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{{Event
|Title=1st International Workshop on "Generalizing knowledge: from Machine Learning and Knowledge Representation to the Semantic Web"
|Start date=14/11/2017
|End date=17/11/2017
|Series=GenSW
|City=Bari
|Country=Italy
|Field=Semantic Web
|Type=workshop
|Homepage=https://sites.google.com/site/gensw2017/
}}

GenSW2017: 1st International Workshop on "Generalizing knowledge: from Machine Learning and Knowledge Representation to the Semantic Web"

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


=== IMPORTANT DATES ===

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


=== FOCUS ===

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 the available information. A third one is data interdependency 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 ===

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
    - Generalization methods for  finding commonalities  and differences in the Semantic Web
    - Generalization methods for enrichment Semantic Web knowledge bases
    - 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, and not exceed 12 pages (full papers) or 4 pages (position papers) plus bibliography.
Papers must be submitted via easychair: https://easychair.org/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 a SPECIAL ISSUE for the journal “Semantic Web – Interoperability, Usability, Applicability" (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
End date	November 17, 2017 +
Event in series	GenSW +
Event type	workshop +
Has coordinates	41° 7' 33", 16° 51' 43"Latitude: 41.125783333333 Longitude: 16.862030555556 +
Has location city	Bari +
Has location country	Category:Italy +
Homepage	https://sites.google.com/site/gensw2017/ +
IsA	Event +
Start date	November 14, 2017 +
Title	1st International Workshop on "Generalizing knowledge: from Machine Learning and Knowledge Representation to the Semantic Web" +