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.

Valid Time RDF

The Semantic Web aims at building a foundation of semantic-based data models and languages for not only manipulating data and knowledge, but also supporting decision making by machines. Naturally, time-varying data and knowledge are required in Semantic Web applications to incorporate time and further reason about it. However, the original specifications of Resource Description Framework (RDF) and Web Ontology Language (OWL) do not include constructs for handling time-varying data and knowledge. For simplicity, RDF model is confined to binary predicates, hence some form of reification is needed to represent higher-arity predicates. To this date, there are many proposals extending RDF and OWL for handling temporal data and knowledge. They all focus on the valid time. Some of these proposals stay within the standards whereas others add new constructs to RDF and its query language, SPARQL. We first study these models in a comparative framework and develop a taxonomy for classifying them. On this basis, we propose a new temporal data model, Valid Time RDF, or VTRDF, that incorporates valid time explicitly into RDF. We define valid time resources as the building blocks of VTRDF. Our approach treats all resources in VTRDF uniformly, which is significant in that the need of RDF reification is eliminated. In particular, using VTRDF to handle temporal data and knowledge requires no additional triples or objects. We formally define valid time triples and graphs, which are subject to the Temporal Triple Integrity, and the formal semantics for the layered sets of VTRDF vocabularies. To query VTRDF triple databases, we design a query language, VT-SPARQL, that extends the standard SPARQL to handle valid time resources, time intervals, and temporal reasoning. We have also shown that space and time complexity of VTRDF, and the time complexity of the evaluating VT-SPARQL queries

Towards a representation of temporal data in archival records: Use cases and requirements

Archival records are essential sources of information for historians and digital humanists to understand history. For modern information systems they are often analysed and integrated into Knowledge Graphs for better access, interoperability and re-use. However, due to restrictions of the representation of RDF predicates temporal data within archival records is a challenge to model. This position paper explains requirements for modeling temporal data in archival records based on running research projects in which archival records are analysed and integrated in Knowledge Graphs for research and exploration

Towards a representation of temporal data in archival records: Use cases and requirements

Archival records are essential sources of information for historians and digital humanists to understand history. For modern information systems they are often analysed and integrated into Knowledge Graphs for better access, interoperability and re-use. However, due to restrictions of the representation of RDF predicates temporal data within archival records is a challenge to model. This position paper explains requirements for modeling temporal data in archival records based on running research projects in which archival records are analysed and integrated in Knowledge Graphs for research and exploration

A Semantic loT Early Warning System for Natural Environment Crisis Management

An early warning system (EWS) is a core type of data driven Internet of Things (IoTs) system used for environment disaster risk and effect management. The potential benefits of using a semantic-type EWS include easier sensor and data source plug-and-play, simpler, richer, and more dynamic metadata-driven data analysis and easier service interoperability and orchestration. The challenges faced during practical deployments of semantic EWSs are the need for scalable time-sensitive data exchange and processing (especially involving heterogeneous data sources) and the need for resilience to changing ICT resource constraints in crisis zones. We present a novel IoT EWS system framework that addresses these challenges, based upon a multisemantic representation model.We use lightweight semantics for metadata to enhance rich sensor data acquisition.We use heavyweight semantics for top level W3CWeb Ontology Language ontology models describing multileveled knowledge-bases and semantically driven decision support and workflow orchestration. This approach is validated through determining both system related metrics and a case study involving an advanced prototype system of the semantic EWS, integrated with a reployed EWS infrastructure

A Semantic IoT Early Warning System for Natural Environment Crisis Management

This work was supported in part by the European FP7 Funded Project TRIDEC under Grant 258723, the other project partners in helping to deliver the complete project Syste, in particular, GFZ, and the German Research Centre for Geosciences, Potsdam, Germany. The work of R. Tao was supported by the Queen Mary University of London for a Ph.D. studentship

BiTRDF: Extending RDF for BiTemporal Data

The Internet is not only a platform for communication, transactions, and cloud storage, but it is also a large knowledge store where people as well as machines can create, manipulate, infer, and make use of data and knowledge. The Semantic Web was developed for this purpose. It aims to help machines understand the meaning of data and knowledge so that machines can use the data and knowledge in decision making. The Resource Description Framework (RDF) forms the foundation of the Semantic Web which is organized as the Semantic Web Layer Cake. RDF is limited and can only express a binary relationship with the format of . However, expressing higher order relationships requires reification which is very cumbersome. Naturally, time varying data is very common and cannot be represented by only binary relationships. We first surveyed approaches that use reification or extend RDF for higher order relationships. Then we proposed a new data model, BiTemporal RDF (BiTRDF), that incorporates both valid time and transaction time explicitly into standard RDF resources. We defined the BiTRDF model with its elements, vocabulary, semantics, and entailment, and the BiTemporal SPARQL (BiT-SPARQL) query language. We discussed the foundation for implementing BiTRDF and we also explored different approaches to implement the BiTRDF model. We concluded this thesis with potential research directions. This thesis lays the foundation for a new approach to easily embed any or more dimensions, such as temporal data, spatial data, probabilistic data, confidence levels, etc

Reasoning About Temporal Constraints in RDF

Abstract. Time management is a key feature needed in any query language for web and semistructured data. However, only recently this has been addressed by the Semantic Web community, through the study of temporal extensions to RDF (Resource Description Framework). In this paper we show that the ability of the RDF data model of handling unknown resources by means of blank nodes, naturally yields a rich framework for temporal reasoning in RDF. That is, even without knowing the interval of validity of some statements we can still entail useful knowledge from temporal RDF databases. To take advantage of this, we incorporate a class of temporal constraints over anonymous timestamps. We show that testing entailment in temporal graphs with constraints reduces to closure computation and mapping discovery, that is, an extended form of the standard approach for testing entailment in non-temporal RDF graphs.