- From: Christian Bizer <chris@bizer.de>
- Date: 2015年7月02日 09:58:47 +0200
- To: public-lod@w3.org, semantic-web@w3.org, public-vocabs@w3.org, dbpedia-discussion@lists.sourceforge.net, semanticweb@yahoogroups.com
- Message-ID: <20150702095847.Horde.PyVXq9UGNbfQhPUM_btj_5O@staff.webmail.uni-mannheim.de>
Hi all, we are happy to announce that the ACM Journal of Data and Information Quality (JDIQ) will feature a special issue on Web Data Quality. The goal of the special issue is to present innovative research in the areas of Web Data Quality Assessment and Web Data Cleansing. The submission deadline for the special issue is November 1st, 2015. Please find the detailed call for papers below and at http://jdiq.acm.org/announcements.cfm#special-issue-of-acm-jdiq-on-web-data-quality Best, Luna Dong, Ihab Ilyas, Maria-Esther Vidal, and Christian Bizer --------------------- Call for Papers: ACM Journal of Data and Information Quality (JDIQ) Special Issue on Web Data Quality --------------------- Guest editors: * Christian Bizer, University of Mannheim, Germany, chris@informatik.uni-mannheim.de * Luna Dong, Google, USA, lunadong@google.com * Ihab Ilyas, University of Waterloo, Canada, ilyas@uwaterloo.ca * Maria-Esther Vidal, Universidad Simon Bolivar, Venezuela, mvidal@umiacs.umd.edu Introduction: The volume and variety of data that is available on the web has risen sharply. In addition to traditional data sources and formats such as CSV files, HTML tables and deep web query interfaces, new techniques such as Microdata, RDFa, Microformats and Linked Data have found wide adoption. In parallel, techniques for extracting structured data from web text and semi-structured web content have matured resulting in the creation of large-scale knowledge bases such as NELL, YAGO, DBpedia, and the Knowledge Vault. Independent of the specific data source or format or information extraction methodology, data quality challenges persist in the context of the web. Applications are confronted with heterogeneous data from a large number of independent data sources while metadata is sparse and of mixed quality. In order to utilize the data, applications must first deal with this widely varying quality of the available data and metadata. Topics: The goal of this special issue of JDIQ is to present innovative research in the areas of Web Data Quality Assessment and Web Data Cleansing. Specific topics within the scope of the call include, but are not limited to, the following: WEB DATA QUALITY ASSESSMENT: * Metrics and methods for assessing the quality of web data, including Linked Data, Microdata, RDFa, Microformats and tabular data. * Methods for uncovering distorted and biased data / data SPAM detection. * Methods for quality-based web data source selection. * Methods for copy detection. * Methods for assessing the quality of instance- and schema-level links Linked Data. * Ontologies and controlled vocabularies for describing the quality of web data sources and metadata. * Best practices for metadata provision. * Cost and benefits of web data quality assessment and benchmarks. WEB DATA CLEANSING: * Methods for cleansing Web data, Linked Data, Microdata, RDFa, Microformats and tabular data. * Conflict resolution using semantic knowledge and truth discovery. * Human-in-the-loop and crowdsourcing for data cleansing. * Data quality for automated knowledge base construction. * Empirical evaluation of scalability and performance of data cleansing methods and benchmarks. APPLICATIONS AND USE CASES IN THE LIFE SCIENCES, HEALTHCARE, MEDIA, SOCIAL MEDIA, GOVERNMENT AND SENSOR DATA. Important dates: Initial submission: November 1, 2015 First review: January 15, 2016 Revised manuscripts: February 15, 2016 Second review: March 30, 2016 Publication: May 2016 Submission guidelines: http://jdiq.acm.org/authors.cfm -- Prof. Dr. Christian Bizer Data and Web Science Group University of Mannheim, Germany chris@informatik.uni-mannheim.de http://dws.informatik.uni-mannheim.de/bizer
Received on Thursday, 2 July 2015 07:59:12 UTC