Semantics data Mining
The Journal of Web Semantics seeks submissions of original research papers for a special issue on machine learning and data mining for the Semantic Web dealing with analytical, theoretical, empirical, and practical aspects of machine learning and data mining for all areas of the Semantic Web. Submissions are due by by Feb 15, 2015.
In the last years, machine learning, as well as data mining approaches have become the main focus of many research works and initiatives related to the Semantic Web and the Web of Data. Challenges imposed by the large scale of Web Data, the uncertainty related to contradictory and incomplete information, and also, by properties and characteristics of Linked Data represent an interesting domain for emerging machine learning and data mining approaches.
For this special issue, we invite high quality contributions from all areas of research that address any aspects of the aforementioned challenges. Topics of interest include but are not limited to the following.
- Ontology-based data mining
- Automatic (and semi-automatic) ontology learning and population
- Distant-supervision (or weak-supervision) methods based on ontologies and knowledge bases
- Web mining using semantic information
- Meta-learning for the Semantic Web
- Cognitive-inspired approaches and exploratory search in the Semantic Web
- Discovery science involving linked data and ontologies
- Data mining and machine learning applied to information extraction in the semantic web
- Big Data analytics involving linked data
- Inductive reasoning on uncertain knowledge for the Semantic Web
- Ontology matching and instance matching using machine learning and data mining
- Data mining and knowledge discovery in the Web of data
- Knowledge base maintenance using Machine Learning and Data Mining
- Crowdsourcing and the Semantic Web
- Mining the social Semantic Web
The Journal of Web Semantics solicits original scientific contributions of high quality. Following the overall mission of the journal, we emphasize the publication of papers that combine theories, methods and experiments from different subject areas in order to deliver innovative semantic methods and applications. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate...