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Dr Thomas Roelleke

Thomas

Senior Lecturer

Email: t.roelleke@qmul.ac.uk
Telephone: +44 20 7882 7988
Room Number: Peter Landin, CS 423
Website: http://www.eecs.qmul.ac.uk/~thor
Office Hours: Tuesday 12:00-14:00

Teaching

Database Systems (Undergraduate)

This module is an introduction to databases and their language systems in theory and practice. The main topics covered by the module are: the principles and components of database management systems; the main modelling techniques used in the construction of database systems; implementation of databases using an object-relational database management system; the main relational database language; Object-Oriented database systems; future trends, in particular information retrieval, data warehouses and data mining.There are two timetabled lectures a week, and one-hour tutorial per week (though not every week). There will be timetabled laboratory sessions (two hours a week) for approximately five weeks.

Research

Research Interests:

My research focuses on three related areas:
1. information retrieval (IR) models and probability theory
2. integration of database (DB) and IR technologies
3. DB+IR+AI technology and advanced statistics for data science

IR models are related to probability theory and the sound derivation of IR models leads to new and general approaches to rank any object, to reason about complex knowledge sources, and to make decisions. Many results of my research over the past 10 years are summarised in the book "IR Models: Foundations and Relationships", Morgan Claypool Publishers, 2013. Currently, my main research interest is in generalisations of probability theory in order to obtain a "new" theory that joins probabilistic and information-theoretic reasoning (logic).

The integration of DB and IR (and AI) is an ongoing research challenge, though, in principle, DB and IR do the same: manage and retrieve data. I have developed probabilistic object-relational, logic-based knowledge representations that are useful for solving tasks in the domain of "semantic" (knowledge-rich) information management tasks. This led to POOL (a probabilistic object-oriented logic) based on AI knowledge representations, and the "Relational Bayes", a patented technology (VLDB Journal 2008).

Both, probability theory and DB+IR+AI technology produce methods and tools for solving complex data science tasks.

Based on the insights into probabilistic reasoning and IR models, and a seamless DB+IR+AI technology, we apply a probabilistic Datalog engine in data science scenarios (data analytics, complex search).

Publications

  • Milajevs D, Sadrzadeh M, Roelleke T (2015). IR meets NLP. Proceedings of the 2015 International Conference on The Theory of Information Retrieval


  • Roelleke T, Kaltenbrunner A, Baeza-Yates R (2015). Harmony Assumptions in Information Retrieval and Social Networks. nameOfConference


  • Martinez-Alvarez M, Bonzanini M, Roelleke T (2013). Mathematical Specification and Logic Modelling in the context of IR. Proceedings of the 2013 Conference on the Theory of Information Retrieval


    QMRO: qmroHref
  • Roelleke T, Bonzanini M, Martinez-Alvarez M (2013). On the modelling of ranking algorithms in probabilistic datalog. Proceedings of the 7th International Workshop on Ranking in Databases


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  • Bonzanini M, Martinez-Alvarez M, Roelleke T (2013). Extractive summarisation via sentence removal. Proceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval


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  • Martinez-Alvarez M, Bellogin A, Roelleke T (2013). Document Difficulty Framework for Semi-automatic Text Classification. nameOfConference


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  • Bonzanini M, Martinez-Alvarez M, Roelleke T (2012). Investigating the use of extractive summarisation in sentiment classification. nameOfConference

    DOI: doi

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  • Bonzanini M, Martinez-Alvarez M, Roelleke T (2012). Opinion summarisation through sentence extraction. Proceedings of the 35th international ACM SIGIR conference on Research and development in information retrieval


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  • Martinez-Alvarez M, Yahyaei S, Roelleke T (2012). Semi-automatic document classification. nameOfConference


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  • Martinez-Alvarez M, Roelleke T (2011). A Descriptive Approach to Classification. nameOfConference


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  • Azzam H, Roelleke T (2011). A Generic Data Model for Schema-Driven Design in Information Retrieval Applications. nameOfConference


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  • Yahyaei S, Bonzanini M, Roelleke T (2011). Cross-Lingual Text Fragment Alignment Using Divergence from Randomness. nameOfConference


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  • Smeraldi F, Martinez-Alvarez M, Frommholz I et al. (2011). On the probabilistic logical modelling of quantum and geometrically–inspired IR. nameOfConference

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  • Azzam H, Roelleke T (2010). SQR. CIKM '10: International Conference on Information and Knowledge Management


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  • Martinez-Alvarez M, Roelleke T (2010). Modelling Probabilistic Inference Networks and Classification in Probabilistic Datalog. nameOfConference


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  • Amer-Yahia S, Hiemstra D, Roelleke T et al. (2008). DB&IR Integration: Report on the Dagstuhl Seminar "Ranked XML Querying". nameOfConference

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    QMRO: qmroHref
  • ROELLEKE T, Wang J (2008). TF-IDF Uncovered: A Study of Theories and Probabilities. 31st Annual International ACM SIGIR Conference on Research and Development in Information Retrieval


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  • ROELLEKE T, Wang J (2006). A Parallel Derivation of Probabilistic Retrieval Models. 29th Annual International ACM SIGIR Conference on Research and Development in Information Retrieval, Seattle, US


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  • LALMAS M, Roelleke T, Ruthven I (2003). Abductive retrieval for multimedia information seeking. 10th International Conference on Human - Computer Interaction, HCI International, Crete, Greece, vol. 4

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  • Pearmain A, Lalmas M, Moutogianni E et al. (2002). Using MPEG-7 at the consumer terminal in broadcasting. nameOfConference


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  • Lalmas L, ROELLEKE T, Fuhr N (2002). Intelligent Hypermedia Retrieval. nameOfConference

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