Automatic identity recognition in the Semantic Web

The OKKAM initiative1 has recently highlighted the need of moving from the traditional web towards a \web of entities", where real-world objects descriptions could be retrieved, univocally identified, and shared over the web. In this paper, we propose our vision of the entity recognition problem and, in particular, we propose methods and techniques to capture the \identity" of a real entity in the Semantic Web. We claim that automatic techniques are needed to compare different RDF descriptions of a domain with the goal of automatically detect heterogeneous descriptions of the same real-world objects. Problems and techniques to solve them are discussed together with some experimental results on a real case study on web data

Ontology Alignment at the Instance and Schema Level

We present PARIS, an approach for the automatic alignment of ontologies. PARIS aligns not only instances, but also relations and classes. Alignments at the instance-level cross-fertilize with alignments at the schema-level. Thereby, our system provides a truly holistic solution to the problem of ontology alignment. The heart of the approach is probabilistic. This allows PARIS to run without any parameter tuning. We demonstrate the efficiency of the algorithm and its precision through extensive experiments. In particular, we obtain a precision of around 90% in experiments with two of the world's largest ontologies.Comment: Technical Report at INRIA RT-040

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories

Effective Instance Matching for Heterogeneous Structured Data

One main problem towards the effective usage of structured data is instance matching, where the goal is to find instance representations referring to the same real-world thing. In this book we investigate how to effectively match Heterogeneous structured data. We evaluate our approaches against the latest baselines. The results show advances beyond the state-of-the-art

Automatic Identity Recognition in the Semantic Web ⋆

Abstract. The OKKAM initiative 1 has recently highlighted the need of moving from the traditional web towards a “web of entities”, where real-world objects descriptions could be retrieved, univocally identified, and shared over the web. In this paper, we propose our vision of the entity recognition problem and, in particular, we propose methods and techniques to capture the “identity ” of a real entity in the Semantic Web. We claim that automatic techniques are needed to compare different RDF descriptions of a domain with the goal of automatically detect heterogeneous descriptions of the same real-world objects. Problems and techniques to solve them are discussed together with some experimental results on a real case study on web data.