<div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div><div class="m_-1415486771821198784m_3778546924395444620m_1840613165537529635gmail_signature"><div dir="ltr"><div><div dir="ltr"><div><div dir="ltr"><div><div><div><font color="#000000">[<span class="m_-1415486771821198784m_3778546924395444620m_1840613165537529635gmail-m_2071514696854025527gmail-m_6855463999965956031gmail-m_-3431314144706948037gmail-il" style="font-size:12.8px">Apologies</span><span style="font-size:12.8px"> for cross-posting.</span>]<br></font></div></div></div></div></div></div></div></div></div></div><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><div class="gmail_quote"><div dir="ltr"><font color="#000000"><span class="m_-1415486771821198784m_3778546924395444620m_1840613165537529635gmail-"><div><br></div>***First International Workshop on "Generalizing knowledge: from Machine Learning and Knowledge Representation to the Semantic Web" (GenSW2017)***<br> <br>In conjunction with with "The 16th International Conference of the Italian Association for Artificial Intelligence" (AI*IA 2017), Bari, Italy, November 14 - 17 2017.<br><br>Website: <a href="https://sites.google.com/site/gensw2017/" target="_blank">https://sites.google.com/site/<wbr>gensw2017/</a><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><br></span>***Important Dates*** <br><br>Abstract submission: 13 July 2017<br>Paper submission: 18 July 2017<br>Notification to authors: 8 September 2017<br>Camera-ready copies: 29 September 2017<span class="m_-1415486771821198784m_3778546924395444620m_1840613165537529635gmail-"><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span style="font-size:13.3333px">***Call for Papers***</span><br></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US">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).</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US">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.</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US">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</span><span lang="EN-US" style="font-family:Arial,sans-serif"></span><span lang="EN-US">available information. A third one is data interdependencies stemming from RDFS statements.</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><br></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US">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.</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US">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. </span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin-bottom:0.0001pt"><span style="background-color:transparent;font-size:10pt">Topics of interest include, but are not limited to:</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US" style="font-family:Symbol">·<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">KR and/or ML methods (possibly in combination) for generalizing in the Semantic Web</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US" style="font-family:Symbol">·<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Semi-supervised, unbalanced, inductive learning for generalizing in the Semantic Web</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US" style="font-family:Symbol">·<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Reasoning services for generalization in the Semantic Web</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 54pt"><span lang="EN-US">1.<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Generalization methods for finding commonalities and differences in the Semantic Web</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 54pt"><span lang="EN-US">2.<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Generalization methods for enrichment Semantic Web knowledge bases</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 54pt"><span lang="EN-US">3.<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Generalization methods for indexing in the Linked Data Cloud</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US" style="font-family:Symbol">·<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Evaluation and benchmarking of generalization approaches in the Semantic Web</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US" style="font-family:Symbol">·<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Scalable algorithms for generalizing the Web of Data</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"></span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US" style="font-family:Symbol">·<span style="font-stretch:normal;font-size:7pt;line-height:normal;font-family:"Times New Roman""> </span></span><span lang="EN-US">Generalization in presence of uncertain/inconsistent/noisy knowledge</span></p><p style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px;margin:0cm 0cm 0.0001pt 36pt"><span lang="EN-US"><br></span></p><div style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px"><span style="font-size:13.3333px;background-color:transparent">Papers should be written in English, formatted according to the</span><span style="font-size:13.3333px;background-color:transparent"> </span><a href="https://www.springer.com/gp/computer-science/lncs/conference-proceedings-guidelines" rel="nofollow" style="font-size:13.3333px;background-color:transparent" target="_blank">Springer LNCS style</a><span style="font-size:13.3333px;background-color:transparent">, and not exceed 12 pages </span><span style="font-size:13.3333px;background-color:transparent">(full papers) or 4 pages (position papers) plus bibliography.</span></div><div style="font-family:Arial,Verdana,sans-serif;font-size:13.3333px"><div><span style="font-size:13.3333px">Papers must be submitted via easychair</span>: <a href="https://easychair.org/conferences/?conf=gensw2017" rel="nofollow" target="_blank">https://easychair.o<wbr>rg/conferences/?conf=gensw2017</a><wbr>. </div><div style="font-size:13.3333px"><br></div><div><div style="font-size:13.3333px"><span style="background-color:transparent;font-size:13.3333px">All accepted papers will be scheduled for oral presentations and will</span><span style="background-color:transparent;font-family:"Libre Franklin","Helvetica Neue",helvetica,arial,sans-serif;font-size:16px;text-align:justify"> </span><span style="background-color:transparent;font-size:13.3333px">be published in CEUR Workshop Proceedings AI*IA Series.</span></div><div style="font-size:13.3333px"><span style="background-color:transparent;font-size:13.3333px"><br></span></div><div><b><span style="font-size:10pt;background-color:transparent">Authors of selected papers accepted to the workshop will be invited to submit an extended version for publication on the journal </span>“Semantic Web – Interoperability, Usability, Applicability" (<a href="http://www.semantic-web-journal.net/" rel="nofollow" target="_blank">http://www.semantic-web-journ<wbr>al.net/</a>). Papers selected for the special issue have to go through a full review process before acceptanc<span style="font-size:12.8px;font-family:arial,sans-serif;background-color:transparent">e.</span></b></div></div><div style="font-size:13.3333px"><br></div><div style="font-size:13.3333px"><br></div><div><div><p style="font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US">***Organizing Committee***</span></p><p style="font-size:13.3333px;margin:0cm 0cm 0.0001pt;text-align:justify;line-height:normal"><span lang="EN-US"><br></span></p><font style="font-family:arial,sans-serif;font-size:small">Simona Colucci, Politecnico di Bari<br>Claudia d'Amato, Università degli Studi di Bari<br>Francesco M. Donini, Università della Tuscia, Viterbo</font><span style="font-size:13.3333px"><br></span></div><div><font style="font-family:arial,sans-serif;font-size:small"><br></font></div></div></div></span></font></div>
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</div><font color="#000000"><br></font></div><div dir="ltr"><span style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><font color="#000000">Simona Colucci, Ph.D.</font></span><div><font face="Helvetica, Arial, Verdana, serif" color="#000000">Assistant Professor<br></font><div><font face="Helvetica, Arial, Verdana, serif" color="#000000">Politecnico di Bari<br></font><div><font color="#000000">Department of Electrical and Information Engineering<br style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><a href="http://sisinflab.poliba.it/" style="text-decoration-line:none;border:0px solid;font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px" target="_blank">Information Systems</a><span style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"> Research Group</span><br style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><span style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><b>Address</b>: via E. Orabona, 4 - 70125 Bari</span><br style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><span style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><b>Tel:</b> <a href="tel:+39%20080%20596%203641" value="+390805963641" target="_blank">+ 39 080 596 3641</a></span><br style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px"><strong style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px">Fax</strong><span style="font-family:Helvetica,Arial,Verdana,serif;font-size:12.8px">: <a href="tel:+39%20080%20596%203410" value="+390805963410" target="_blank">+ 39 080 596 3410</a></span></font></div></div></div></div></div>
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