The rapid explosion of linked data has resulted into many weakly structured and incomplete data sources, where typing information might be missing. On the other hand, type information is essential for a number of tasks such as query answering, integration, summarization and partitioning. Existing approaches for type discovery, either completely ignore type declarations available in the dataset (implicit type discovery approaches), or rely only on existing types, in order to complement them (explicit type enrichment approaches). Implicit type discovery approaches are based on instance grouping, which requires an exhaustive comparison between the instances. This process is expensive and not incremental. Explicit type enrichment approaches on the other hand, are not able to identify new types and they can not process data sources that have little or no schema information. In this paper, we present HInT, the first incremental and hybrid type discovery system for RDF datasets, enabling type discovery in datasets where type declarations are missing. To achieve this goal, we incrementally identify the patterns of the various instances, we index and then group them to identify the types. During the processing of an instance, our approach exploits its type information, if available, to improve the quality of the discovered types by guiding the classification of the new instance in the correct group and by refining the groups already built. We analytically and experimentally show that our approach dominates in terms of efficiency, competitors from both worlds, implicit type discovery and explicit type enrichment while outperforming them in most of the cases in terms of quality.

A growing number of data sources expressed in RDF(S)/OWL are available on the Web. They are increasingly used in big data and real-time applications. These data sources may be created without formally defining their schema, which is implicit in the stored data. The instances of a source do not have to conform to the schema when it is defined. This offers more flexibility and eases data evolution. However, it comes at the cost of losing the description of the data, which can be useful in many contexts. In this paper, we present SchemaDecrypt, a novel approach for discovering a versioned schema for a remote data source. SchemaDecrypt enables the discovery of the different structures of the existing classes. Our approach discovers the versions on-line, without uploading or browsing the data source. It enables to overcome the source querying restrictions and the combinatorial explosion of the candidate versions. We present some experimental evaluations on DBpedia to demonstrate the performances of our approach.