Susan Craw H&S
Title: Professor
First Name: Susan
Surname: Craw
Position: Research Professor
Telephone: 01224 262711

Research Interests

Susan’s research in Artificial Intelligence develops innovative data/text/web mining technologies to discover knowledge to embed in case-based reasoning systems, recommender systems, and other intelligent information systems. Her research extracts knowledge from sources of ‘big data’, including databases, documents, music and image collections, and social media, and focuses on discovery and refinement for CBR’s knowledge containers including case bases and retrieval, reuse and adaptation knowledge.

Her work is highly relevant to industry; e.g. oil & gas ‘lessons learned’, satellite incident management, medical decision support, and pharmaceutical product design. Recent research develops smart information systems that allow intelligent interaction and engagement with information, including recommendation of on-line music, browsing textile archives, and context-aware interpretations for tourist locations, museum exhibits and historic sites.

Susan's research papers are available at Google Scholar, SCOPUS, ResearchGate and RGU’s OpenAir open-access repository.

Duties and Responsibilities

  • Researcher Case-Based Reasoning, Data/Text Mining, Knowledge Discovery, Recommender Systems, Intelligent Information Systems
  • Research Student Supervisor
  • Member of Smart Information Systems research group 

Academic Background

  • PhD in Computing Science, University of Aberdeen, “Automating the Refinement of Knowledge Based Systems” (1991).
  • MSc by Research in Mathematics, University of Aberdeen, “Homotopy in Banach Algebras”  (1979)

Research Projects

Recent Research Projects Include

  • e-Learning Recommendation: The Web is an excellent source of e-learning materials but learners can have difficulty finding the right learning materials because of the difficulty in assembling effective search keywords. This recommender exploits learning concepts extracted from e-books as a knowledge-rich representation that takes advantage of tutors' expertise.
  • Music Recommenders: Users of on-line music services are looking for good recommendations, but also want to discover music that they do not already know. This recommender uses audio and social tagging to find tracks that balance novelty with quality.
  • Decisions from Data: a methodology allowing knowledge to be extracted from numeric or textual data, so that it can be effectively retrieved and reused to support decision-making on new problems.
  • Living History: A mobile app solution for tourists improves the interaction with objects at remote historic sites. NFC tags for tap-and-go services provide relevant visitor information about specific objects/places on historic sites to the visitor’s mobile without 3G/Web connectivity.
  • Smart Beacons: This mobile app uses proximity-aware Neate Beacon sensors to trigger the delivery of content relevant to a nearby item. Museums are interested to exploit this to enhance visitor experiences through engagement with exhibits.
  • STAR Smart Textile Archive: This prototype provides more flexible, interactive and collaborative engagements for designers and practitioners with textile assets. The project assesses the feasibility and potential impact of such an archive to the textile industry.


  • AHRC Digital Transformations, Accessing implicit knowledge of textiles and design - a smart, living archive for a heritage industry'' (AH/J013218/1), Williams, Craw, Wiratunga, Martin (Heriot-Watt) & Burnett, 2012
  • SFC Horizon, Smart Tourism, Oberlander (Edinburgh), Chalmers (Glasgow), Craw, Edwards (Aberdeen) & Quigley (St Andrews), 2011-2014, 

External/Professional Roles

  • Member of EPSRC Peer College
  • Member of SFC Research and Knowledge Exchange Committee
  • International expert for SFI EXPOSED Aquaculture Centre for Research-Based Innovation, Trondheim
  • Senior PC Member IJCAI 2015 & 2016
  • Senior Member of Association for the Advancement of Artificial Intelligence in recognition of contribution to AI
  • Member ACM, IEEE

Selected Publications

  • Blessing Mbipom, Susan Craw and Stewart Massie (2016).  Harnessing Background Knowledge for E-learning Recommendation. In Proceedings of the 36th SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence. doi:10.1007/978-3-319-47175-4_1  PDF.
  • Eduardo Lupiani, Stewart Massie, Susan Craw, Jose M Juarez and Jose T Palma (2016). Case-base maintenance with multi-objective evolutionary algorithms. Journal of Intelligent Information Systems, 46(2):259–284, Springer. doi:10.1007/s10844-015-0378-zPDF.
  • Susan Craw, Ben Horsburgh and Stewart Massie (2015). Music recommendation: Audio Neighbourhoods to Discover Music in the Long Tail. In Proceedings of the 23rd International Conference on Case-Based Reasoning, pages 73–87, Frankfurt, Germany. Springer LNAI 9343. Best Paper Award. doi:10.1007/978-3-319-24586-7_6. PDF
  • Susan Craw, Ben Horsburgh and Stewart Massie (2015). Music Recommenders: User evaluation without real users? In Proceedings of the 24th International Joint Conference on Artificial Intelligence (IJCAI), pages = 1749–1755, Buenos Aires, Argentina. AAAI Press. PDF.
  • Ben Horsburgh, Susan Craw and Stewart Massie (2015). Learning pseudo-tags to augment sparse tagging in hybrid music recommender systems, Artificial Intelligence 219:25–39. In Elsevier’s Top25 Hottest Articles for the Artificial Intelligence Journal (13th) Jan-Mar 2015. doi:10.1016/j.artint.2014.11.004PDF
  • Ben Horsburgh, Susan Craw, Dorothy Williams, Simon Burnett, Katie Morrison and Suzanne Martin (2013). User perceptions of relevance and its effect on retrieval in a smart textile archive. In Proceedings of the 21st International Conference on Case-Based Reasoning, pages 149–163, Saratoga Springs, NY. Springer. doi:10.1007/978-3-642-39056-2_11. PDF.
  • Eduardo Lupiani, Susan Craw, Stewart Massie, Jose M Juarez and Jose T Palma (2013). A multi-objective evolutionary algorithm fitness function for case-base maintenance. In Proceedings of the 21st International Conference on Case-Based Reasoning, pages 218–232, Saratoga Springs, NY. Springer. doi:10.1007/978-3-642-39056-2_16. PDF
  • Ben Horsburgh, Susan Craw and Stewart Massie (2012). Music-inspired texture representation. In Proceedings of the 26th AAAI  Conference on Artificial Intelligence, pages 52–58, Toronto, Canada. AAAI Press. PDF.
  • Susan Craw (2011). Introspective learning to build case-based reasoning. In Norbert M. Seel, editor, Encyclopedia of the Sciences of Learning. Springer, Heidelberg. doi:10.1007/978-1-4419-1428-6. Invited Chapter.
  • Ben Horsburgh, Susan Craw, and Stewart Massie (2011). Finding the hidden gems: Recommending untagged music. In Proceedings of the 22nd International Joint Conference in Artificial Intelligence (IJCAI), pages 2256–2261, Barcelona, Spain, AAAI Press. doi:10.5591/978-1-57735-516-8/IJCAI11-376. PDF.
  • Richard Thomson, Susan Craw, Stewart Massie, Hatem Ahriz, and Ian Mills (2011). Plan recommendation for well engineering. In Proceedings of the 24th International Conference on Industrial, Engineering and Other Applications of Applied Intelligent Systems, pages 436–445, Syracuse, NY. Springer. doi:10.1007/978-3-642-21827-9_45. PDF.
  • Martijn van den Branden, Nirmalie Wiratunga, Dean Burton, and Susan Craw (2011). Integrating case-based reasoning with an electronic patient record system. Artificial Intelligence in Medicine, 51(2):117– 123. doi:10.1016/j.artmed.2010.12.004
  • Susan Craw. Case-based reasoning (2010). In Claude Sammut and Geoffrey Webb, editors, Encyclopedia of Machine Learning, pages 147–154. Springer, Heidelberg. doi:10.1007/978-0-387-30164-8_97 Invited Chapter.
  • Susan Craw (2009). We’re wiser together. In Proceedings of the 8th International Conference on Case-Based Reasoning, pages 1–5, Seattle, WA. Springer. doi:10.1007/978-3-642-02998-1_1
  • Susan Craw, David W. Aha, Sarabjot Singh Anand, and Barry Smyth, editors (2009). Proceedings of the IJCAI Workshop on Grand Challenges for Reasoning from Experiences, Pasadena, CA.
  • Susan Craw (2009). Agile case-based reasoning: A grand challenge towards opportunistic reasoning from experiences. In Proceedings of the IJCAI-09 Workshop on Grand Challenges in Reasoning from Experiences, pages 33–39, Pasadena, CA, 2009.
  • Stella Asiimwe, Susan Craw, Nirmalie Wiratunga, and Bruce Taylor (2007). Automatically acquiring structured case representations: The SMART way. In Proceedings of the 27th BCS SGAI International Conference on Innovative Techniques and Applications of Artificial Intelligence, pages 45-58, Cambridge, UK. Springer. doi:10.1007/978-1-84800-086-5_4. PDF.
  • Susan Craw, Nirmalie Wiratunga and Ray C. Rowe (2006). Learning adaptation knowledge to improve case-based reasoning. Artificial Intelligence, 170(16-17):1175–1192. doi:10.1016/j.artint.2006.09.001.
  • K.B. Matthews, K. Buchan, A.R. Sibbald, and S. Craw (2006). Combining deliberative and computer-based methods for multi-objective land-use planning.  Agricultural Systems, 87(1):18–37. doi:10.1016/j.agsy.2004.11.002
  • Ramon López de Mántaras, David McSherry, Derek Bridge, David Leake, Barry Smyth, Susan Craw, Boi Faltings, Mary Lou Maher, Michael T. Cox, Kenneth Forbus, Mark Keane, Agnar Aamodt, and Ian Watson (2005). Retrieval, reuse, revision, and retention in case-based reasoning. Knowledge Engineering Review, 20(3):215–240. doi:10.1017/S0269888906000646. PDF.
  • Ashok K. Goel and Susan Craw (2005). Design, innovation and case-based reasoning. Knowledge Engineering Review, 20(3):271–276. doi:10.1017/S0269888906000609
  • Jacek Jarmulak, Susan Craw and Ray Rowe (2001). Using case-base data to learn adaptation knowledge for design. In Proceedings of the 17th International Joint Conference in Artificial Intelligence (IJCAI), pages 1011–1016, Seattle, WA. Morgan Kaufmann.
  • Jacek Jarmulak, Susan Craw and Ray Rowe (2000). Self-optimising CBR retrieval. In Proceedings of the 12th IEEE International Conference on Tools with AI, pages 376–383. IEEE Press.
  • Keith B. Matthews, Alan R. Sibbald and Susan Craw (1999). Implementation of a spatial decision support system for rural land use planning: integrating geographic information system and environmental models with search and optimisation algorithms. Computers and Electronics in Agriculture 23(1):9–26. doi:10.1016/S0168-1699(99)00005-8
  • Susan Craw and Robin Boswell (1999). Representing Problem-Solving for Knowledge Refinement. In Proceedings of the 16th AAAI  Conference on Artificial Intelligence, pages 227–234, Orlando, FL. AAAI Press/MIT Press. PDF.
  • Susan Craw, Nirmalie Wiratunga and Ray Rowe (1998). Case-based design for tablet formulation. In Proceedings of the 4th European Workshop on Case-Based Reasoning, pages 358–369, Dublin, Ireland. Springer LNCS 1488. doi:10.1007/BFb0056347.

Additional Information / Media work

  • Invited keynote “Robust Intelligence from Case-Based Systems”, BCS Real AI Day, London, UK, 2016
  • Meet the Expert talk “On-line Recommenders: Artificial Intelligence + Big Data”, Aberdeen Science Centre, Aberdeen, UK, 2016
  • Panel Member “Is AI an existential threat to humanity?”, 35th BCS-SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2015
  • Invited Keynote "Recommender Systems: Taking Advantage of Noisy Neighbours”, 20th UK-CBR Workshop, Cambridge, UK, 2015
  • Invited Seminar “Discovering Knowledge for Smarter Case-Based Systems”, Fraunhofer Institute for Intelligent Analysis and Information Systems, Sankt Augustin, Germany, 2015
  • ICCBR 2015 Best Paper Award for “Music recommendation: Audio Neighbourhoods to Discover Music in the Long Tail”, 23rd International Conference on Case-Based Reasoning, Frankfurt, Germany, 2015
  • Invited Presentation “Smart Information Systems for Exposed Aquaculture Operations”, SINTEF, Trondheim, Norway, 2015
  • Invited Keynote “The Future Influence of Digital Technology on Tourism”, Aberdeen City Shire Tourism Conference, Aberdeen, UK, 2014
  • Invited Keynote “Smart Data Technologies: From Data to Improved Decision Making”, Oil Gas ICT Leader Conference, Aberdeen, UK, 2014
  • Application Keynote “Corporate Memory and Innovation: Closing the Loop”, “Real AI Day”, 30th BCS-SGAI International Conference on Artificial Intelligence, Cambridge, UK, 2010
  • Invited Keynote “We’re Wiser Together”, 8th International Conference on Case-Based Reasoning, Seattle, WA, 2009