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SEMANTIC WEB JOURNAL - Call for papers: SPECIAL ISSUE ON Machine Learning for Knowledge Base Generation and Population
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In the last decade, in the Semantic Web field, knowledge bases have attracted tremendous interest from both academia and industry and many large knowledge bases are now available. However, both generation of new knowledge and population of already existing knowledge bases with new facts face several challenges. Most of the time knowledge bases have been manually built, resulting in a highly specialistic and time consuming activity. Nevertheless, sources of unstructured and semi-structured data are still growing at a much faster rate than structured ones, as such it could be desirable to exploit such a large non-structured sources to populate structured knowledge bases. In the Semantic Web, a major cornerstone of knowledge bases are ontologies and schemas that play a key role for providing common vocabularies and for describing and constructing the Web of Data. However, nowadays, schema level and instance level data are often decoupled and as such can be out of sync, e.g., schema level knowledge may be inconsistent with the actual usage of its conceptual vocabulary in the assertions. In order to cope with this issue, the availability of automatic methods for schema aware generation and population of knowledge bases results fundamental. Furthermore, even in the cases of largely populated knowledge bases, they still often result incomplete and/or noisy with respect to the domain of reference. Automatic methods for dealing with such problems, namely for enriching and completing knowledge bases, both at schema and instance level are needed.


In this scenario, by exploiting evidence derived from the data, new machine learning and data mining methods, that are able to deal with the heterogeneity, the intrinsic uncertainty and complexity of Semantic Web data, can be used for: learning new concept definitions, capturing emerging concepts (only extensionally defined) and/or concepts drift, predicting new links among resources and new assertions, discovering matches among resources and many others, with the final goal of constructing new knowledge bases, enriching existing ones, supporting their continuous evolution.

The primary goal of the special issue is to provide novel machine learning/data mining methods for knowledge base generation, population, enrichment, evolution showing advances in the Semantic Web field.



Topics of Interest
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We welcome original high quality submissions on (but are not restricted to) the following topics:
- Machine Learning for constructing, enriching, refining, maintaining, interlinking Semantic Web Knowledge Bases
- (Statistical) relational learning for the Web of Data
- Semi-supervised, unbalanced, inductive learning for mining and maintaining Semantic Web Knowledge Bases
- Data mining and knowledge discovery in Semantic Web Knowledge Bases
- Population of Knowledge Bases from unstructured and semi-structured sources
- Feature extraction, pre-processing and transformation of Semantic Web Knowledge Bases
- Machine Learning for ontology/instance matching
- Deep Learning for Semantic Web Knowledge Bases
- Scalable Machine Learning algorithms for the Web of Data
- Machine Learning methods for handling uncertain knowledge
- Combination of logic reasoning and machine learning for Knowledge Base construction, population and enrichment
- OWA vs. CWA in Knowledge Base generation, population and enrichment
- Link Prediction in the Linked Data Cloud
- Evaluation and benchmarking of machine learning models for Knowledge Base generation and population


Submission Instructions
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Submission deadline: December 12, 2016 Hawaii-Time
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Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at 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 Special Issue on Machine Learning for Knowledge Base Generation and Population.

Submissions are possible in the full research papers category. Papers describing application reports, tools and systems are also welcome, provided that the main contribution still remains an advance of the state of the art with respect to the research perspective. While there is no upper limit, paper length must be justified by content.


Guest editors
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Claudia d’Amato, University of Bari, Italy
Agnieszka Lawrynowicz, Poznan University of Technology, Poland
Jens Lehmann, University of Bonn and Fraunhofer IAIS, Germany