Functional Dependencies in OWL ABox

Functional Dependency (FD) has been extensively studied in database theory. Most recently there have been some works investigating the implications of extending Description Logics with functional dependencies. In particular the OWL ontology language offers the functional property property allowing simple functional dependency to be specified. As it turns out, more complex FD specified as concept constructors has been proved to lead to undecidability in the general case, which restricts its usage as part of TBOX. This paper departs from previous ones by restricting FDs applicability to instances in the ABOX. We specify FD as a new constructor, an OWL concept. FD instances are mapped to Horn clauses and evaluated against the ABOX according to user’s desired behavior. The latter allows users to determine whether FDs should be interpreted as constraints, assertions or views. Our approach gives ontology users data guarantees usually found in databases, integrated with the ontology conceptual model

Enabling Ontology-based data access to streaming sources

The availability of streaming data sources is progressively increasing thanks to the development of ubiquitous data capturing tech- nologies such as sensor networks. The heterogeneity of these sources in- troduces the requirement of providing data access in a uni ed and co- herent manner, whilst allowing the user to express their needs at an ontological level. In this paper we describe an ontology-based streaming data access service. Sources link their data content to ontologies through s2o mappings. Users can query the ontology using sparqlStream, an ex- tension of sparql for streaming data. A preliminary implementation of the approach is also presented. With this proposal we expect to set the basis for future e orts in ontology-based streaming data integration

Demo paper: tablet-based visualization of transportation data in Madrid using SPARQLStream

In this demo paper we describe an iOS-based application that allows visualizing live bus transport data in Madrid from static and streaming RDF endpoints, reusing the Web services provided by the bus transport authority in the city and wrapping them using SPARQLStrea

NOR2O: a Library for Transforming Non-Ontological Resources to Ontologies

With the goal of speeding up the ontology development pro- cess, ontology engineers are starting to reuse and transform as much as possible available non-ontological resources, such as classication schemes, thesauri, lexicons, etc. Within the NeOn project we propose a method for re-engineering non-ontological resources into ontologies. This method is based on the so-called re-engineering patterns. This paper presents the description of the software library, that implements the transformations suggested by the patterns

RDF Stream Processing: Let's React

Stream processing has recently gained a prominent role in Computer Science research. From networks or databases to information theory or programming languages, a lot of work has been dedicated to conceive ways of representing, transmitting, processing and understanding infinite sequences of data. Nevertheless, there are still aspects that need time to reach a mature state. In particular, heterogeneity in stream data management and event processing is both a challenging topic and a key enabler for the rising Web of Things, where smart devices continuously sense properties of the surrounding world. Different proposals on RDF and Linked Data streams have shown promising results for managing this type of data, while keeping explicit semantics on the data streams, and linking them to other datasets in a web-friendly way. With time, these efforts led to the emergence of initiatives such as the RDF Stream Processing (RSP) W3C community group, aiming at specifying a base RDF stream model and query language for that model. Although these works produced interest results in defining overarching model definitions, there are still multiple orthogonal challenges that need to be addressed. In this work we identify some of these challenges, and we link them to the characteristics of what are nowadays called reactive systems. This paradigm includes natively supporting event-driven asynchronous message passing, non-blocking data communication and processing through all layers, and on-demand flexible scalability. We argue that RDF stream systems, combined with reactive techniques can lead to powerful, resilient and interoperable systems at Web scale

Short paper: From streaming data to linked data. A case study with bike sharing systems

Current methods and tools that support Linked Data publication have mainly focused so far on static data, without considering the growing amount of streaming data available on the Web. In this paper we describe a case study that involves the publication of static and streaming Linked Data for bike sharing systems and related entities. We describe some of the challenges that we have faced, the solutions that we have explored, the lessons that we have learned, and the opportunities that lie in the future for exploiting Linked Stream Data

Scalable and Reactive Programming for Semantic Web Developers

The Semantic Web brings a powerful set of concepts, standards and ideas that are already changing the shape of the Web. However, in order to put these notions into practice we need to translate them into code. That is why the broad notion of programming the Semantic Web is important, even if it remains challenging to provide the appropriate tools for developers to implement these ideas. In this work, we present our experience in a series of common Semantic Web programming tasks, and how we improve the developer experience and productivity by providing a simple Domain Specific Language (DSL) in Scala. We exemplify its use through fundamental tasks such as dealing with RDF data and ontologies, or performing reasoning and querying using JVM languages. In particular, we show how we can profit from the intrinsic extensibility of the Scala language to simplify these tasks using a custom DSL, at the same time that we introduce principles of reactivity and scalability in the code

Enabling query technologies for the semantic sensor web

Sensor networks are increasingly being deployed in the environment for many different purposes. The observations that they produce are made available with heterogeneous schemas, vocabularies and data formats, making it difficult to share and reuse this data, for other purposes than those for which they were originally set up. The authors propose an ontology-based approach for providing data access and query capabilities to streaming data sources, allowing users to express their needs at a conceptual level, independent of implementation and language-specific details. In this article, the authors describe the theoretical foundations and technologies that enable exposing semantically enriched sensor metadata, and querying sensor observations through SPARQL extensions, using query rewriting and data translation techniques according to mapping languages, and managing both pull and push delivery modes

On correctness in RDF stream processor benchmarking

Two complementary benchmarks have been proposed so far for the evaluation and continuous improvement of RDF stream processors: SRBench and LSBench. They put a special focus on different features of the evaluated systems, including coverage of the streaming extensions of SPARQL supported by each processor, query processing throughput, and an early analysis of query evaluation correctness, based on comparing the results obtained by different processors for a set of queries. However, none of them has analysed the operational semantics of these processors in order to assess the correctness of query evaluation results. In this paper, we propose a characterization of the operational semantics of RDF stream processors, adapting well-known models used in the stream processing engine community: CQL and SECRET. Through this formalization, we address correctness in RDF stream processor benchmarks, allowing to determine the multiple answers that systems should provide. Finally, we present CSRBench, an extension of SRBench to address query result correctness verification using an automatic method

Efficient RDF Interchange (ERI) format for RDF data streams

RDF streams are sequences of timestamped RDF statements or graphs, which can be generated by several types of data sources (sensors, social networks, etc.). They may provide data at high volumes and rates, and be consumed by applications that require real-time responses. Hence it is important to publish and interchange them efficiently. In this paper, we exploit a key feature of RDF data streams, which is the regularity of their structure and data values, proposing a compressed, efficient RDF interchange (ERI) format, which can reduce the amount of data transmitted when processing RDF streams. Our experimental evaluation shows that our format produces state-of-the-art streaming compression, remaining efficient in performance