[[[ Apologies for multiple copies of this
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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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