[Corpora-List] 2 joint PhD Positions at the Fondazione Bruno Kessler and University of Brescia

[Bernardo Magnini]

Apologies for cross-posting


Position: 2 PhD positions
Duration: 3 years
Deadline: November 18, 2013 at 1.00 pm (italian time)
Web site: http://www.unibs.it/node/7683 (follow Ph.D. in Information Engineering)

Two PhD positions are open at the Department of Information Engineering, University of Brescia on (1) Natural Language Understanding  and (2) Reasoning-aided Business Process Management. The research will be carried out jointly between the Department of Information Engineering, University of Brescia (www.unibs.it ) and the research groups of Human Language Technology (hlt.fbk.eu) and SHELL (shell.fbk.eu) of the Fondazione Bruno Kessler, where most of the research activities will be conducted. 


About Us
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FBK-ICT (www.fbk.eu) conducts research in information technology. Research units and interdisciplinary research projects, such as HLT (hlt.fbk.ru) and SHELL (shell.fbk.eu), aim at  addressing important research challenges by exploiting and joining the different scientific competences that are at the base of the internationally well-known scientific excellence of FBK.

The Department of Information Engineering conducts research in several areas of information and communication technologies, including computer science and engineering. In this area different groups work on various basic and applied research projects with strong scientific competences especially in the fields of artificial intelligence, information systems, robotics, and human-computer interaction.


For further informations, and informal enquiries, please contact:

Prof. Alfonso Gerevini, gerevini at ing.unibs.it
Department of Information Engineering , University of Brescia,

Dr. Bernardo Magnini, magnini at fbk.eu
HLT, Fondazione Bruno Kessler

Dr. Chiara Ghidini, ghidini at fbk.eu
SHELL, Fondazione Bruno Kessler


Topics Description
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Position 1

TITLE: Automatic reasoning for semantic analysis of text
REF PERSON: Bernardo Magnini, Alfonso Gerevini

ABSTRACT
The automatic processing of large amount of unstructured data (e.g. texts) is crucial for a number of emerging applications, including media monitoring, customer interactions analytics and semantic search over big data.  On such a large-scale, pure logic-based methods of natural language interpretation have shown drawbacks in scalability. On the other side, pure statistical methods, although well scalable, still do not provide enough accuracy. Particularly when semantic inferences on text are considered, the need to combine the two approaches is becoming an hot topic in several areas of Computational Linguistic.
This PhD thesis addresses the study and the development of novel approaches for textual semantic inferences, with specific interest on Textual Entailment. The goal is to advance the state of the art in general entailment algorithms (e.g. graph transformations, tree edit distance), and to define a framework where both knowledge resources (e.g. WordNet, Wikipedia) and specific inference components (e.g. temporal, causal) interact each other while trying to establish an entailment relation between two portions of text.

PREFERRED SKILLS: reasonable knowledge of logic and machine learning
 


Position 2

TITLE: Reasoning-based Process Mining. 
REF PERSON: Chiara Ghidini, Alfonso Gerevini 

ABSTRACT: 
Process mining is a recent and rapidly emerging research field, aiming at discovering, monitoring and improving real processes by extracting knowledge from event logs readily available in today's (information) systems. However, despite the several enormous steps carried on in the last decade, as witnessed by the Process Mining Manifesto, still a number of open challenges waits to be addressed in this field, as for example, the run-time operational support for processes (i.e., the on-line detection and prediction of problems, and  the run-time provision of recommendations towards their resolution), or the management of complex event logs with different characteristics (e.g., too many, too few or too abstract data).  

The aim of this thesis is investigating how to exploit, adapt and combine techniques and approaches borrowed from different research fields, ranging from logic to artificial intelligence, from model checking to statistics, to advance the existing services for process analysis and process model (re-)design from monitoring data. The reasoning-based services provided as output can be for example the verification of complex requirements, constraining  the control flow or other dimensions as time or data, or the definition and provision of new metrics and key performance indicators (KPIs).
To this purpose, several are the challenges to be faced in the work as, for example, (i) the capability to represent and reason about secondary aspects for business processes such as data, time, resources; (ii) the capability to align execution information with models, when they exist, or to discover models from traces, when they do not exist; (iii) the capability to manage and reason on extremely large quantity of data (big data); (iv) the capability to realize the abovementioned analyses at run-time.  
The work will put together theoretical and methodological aspects, including for example the problem conceptualization and representation, as well as implementation and optimization ones, aimed at the development of process analysis services and tools.

PREFERRED SKILLS: good knowledge of logic and knowledge representation; reasonable knowledge of software engineering and conceptual modelling.
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