Dear Anna Lisa, We recently updated our CFP posting policy on the DBpedia mailing lists. As a recap, we only allow CFPs explicitly related to DBpedia on the discussion list (for example, you mention DBpedia in your CFP or make a personalised introduction)
You may find the decision here and please keep it in mind for future postings http://www.mail-archive.com/dbpedia-discussion@lists.sourceforge.net/msg07775.html Best regards, Dimitris On Fri, Nov 25, 2016 at 3:24 PM, Anna Lisa Gentile < annal...@informatik.uni-mannheim.de> wrote: > > > > *--------------------------- SEMANTIC WEB JOURNAL - Call for papers: > SPECIAL ISSUE ON Linked Data for Information Extraction > --------------------------- *Submission deadline: 07 April 2017, > Hawaii-Time > -------------------- > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > > * Information Extraction (IE) is the task of automatically extracting > structured information from unstructured and/or semi-structured > machine-readable documents. It is a crucial technology to enable the > Semantic Web vision. One of the major bottlenecks for the current state of > the art in IE is the availability of learning materials (e.g., seed data, > training corpora), which, traditionally are manually created but are > expensive to build and maintain. Linked Data (LD) defines best practices > for exposing, sharing, and connecting data, information, and knowledge on > the Semantic Web using uniform means such as URIs and RDF. It has so far > created a gigantic knowledge source of Linked Open Data (LOD), which now > constitutes billions of triples (facts). This has created unprecedented > opportunities for Information Extraction. Linked Data offers a uniform > approach to link resources uniquely identifiable by URIs. This creates a > large knowledge base of entities and concepts, connected by semantic > relations. Such resources can be valuable resources to seed distant > learning. Moreover, initiatives such as RDFa (supported by W3C) or > Microformats (used by schema.org <http://schema.org> and supported by major > search engines) constantly produce a vast amount of annotated web pages > which can be used as training data in the traditional machine learning > paradigm. However, powering IE using LOD faces major challenges. First, > discovering relevant learning materials on LOD for specific IE tasks is > non-trivial due to (i) the highly heterogeneous vocabularies used by data > publishers and (ii) the lack of contextual information for annotated > content on web pages (e.g., annotations often predominantly found in page > headers) and the skewed distribution towards popular entities. Users are > often required to be familiar with the datasets, vocabularies, as well as > query languages that data publishers use to expose their data. > Unfortunately, considering the sheer size and the diversity of LOD, > imposing such requirements on users is infeasible. Second, it is known that > the coverage of domains can be very imbalanced and for certain domains the > data can be very sparse. Furthermore, the majority of LOD are created > automatically by converting legacy databases with limited or no human > validation, thus data inconsistency and redundancy are widespread. Another > crucial aspect in IE research is the shift of attention from purely > unstructured text to semi-structured content. Two main source of interest > are Web tables and Open Data (often available as csv files). These data are > particularly rich of content and relations but often lack contextual data, > often used in classical IE methods. The aim of this special issue is to > foster research on methodologies that exploit Linked Data for Information > Extraction, to answer questions such as: to what extent can we identify > domain-specific learning resources for IE; how to identify and deal with > noise in the learning resources; how can these learning resources be used > to train IE models, both for classical unstructured text and for > semi-structured content; and how should the information extracted by such > models integrate into the existing LOD. Topics of Interest > ------------------------------ We solicit original papers addressing the > challenges and research questions mentioned above. Topics of interest are > listed (but not limited to) the ones below. Note that work must make use of > Linked Data of any form and must be related to Information Extraction in > some way. Please contact the editors if in doubt. - Methods for generating > seed data for IE (e.g., distant supervision) from Linked Data - Methods for > identifying labelled data for IE from the annotated webpage content under > the initiative such as RDFa and Microdata format (schema.org > <http://schema.org>) - IE tasks exploiting Linked Data in any form, such as > (not limited to) * wrapper induction * table annotation * named > entity recognition * relation extraction * ontology population, > ontology expansion (A-box) * ontology learning (T-box) - Methods for > identifying and reducing noise in the context of IE tasks - Disambiguation > using Linked Data - IE for knowledge graph construction Submission > Instructions ----------------------------- Submissions shall be made > through the Semantic Web journal website at > http://www.semantic-web-journal.net <http://www.semantic-web-journal.net>. > Prospective authors must take notice of the submission guidelines posted at > http://www.semantic-web-journal.net/authors > <http://www.semantic-web-journal.net/authors>. Note that you need to > request an account on the website for submitting a paper. Please indicate > in the cover letter that it is for the "Linked Data for Information > Extraction" special issue. All manuscripts will be reviewed based on the > SWJ open and transparent review policy and will be made available online > during the review process. Guest editors -------------------- Anna Lisa > Gentile, University of Mannheim, Germany Ziqi Zhang, Nottingham Trent > University, UK The call is also available at the official journal website: > http://www.semantic-web-journal.net/blog/call-papers-special-issue-linked-data-information-extraction > <http://www.semantic-web-journal.net/blog/call-papers-special-issue-linked-data-information-extraction> > * > > -- > Anna Lisa Gentile > Postdoctoral Researcher > Data and Web Science Group > University of Mannheimhttps://w3id.org/people/annalisa > email: annal...@informatik.uni-mannheim.de > office: +49 621 181 2646 > skype: anlige > > > ------------------------------------------------------------ > ------------------ > > _______________________________________________ > DBpedia-discussion mailing list > DBpedia-discussion@lists.sourceforge.net > https://lists.sourceforge.net/lists/listinfo/dbpedia-discussion > > -- Kontokostas Dimitris
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