The Semantic Web represents a huge information space where an increasing number of datasets, described in RDF, are made available to users and applications. In this context, the data is not constrained by a predefined schema. In RDF datasets, the schema may be incomplete or even missing. While this offers high flexibility in creating data sources, it also makes their use difficult. Several works have addressed the problem of automatic schema discovery for RDF datasets, but existing approaches rely only on the explicit information provided by the data source, which may limit the quality of the results. Indeed, in an RDF data source, an entity is described by explicitly declared properties, but also by implicit properties that can be derived using reasoning rules. These implicit properties are not considered by existing schema discovery approaches. In this work, we propose a first contribution towards a hybrid schema discovery approach capable of exploiting all the semantics of a data source, which is represented not only by the explicitly declared triples, but also by the ones that can be inferred through reasoning. By considering both explicit and implicit properties, the quality of the generated schema is improved. We provide a scalable design of our approach to enable the processing of large RDF data sources while improving the quality of the results. We present some experiments which demonstrate the efficiency of our proposal and the quality of the discovered schema.

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.

Ridesharing services are getting a lot of attention in the recent years as they are beneficial for both travelers and drivers, and friendly to the environment. The problem is that these services are isolated from existing public transportation networks. They are proposed as alternative plans and not as part of a trip plan. Integrating these services may vastly improve the trips quality and serve as a backup plan in case delays or unexpected events happen. The main challenge facing the integration is limiting the search space and coping with the specific characteristics of ridesharing. This paper introduces RETRy, a system that enables the integration of ridesharing services with existing trip planners to provide real multi-modal trip planning solutions in near-real time.

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.

Integrating transportation data is a key issue to provide passengers with optimized and more suitable trips that combines multiple transportation modes. Current integration solutions in the transportation domain mostly rely on experts knowledge and manual matching tasks. Besides, existing automatic matching solutions do not exploit the geospatial features of the data. This demo introduces an instance based system to identify geospatial properties and match transportation points of transfers using geocoding services as mediators.