve has evolved a lot in the last 15 years. But, the ability for them to represent in the same space data from many different domains is still a key aspect. Still only few applications have exploited it, due to: - Lack of robustness of the methods to access the data - Lack of understanding of the benefits of linked data - Lack of integration with with other tools (reasoning, mining, machine learning). Parenthesis: Linked Data - One graph or a collection of datasets? Thank you! Contacts: @mdaquin - mathieu.daquin@insight-centre.org - mdaquin.net

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