[DL] [CfP] GenSW2017: 1st Workshop on "Generalizing knowledge: from Machine Learning and Knowledge Representation to the Semantic Web"

[Simona Colucci]

[Apologies for cross-posting.]

***First International Workshop on "Generalizing knowledge: from Machine
Learning and Knowledge Representation to the Semantic Web" (GenSW2017)***

In conjunction with with "The 16th International Conference of the Italian
Association for Artificial Intelligence"  (AI*IA 2017),  Bari, Italy,
November 14 - 17 2017.

Website: https://sites.google.com/site/gensw2017/



***Important Dates***

Abstract submission:  13 July 2017
Paper submission: 18 July 2017
Notification to authors:  8 September 2017
Camera-ready copies: 29 September 2017



***Call for Papers***


Generalizing descriptions is a problem  traditionally investigated in at
least two different  fields of Artificial Intelligence: Machine Learning
(ML) and Knowledge Representation (KR). Both  research fields have played
an important role in the development of the Semantic Web (SW).

KR provided the theoretical basis for formalizing shared knowledge bases,
a.k.a.  ontologies, and for deductively reasoning over them. ML methods
have been used for enriching  ontologies, both at schema and instance
level, by exploiting inductive reasoning, while still benefiting from
deductive reasoning, when possible.


In the Web of Data, the availability of generalization mechanisms could be
crucial  for performing several knowledge management tasks, such as data
summarization, data indexing, cluster discovery and many others.  However,
performing generalization in such a context cannot be done by just
revisiting traditional generalization services, because some  issues and
peculiarities need to be carefully taken into account. One of these
peculiarities is the data size, which requires new scalable techniques. The
second one is the data quality, which is affected by the endemic
redundancy, noise, frequent irrelevance  and possible inconsistency of
theavailable
information. A third one is data interdependencies stemming from RDFS
statements.


Despite some preliminary research efforts, very few solutions and methods
can be found at the state of the art for coping with this urgent problem.
The maturity of solutions coming from the ML and KR fields may certainly
provide a reasonable starting point.  However, methods mixing or stacking
solutions coming from both fields may result more promising to address all
raised issues. Therefore, the main goal of the workshop is to foster
solutions cross-fertilizing both  ML and KR fields,  focusing on
generalizing SW knowledge descriptions and, possibly taking into account
scalability issues. Solutions of interest should cope with descriptions
formalized, primarily, in RDF/RDFS,  but also in more expressive
representation languages, like Description Logics/OWL.


The workshop aims at gathering solutions for the generalization of
knowledge descriptions  formalized  in  standard representation languages
for the Semantic  Web (primarily, but not only, RDF/RDFS).  Solutions of
interest should focus  (primarily, but not only) on methods  mixing and/or
stacking solutions coming from Machine Learning and Knowledge
Representation fields  and applicable to standard Semantic Web
representation languages. The developments of scalable solutions for this
purpose will be particularly appreciated.

Topics of interest include, but are not limited to:

·        KR  and/or ML methods (possibly in combination) for generalizing
in the Semantic Web

·         Semi-supervised, unbalanced, inductive learning for generalizing
in the Semantic Web

·         Reasoning services for generalization in the Semantic Web

1.       Generalization methods for  finding commonalities  and differences
in the Semantic Web

2.       Generalization methods for enrichment Semantic Web knowledge bases

3.       Generalization methods for indexing in the Linked Data Cloud

·         Evaluation and benchmarking  of generalization approaches in the
Semantic Web

·         Scalable algorithms for generalizing the Web of Data

·         Generalization in presence of uncertain/inconsistent/noisy
knowledge


Papers should be written in English, formatted according to the Springer
LNCS style
<https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines>,
and not exceed 12 pages (full papers) or 4 pages (position papers) plus
bibliography.
Papers must be submitted via easychair: https://easychair.o
rg/conferences/?conf=gensw2017.

All accepted papers will be scheduled for oral presentations and will be
published in CEUR Workshop Proceedings AI*IA Series.

*Authors of selected papers accepted to the workshop will be invited to
submit an extended version for publication on the journal “Semantic Web –
Interoperability, Usability, Applicability"
(http://www.semantic-web-journal.net/
<http://www.semantic-web-journal.net/>). Papers selected for the special
issue have to go through a full review process before acceptance.*


***Organizing  Committee***


Simona Colucci, Politecnico di Bari
Claudia d'Amato, Università degli Studi di Bari
Francesco M. Donini, Università della Tuscia, Viterbo



Simona Colucci, Ph.D.
Assistant Professor
Politecnico di Bari
Department of Electrical and Information Engineering
Information Systems <http://sisinflab.poliba.it/> Research Group
*Address*: via E. Orabona, 4 - 70125 Bari
*Tel:*  + 39 080 596 3641 <+39%20080%20596%203641>
*Fax*: + 39 080 596 3410 <+39%20080%20596%203410>
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