Transforming XML to RDF(S) with Temporal Information

The Resource Description Framework (RDF) is a model for representing resources on the Web. With the widespread acceptance of RDF in various applications (e.g., knowledge graph), a huge amount of RDF data is being proliferated. Therefore, transforming legacy data resources into RDF data is of increasing importance. In addition, time information widely exists in various real-world applications and temporal Web data has been represented and managed in the context of temporal XML. In this paper, we concentrate on transformation of temporal XML (eXtensible Markup Language) to temporal RDF data. We propose the mapping rules and mapping algorithms which can transform the temporal XML Schema and document into temporal RDF Schema and temporal RDF triples, respectively. We illustrate our mapping approach with an example and implement a prototype system. It is demonstrated that our mapping approach is valid

Towards the Temporal Streaming of Graph Data on Distributed Ledgers

We present our work-in-progress on handling temporal RDF graph data using the Ethereum distributed ledger. The motivation for this work are scenarios where multiple distributed consumers of streamed data may need or wish to verify that data has not been tampered with since it was generated – for example, if the data describes something which can be or has been sold, such as domestically-generated electricity. We describe a system in which temporal annotations, and information suitable to validate a given dataset, are stored on a distributed ledger, alongside the results of fixed SPARQL queries executed at the time of data storage. The model adopted implements a graph-based form of temporal RDF, in which time intervals are represented by named graphs corresponding to ledger entries. We conclude by discussing evaluation, what remains to be implemented, and future directions

Temporal RDF(S) Data Storage and Query with HBase

Resource Description Framework (RDF) is a metadata model recommended by World Wide Web Consortium (W3C) for describing the Web resources. With the arrival of the era of Big Data, very large amounts of RDF data are continuously being created and need to be stored for management. The traditional centralized RDF storage models cannot meet the need of largescale RDF data storage. Meanwhile, the importance of temporal information management and processing has been acknowledged by academia and industry. In this paper, we propose a storage model to store temporal RDF based on HBase. The proposed storage model applies the built-in time mechanism of HBase. Our experiments on LUBM dataset with temporal information added show that our storage model can store large temporal RDF data and obtain good query efficiency

TEMPORAL EXTENSIONS TO RDF

The Semantic Web is based on Resource Description Framework (RDF) which is widely used in practice. RDF represents information by only binary predicates. This simple representation scheme is the basis of an elaborate layers of methodologies, called Semantic Web Layer Cake. Though simple, it is very powerful for modeling data and basic knowledge. However, it is very limited in representing their temporal variation. Reification is the method proposed in RDF for modeling temporal changes in data and knowledge. Moreover, reification is cumbersome since it requires at least four more triples to represent just one temporal fact. By their very nature, RDF repositories are large in general and reification causes them to explode in size. In this paper, we review Semantic Web techniques that are proposed for representing temporal data in RDF.

Generating Natural Language from Linked Data:Unsupervised template extraction

We propose an architecture for generating natural language from Linked Data that automatically learns sentence templates and statistical document planning from parallel RDF datasets and text. We have built a proof-of-concept system (LOD-DEF) trained on un-annotated text from the Simple English Wikipedia and RDF triples from DBpedia, focusing exclusively on factual, non-temporal information. The goal of the system is to generate short descriptions, equivalent to Wikipedia stubs, of entities found in Linked Datasets. We have evaluated the LOD-DEF system against a simple generate-from-triples baseline and human-generated output. In evaluation by humans, LOD-DEF significantly outperforms the baseline on two of three measures: non-redundancy and structure and coherence.

A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data

We describe a generic framework for representing and reasoning with annotated Semantic Web data, a task becoming more important with the recent increased amount of inconsistent and non-reliable meta-data on the web. We formalise the annotated language, the corresponding deductive system and address the query answering problem. Previous contributions on specific RDF annotation domains are encompassed by our unified reasoning formalism as we show by instantiating it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we provide a generic method for combining multiple annotation domains allowing to represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the development of a query language -- AnQL -- that is inspired by SPARQL, including several features of SPARQL 1.1 (subqueries, aggregates, assignment, solution modifiers) along with the formal definitions of their semantics

On Reasoning with RDF Statements about Statements using Singleton Property Triples

The Singleton Property (SP) approach has been proposed for representing and querying metadata about RDF triples such as provenance, time, location, and evidence. In this approach, one singleton property is created to uniquely represent a relationship in a particular context, and in general, generates a large property hierarchy in the schema. It has become the subject of important questions from Semantic Web practitioners. Can an existing reasoner recognize the singleton property triples? And how? If the singleton property triples describe a data triple, then how can a reasoner infer this data triple from the singleton property triples? Or would the large property hierarchy affect the reasoners in some way? We address these questions in this paper and present our study about the reasoning aspects of the singleton properties. We propose a simple mechanism to enable existing reasoners to recognize the singleton property triples, as well as to infer the data triples described by the singleton property triples. We evaluate the effect of the singleton property triples in the reasoning processes by comparing the performance on RDF datasets with and without singleton properties. Our evaluation uses as benchmark the LUBM datasets and the LUBM-SP datasets derived from LUBM with temporal information added through singleton properties

EAGLE—A Scalable Query Processing Engine for Linked Sensor Data

Recently, many approaches have been proposed to manage sensor data using semantic web technologies for effective heterogeneous data integration. However, our empirical observations revealed that these solutions primarily focused on semantic relationships and unfortunately paid less attention to spatio–temporal correlations. Most semantic approaches do not have spatio–temporal support. Some of them have attempted to provide full spatio–temporal support, but have poor performance for complex spatio–temporal aggregate queries. In addition, while the volume of sensor data is rapidly growing, the challenge of querying and managing the massive volumes of data generated by sensing devices still remains unsolved. In this article, we introduce EAGLE, a spatio–temporal query engine for querying sensor data based on the linked data model. The ultimate goal of EAGLE is to provide an elastic and scalable system which allows fast searching and analysis with respect to the relationships of space, time and semantics in sensor data. We also extend SPARQL with a set of new query operators in order to support spatio–temporal computing in the linked sensor data context.EC/H2020/732679/EU/ACTivating InnoVative IoT smart living environments for AGEing well/ACTIVAGEEC/H2020/661180/EU/A Scalable and Elastic Platform for Near-Realtime Analytics for The Graph of Everything/SMARTE

A Reasoner for Calendric and Temporal Data

Calendric and temporal data are omnipresent in countless Web and Semantic Web applications and Web services. Calendric and temporal data are probably more than any other data a subject to interpretation, in almost any case depending on some cultural, legal, professional, and/or locational context. On the current Web, calendric and temporal data can hardly be interpreted by computers. This article contributes to the Semantic Web, an endeavor aiming at enhancing the current Web with well-defined meaning and to enable computers to meaningfully process data. The contribution is a reasoner for calendric and temporal data. This reasoner is part of CaTTS, a type language for calendar definitions. The reasoner is based on a \theory reasoning" approach using constraint solving techniques. This reasoner complements general purpose \axiomatic reasoning" approaches for the Semantic Web as widely used with ontology languages like OWL or RDF