Model Theory and Entailment Rules for RDF Containers, Collections and Reification

An RDF graph is, at its core, just a set of statements consisting of subjects, predicates and objects. Nevertheless, since its inception practitioners have asked for richer data structures such as containers (for open lists, sets and bags), collections (for closed lists) and reification (for quoting and provenance). Though this desire has been addressed in the RDF primer and RDF Schema specification, they are explicitely ignored in its model theory. In this paper we formalize the intuitive semantics (as suggested by the RDF primer, the RDF Schema and RDF semantics specifications) of these compound data structures by two orthogonal extensions of the RDFS model theory (RDFCC for RDF containers and collections, and RDFR for RDF reification). Second, we give a set of entailment rules that is sound and complete for the RDFCC and RDFR model theories. We show that complexity of RDFCC and RDFR entailment remains the same as that of simple RDF entailment

Using Description Logics for RDF Constraint Checking and Closed-World Recognition

RDF and Description Logics work in an open-world setting where absence of information is not information about absence. Nevertheless, Description Logic axioms can be interpreted in a closed-world setting and in this setting they can be used for both constraint checking and closed-world recognition against information sources. When the information sources are expressed in well-behaved RDF or RDFS (i.e., RDF graphs interpreted in the RDF or RDFS semantics) this constraint checking and closed-world recognition is simple to describe. Further this constraint checking can be implemented as SPARQL querying and thus effectively performed.Comment: Extended version of a paper of the same name that will appear in AAAI-201

A Study on the Correspondence between FCA and ELI Ontologies

Abstract. The description logic EL has been used to support ontology design in various domains, and especially in biology and medicine. EL is known for its efficient reasoning and query answering capabilities. By contrast, ontology design and query answering can be supported and guided within an FCA framework. Accordingly, in this paper, we propose a formal transformation of ELI (an extension of EL with inverse roles) ontologies into an FCA framework, i.e. KELI, and we provide a formal characterization of this transformation. Then we show that SPARQL query answering over ELI ontologies can be reduced to lattice query answering over KELI concept lattices. This simplifies the query answering task and shows that some basic semantic web tasks can be improved when considered from an FCA perspective

A Study on the Correspondence between FCA and ELI Ontologies

International audienceThe description logic EL has been used to support ontology design in various domains, and especially in biology and medicine. EL is known for its efficient reasoning and query answering capabilities. By contrast, ontology design and query answering can be supported and guided within an FCA framework. Accordingly, in this paper, we propose a formal transformation of ELI (an extension of EL with inverse roles) ontologies into an FCA framework, i.e. KELI, and we provide a formal characterization of this transformation. Then we show that SPARQL query answering over ELI ontologies can be reduced to lattice query answering over KELI concept lattices. This simplifies the query answering task and shows that some basic semantic web tasks can be improved when considered from an FCA perspective

Open ebusiness ontology usage: investigating community implementation of goodrelations

The GoodRelations Ontology is experiencing the first stages of mainstream adoption, with its appeal to a range of enterprises as the eCommerce ontology of choice to promote its offerings and product catalogue. As adoption increases, so too does the need to critically review and analyze current implementation of the ontology to better assist future usage and uptake. To comprehensively understand the implementation approaches, usage patterns, instance data and model coverage, data was collected from 105 different web based sources that have published their business and product-related information using the GoodRelations Ontology. This paper analyses the ontology usage in terms of data instantiation, and conceptual coverage using a SPARQL queries to evaluate quality, usefulness and inference provisioning. Experimental results highlight that early publishers of structured eCommerce data benefit more due to structured data being more readily search engine indexable, but the lack of available product ontologies and product master datasheets is impeding the creation of a semantically interlinked eCommerce Web

Secrecy-Preserving Reasoning Over Entailment Systems: Theory and Applications

Privacy, copyright, security and other concerns make it essential for many distributed web applications to support selective sharing of information while, at the same time, protecting sensitive knowledge. Secrecypreserving reasoning refers to the answering of queries against a knowledge base involving inference that uses sensitive knowledge without revealing it. We present a general framework for secrecy-preserving reasoning over arbitrary entailment systems. This framework enables reasoning with hierarchical ontologies, propositional logic knowledge bases (over arbitrary logics) and RDFS knowledge bases containing sensitive information that needs to be protected. We provide an algorithm that, given a knowledge base over an effectively enumerable entailment system, and a secrecy set over it, defines a maximally informative secrecypreserving reasoner. Secrecy-preserving mappings between knowledge bases that allow reusing reasoners across knowledge bases are introduced

Hierarchical Multi-Label Classification Using Web Reasoning for Large Datasets

Extracting valuable data among large volumes of data is one of the main challenges in Big Data. In this paper, a Hierarchical Multi-Label Classification process called Semantic HMC is presented. This process aims to extract valuable data from very large data sources, by automatically learning a label hierarchy and classifying data items.The Semantic HMC process is composed of five scalable steps, namely Indexation, Vectorization, Hierarchization, Resolution and Realization. The first three steps construct automatically a label hierarchy from statistical analysis of data. This paper focuses on the last two steps which perform item classification according to the label hierarchy. The process is implemented as a scalable and distributed application, and deployed on a Big Data platform. A quality evaluation is described, which compares the approach with multi-label classification algorithms from the state of the art dedicated to the same goal. The Semantic HMC approach outperforms state of the art approaches in some areas