Mining the Semantic Web with Machine Learning: Main Issues that Need to Be Known

The Semantic Web (SW) is characterized by the availability of a vast amount of semantically annotated data collections. Annotations are provided by exploiting ontologies acting as shared vocabularies. Additionally ontologies are endowed with deductive reasoning capabilities which allow to make explicit knowledge that is formalized implicitly. Along the years a large number of data collections have been developed and interconnected, as testified by the Linked Open Data Cloud. Currently, seminal examples are represented by the numerous Knowledge Graphs (KGs) that have been built, either as enterprise KGs or open KGs, that are freely available. All of them are characterized by very large data volumes, but also incompleteness and noise. These characteristics have made the exploitation of deductive reasoning services less feasible from a practical viewpoint, opening up to alternative solutions, grounded on Machine Learning (ML), for mining knowledge from the vast amount of information available. Actually, ML methods have been exploited in the SW for solving several problems such as link and type prediction, ontology enrichment and completion (both at terminological and assertional level), and concept leaning. Whilst initially symbol-based solutions have been mostly targeted, recently numeric-based approaches are receiving major attention because of the need to scale on the very large data volumes. Nevertheless, data collections in the SW have peculiarities that can hardly be found in other fields. As such the application of ML methods for solving the targeted problems is not straightforward. This paper extends [20], by surveying the most representative symbol-based and numeric-based solutions and related problems, with a special focus on the main issues that need to be considered and solved when ML methods are adopted in the SW field as well as by analyzing the main peculiarities and drawbacks for each solution.