@inproceedings{middleton01,
  abstract = {Tools for filtering the World Wide Web exist, but they are hampered by the difficulty of capturing user preferences in such a dynamic environment. We explore the acquisition of user profiles by unobtrusive monitoring of browsing behaviour and application of supervised machine-learning techniques coupled with an ontological representation to extract user preferences. A multi-class approach to paper classification is used, allowing the paper topic taxonomy to be utilised during profile construction. The Quickstep recommender system is presented and two empirical studies evaluate it in a real work setting, measuring the effectiveness of using a hierarchical topic ontology compared with an extendable flat list.},
  address = {New York, NY, USA},
  author = {Middleton, Stuart E. and Roure, David C. De and Shadbolt, Nigel R.},
  booktitle = {K-CAP '01: Proceedings of the 1st international conference on Knowledge capture},
  doi = {http://doi.acm.org/10.1145/500737.500755},
  interhash = {332dfc15a8f0fc442b47a9a4b740b1bf},
  intrahash = {6d0a7792db2c0f96bd0a495a56e57464},
  isbn = {1-58113-380-4},
  location = {Victoria, British Columbia, Canada},
  pages = {100--107},
  publisher = {ACM},
  title = {Capturing knowledge of user preferences: ontologies in recommender systems},
  url = {http://portal.acm.org/citation.cfm?id=500737.500755},
  year = 2001
}

@inproceedings{middleton02,
  abstract = {Recommender systems learn about user preferences over time, automatically finding things of similar interest. This reduces the burden of creating explicit queries. Recommender systems do, however, suffer from cold-start problems where no initial information is available early on upon which to base recommendations. Semantic knowledge structures, such as ontologies, can provide valuable domain knowledge and user information. However, acquiring such knowledge and keeping it up to date is not a trivial task and user interests are particularly difficult to acquire and maintain. This paper investigates the synergy between a web-based research paper recommender system and an ontology containing information automatically extracted from departmental databases available on the web. The ontology is used to address the recommender systems cold-start problem. The recommender system addresses the ontology's interest-acquisition problem. An empirical evaluation of this approach is conducted and the performance of the integrated systems measured.},
  author = {Middleton, Stuart E. and Alani, Harith and Roure, David C. De},
  booktitle = {Proceedings of the WWW2002 International Workshop on the Semantic Web},
  interhash = {a098783b2b8f386218c3312ebcfa6286},
  intrahash = {401e667028f6a4674bb5403ec680d7f3},
  note = {cite arxiv:cs.LG/0204012
Comment: Semantic web conference, WWW2002, 10 pages},
  title = {Exploiting Synergy Between Ontologies and Recommender Systems},
  url = {http://arxiv.org/abs/cs/0204012},
  year = 2002
}

@inproceedings{middleton2003capturing,
  author = {Middleton, Stuart E. and Shadbolt, Nigel R. and {De Roure}, David C.},
  booktitle = {Proceedings of the international conference on Knowledge capture},
  interhash = {dbba6859beb8d94bebc21a74140e746d},
  intrahash = {09268a88aba1913bd7901cfb40819f71},
  isbn = {1-58113-583-1},
  location = {Sanibel Island, FL, USA},
  pages = {62--69},
  publisher = {ACM Press},
  title = {Capturing interest through inference and visualization: ontological user profiling in recommender systems},
  url = {doi.acm.org/10.1145/945645.945657},
  year = 2003
}

@article{middleton2004ontological,
  author = {Middleton, Stuart E. and Shadbolt, Nigel R. and {De Roure}, David C.},
  interhash = {c0bcba5b8f31cfbe434062d77057904e},
  intrahash = {a3516024369f530bcf3fb37d89aea498},
  issn = {1046-8188},
  journal = {ACM Trans. Inf. Syst.},
  number = 1,
  pages = {54--88},
  publisher = {ACM Press},
  title = {Ontological user profiling in recommender systems},
  url = {doi.acm.org/10.1145/963770.963773},
  volume = 22,
  year = 2004
}

@inproceedings{middleton2001capturing,
  author = {Middleton, Stuart E. and {De Roure}, David C. and Shadbolt, Nigel R.},
  booktitle = {Proceedings of the international conference on Knowledge capture},
  interhash = {912fba5e828e72d665ba40e7607f1d97},
  intrahash = {0d38005e912a8511eff1809776ed292f},
  isbn = {1-58113-380-4},
  location = {Victoria, British Columbia, Canada},
  pages = {100--107},
  publisher = {ACM Press},
  title = {Capturing knowledge of user preferences: ontologies in recommender systems},
  url = {doi.acm.org/10.1145/500737.500755},
  year = 2001
}

@inproceedings{middleton2002exploiting,
  author = {Middleton, Stuart E. and Alani, H. and Shadbolt, Nigel R. and {De Roure}, David C.},
  booktitle = {Proceedings of the 11th International World Wide Web Conference WWW-2002},
  interhash = {4aea05259e0bdc2b9001b7ce11c10ac0},
  intrahash = {4e6b8b4a669587d142338c5ccd1be4bb},
  location = {Hawaii, USA},
  title = {Exploiting Synergy Between Ontologies and Recommender System},
  year = 2002
}

@article{bechhofer2013linked,
  abstract = {Scientific data represents a significant portion of the linked open data cloud and scientists stand to benefit from the data fusion capability this will afford. Publishing linked data into the cloud, however, does not ensure the required reusability. Publishing has requirements of provenance, quality, credit, attribution and methods to provide the reproducibility that enables validation of results. In this paper we make the case for a scientific data publication model on top of linked data and introduce the notion of Research Objects as first class citizens for sharing and publishing.},
  author = {Bechhofer, Sean and Buchan, Iain and De Roure, David and Missier, Paolo and Ainsworth, John and Bhagat, Jiten and Couch, Philip and Cruickshank, Don and Delderfield, Mark and Dunlop, Ian and Gamble, Matthew and Michaelides, Danius and Owen, Stuart and Newman, David and Sufi, Shoaib and Goble, Carole},
  doi = {10.1016/j.future.2011.08.004},
  interhash = {8df8b7069a622aa2eae6d74e5fdc0a6b},
  intrahash = {f500b67a045765125183e23c827991d2},
  issn = {0167-739X},
  journal = {Future Generation Computer Systems},
  number = 2,
  pages = {599--611},
  title = {Why linked data is not enough for scientists},
  url = {http://www.sciencedirect.com/science/article/pii/S0167739X11001439},
  volume = 29,
  year = 2013
}