Canonicalizing Knowledge Base Literals

Ontology-based knowledge bases (KBs) like DBpedia are very valuable resources, but their usefulness and usability is limited by various quality issues. One such issue is the use of string literals instead of semantically typed entities. In this paper we study the automated canonicalization of such literals, i.e., replacing the literal with an existing entity from the KB or with a new entity that is typed using classes from the KB. We propose a framework that combines both reasoning and machine learning in order to predict the relevant entities and types, and we evaluate this framework against state-of-the-art baselines for both semantic typing and entity matching

Semantic Web Datatype Inference: Towards Better RDF Matching

International audienceIn the context of RDF document matching/integration, the datatype information, which is related to literal objects, is an important aspect to be analyzed in order to better determine similar RDF documents. In this paper, we propose a datatype inference process based on four steps: (i) predicate information analysis (i.e., deduce the datatype from existing range property); (ii) analysis of the object value itself by a pattern-matching process (i.e., recognize the object lexical-space); (iii) semantic analysis of the predicate name and its context; and (iv) generalization of numeric and binary datatypes to ensure the integration. We evaluated the performance and the accuracy of our approach with datasets from DBpedia. Results show that the execution time of the inference process is linear and its accuracy can increase up to 97.10%. \textcopyright 2017, Springer International Publishing AG

Linked Data Entity Summarization

On the Web, the amount of structured and Linked Data about entities is constantly growing. Descriptions of single entities often include thousands of statements and it becomes difficult to comprehend the data, unless a selection of the most relevant facts is provided. This doctoral thesis addresses the problem of Linked Data entity summarization. The contributions involve two entity summarization approaches, a common API for entity summarization, and an approach for entity data fusion

An entropy-based class assignment detection approach for RDF data

The RDF-style Knowledge Bases usually contain a certain level of noises known as Semantic Web data quality issues. This paper has introduced a new Semantic Web data quality issue called Incorrect Class Assignment problem that shows the incorrect assignment between instances in the instance-level and corresponding classes in an ontology. We have proposed an approach called CAD (Class Assignment Detector) to find the correctness and incorrectness of relationships between instances and classes by analyzing features of classes in an ontology. Initial experiments conducted on a dataset demonstrate the effectiveness of CAD

RiAiR: A Framework for Sensitive RDF Protection

International audienceThe Semantic Web and the Linked Open Data (LOD) initiatives promote the integration and combination of RDF data on the Web. In some cases, data need to be analyzed and protected before publication in order to avoid the disclosure of sensitive information. However, existing RDF techniques do not ensure that sensitive information cannot be discovered since all RDF resources are linked in the Semantic Web and the combination of different datasets could produce or disclose unexpected sensitive information. In this context, we propose a framework, called RiAiR, which reduces the complexity of the RDF structure in order to decrease the interaction of the expert user for the classification of RDF data into identifiers, quasi-identifiers, etc. An intersection process suggests disclosure sources that can compromise the data. Moreover, by a generalization method, we decrease the connections among resources to comply with the main objectives of integration and combination of the Semantic Web. Results show a viability and high performance for a scenario where heterogeneous and linked datasets are present