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[ontolog-forum] Three Ph.D positions available at the DKM group in FBK T

To: "[ontolog-forum]" <ontolog-forum@xxxxxxxxxxxxxxxx>
From: Luciano Serafini <serafini@xxxxxx>
Date: Wed, 25 Jan 2012 16:48:16 +0000
Message-id: <67D02CDF-9013-405F-AB13-AC3CA60BC7C2@xxxxxx>

                                [[[ Apologies for multiple copies of this 
message ]]]    (01)

                                             Three PhD Positions Available    (02)

                                           Data and Knowledge Management Unit
                                                    http://dkm.fbk.eu
                                            Fondazione Bruno Kessler - IRST
                                                     Trento, Italy    (03)



The Data and Knowledge Management (DKM) research unit (http://dkm.fbk.eu) of 
the Bruno Kessler Foundation (FBK), Trento,
Italy, is seeking candidates for 3 Ph.D positions. (See also 
http://dkm.fbk.eu/index.php/PhD_thesis)    (04)

The Ph.D. studies will be held at the International Doctorate School in 
Information and Communication Technologies
(http://www.ict.unitn.it/) of the University of Trento, Italy.    (05)

Interested candidates should inquire for further information and/or apply by 
sending email to serafini<at>fbk.eu or ghidini<at>fbk.eu    (06)

Details of the positions follows:     (07)

P1: INFORMAITON EXTRACTION FOR ONTOLOGY ENGINEERING: 
for more details and statements of interest contact Chiara Ghidini 
<ghidini@xxxxxx>    (08)

Albeit the growing maturity of ontological engineering tools, ontology 
knowledge acquisition remains a highly manual, time-consuming, and complex 
task, that can easily hinder the ontology building process. Automatic ontology 
learning is a well-established research field whose goal is to support the 
semi-automatic construction of ontologies starting from available digital 
resources (e.g., a corpus, web pages, dictionaries, semi-structured and 
structured sources) in order to reduce the time and effort in the ontology 
development process.
In spite of the efforts and progresses made in ontology learning, and of the 
ambitious research plan of the ontology learning field, whose aim is to extract 
increasingly complex information ranging from terms, to relations, to 
hierarchies, and finally to axioms, state of the art methods and tools still 
mainly focus on the extraction of terms, with few exceptions addressing more 
complex tasks such as the extraction of (possibly hierarchical) relations, and 
axioms. Moreover the performances of the current algorithms appear to be more 
suitable to support the construction of light-weight medium-quality ontologies, 
rather than good quality conceptualizations of a domain according to the good 
practices in ontology modeling. To make very simple examples, current 
algorithms for term extraction provide reasonable performances in terms of 
precision and recall but lack the needed quality in providing a precise, 
shared, and well-founded, distinction between the classification of a term in 
an individual or in a concept. Similarly most algorithms for relation 
extraction are able to identify relations at the instance level, but are not 
able to abstract to the concept level, or to identify further characteristics 
of these relations (e.g., their cardinality, functionality, symmetry, and so 
on).
The aim of this thesis is to investigate how to combine work in automatic 
ontology learning, which is mainly based on Natural Language processing, 
information extraction, statistics, and machine learning techniques, and work 
in methodologies and tools for manual knowledge engineering to produce 
(semi)-automatic services for ontology learning better supporting the 
construction of rich and good quality ontologies. The work will start from an 
investigation of the current techniques for information extraction available in 
the field of Natural Language processing and their comparison with the 
requirements coming from the ontology design methodologies in the ontology 
engineering field, and will then research how to tailor those techniques in 
order to fulfill these requirements and to produce tools (or services) able not 
only to extract individuals, concepts, relations, hierarchies, and axioms, but 
to ground them in good ontology practices.    (09)

The work will address key research challenges in both Natural language 
processing and ontology engineering. It will have strong  algorithmic and 
methodological aspects, together with implementation-oriented tasks.    (010)

==============================================================================    (011)

P2: INTEGRATING LOGICAL AND STATISTICAL REASONING
For more details and statements of interest contact Luciano Serafini 
<serafini@xxxxxx>    (012)

In the last decade, automated reasoning techniques have reached a hight level 
of complexity able to support reasoning on large knowledge repositories 
expressed in different logical language. Examples are: SAT based reasoners for 
propositional logic, SMT (SAT solver modulo theory), reasoners on Description 
Logics and other semantic web languages, and resolution based theorem provers. 
In the meanwile, complex statistical methods such as support vector machines, 
kernel methods, and graphical models have been studied and developed. These 
systems are capable of learning regularities in large data-set and of 
synthesizing the result  in a model that supports stochastic inference. The two 
methodologies have reached such a level of maturity, that one could figure out 
also the possibility of profitably combine them in a unique uniform system 
which allow at the same time learning and reasoning.    (013)

During the last three years the FBK joint research project Copilosk has 
investigated on the advantages of combining these two methods for solving 
problems in natural language processing, with extremely interesting and 
encouraging results. which show that the usage of background knowledge 
(available in the semantic web) in combination of machine learning methods 
improves the performance in many important NLP tasks [1,2,3]. Continuing in 
this direction we would like to design a general methodology and formal 
reference model. In the literature there have already been some attempts in 
this direction, such as Markov Logic Networks [4] Fuzzy Logics, and works that 
bridges logics with kernel machines [6]. These approaches however are 
extensions of Machine learning techniques in order to include some logical 
knowledge, and they presents some limits in the exploitation of logical 
reasoning in combination with leaning.    (014)

With this thesis we would like to define a formal framework that integrates in 
a uniform model reasoning and learning. In this new framework it should be 
possible to define the following two general tasks:    (015)

         Learning from data in presence of background knowledge. This task is 
quite important as it implements what can be seen as incremental learning, 
where the learning is performed in successive steps, and at each step the 
system can reuse the knowledge acquired in the previous steps.
         Logical reasoning in presence of real observed data. In this task 
logical reasoning is performed by taking also into account the statistical 
regularities observable in data. This allows to implement "plausible reasoning" 
i.e., inference which are not logically fully correct but that are in fact 
acceptable because some extreme cases never happens (according to the data), 
and are therefore irrelevant from the statistical point of view.
This new framework should combine one of the most standard statistical model, 
such as graphical models or regularization methods  with automatic reasoning 
techniques such as SAT based, or tableaux based or resolution based reasoning.    (016)

[1]     Volha Bryl, Claudio Giuliano, Luciano Serafini, Kateryna Tymoshenko. 
Using Background Knowledge to Support Coreference Resolution. In Proceedings of 
the 19th European Conference on Artificial Intelligence (ECAI 2010), Lisbon, 
Portugal, August 16-20, 2010, pp. 759-764.    (017)

[2]     Volha Bryl, Claudio Giuliano, Luciano Serafini, Kateryna Tymoshenko. 
Supporting natural language processing with background knowledge: coreference 
resolution case. In Proceedings of the 9th International Semantic Web 
Conference (ISWC 2010), Shanghai, China, November 7-11, 2010 (Springer), pp. 
80-95.    (018)

[3]     Volha Bryl, Sara Tonelli, Claudio Giuliano, Luciano Serafini. A Novel 
FrameNet-based Resource for the Semantic Web. To appear in the proceedings of 
ACM Symposium on Appliced Computing (SAC) 2012, Technical Track on The Semantic 
Web and Applications (SWA), Riva del Garda (Trento), Italy) March 25-29, 2012.    (019)

[4]     Matthew Richardson and Pedro Domingos, Markov Logic Networks. Machine 
Learning, 62 (2006), pp 107-136.    (020)

[5]     Michelangelo Diligenti, Marco Gori, Marco Maggini, Leonardo Rigutini: 
Bridging logic and kernel machines. Machine Learning 86(1): 57-88 (2012)    (021)


P3: BEHAVIOR RECOGNITION AND INDUCTION VIA SEMANTIC REASONING OVER HUMAN 
ACTIVITY PROCESSES
Thesis in collaboration with the Skil lab in Trento of Telecom Italia
for more details and statements of interest contact Luciano Serafini 
<serafini@xxxxxx>    (022)

The modern (smart) mobile devices allow for a very wide variety of actions 
(communication, browsing, application execution) and in addition to standard 
data related to phoning, include many different sources of information coming 
from sensors (e.g. GPS position, accelerometer data, etc.). This scenario has 
led to the birth of novel research areas such as context awareness, situation 
detection, activity recognition, behavior understanding and many others, which 
aim at exploiting all these information in order to support the user in 
multiple daily tasks. In parallel, but on a completely different stage, the 
semantic web and the  linked open data made available a huge quantity of 
semantic data and knowledge, concerning semantic tagging of geographical data 
(e.g., openstreetmap) or general knowledge about persons, locations, 
organizations and events, (e.g., available in dbpedia, freebase, etc.) and 
general terminologic and ontological knowledge (e.g., schema.org, sumo and 
dolce upper level ontologies, yago2, wordnet and Framenet. The above scenario 
opens the possibility of new research challenges of combining raw sensor data 
with semantic information and ontological knowledge for the analysis of human 
behavior. The implementation of this vision requires and effective and deep 
integration of techniques from different disciplines in computer science as 
data mining, machine  learning, semantic web and knowledge representation and 
reasoning. The aim of this PhD proposal is to address key research challenges 
in these fields and, in particular, to investigate the benefits of applying the 
semantic based technologies for modeling and reasoning over human activity 
processes.    (023)

The student will develop a research plan which will cover the following three 
important and complementary aspects:    (024)

(i) investigate on models for combining/modifying/extending the standard 
techniques of data and knowledge processing in order to provide a framework 
that support reasoning/learning with raw data, information and knowledge.    (025)

(ii) definition of reasoning services on top of the applied 
techniques/formalisms; (iii) modeling, development and experimentation on 
practical real-world problems in different fields (e-health, smart-city, ...)    (026)

Academic advisor: Prof. Luciano Serafini Industrial Advisor: Michele Vescovi 
(Michele.vescovi@xxxxxxxxxxxxxxxxxxxxxx)    (027)

===================================================================================================    (028)

Candidate Profile
=================    (029)

The ideal candidate should have an MS or equivalent degree in computer science, 
mathematics or electronic engineering, phisics and philosophy, and combine 
solid theoretical background and software development skills.    (030)

The candidate should be able to work in a collaborative environment, with a 
strong commitment to reaching research excellence and achieving assigned 
objectives.
>     (031)


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