Temporal Enrichment and Querying of Ontology-Compliant Data

Lecture Notes in Computer Science, 2006

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.

Lecture Notes in Computer Science, 2009

We introduce TOQL, a query language for querying time information in ontologies. TOQL is a high level query language that handles ontologies almost like relational databases. Queries are issued as SQL-like statements involving time (i.e., time points or intervals) or high-level ontology concepts that vary in time. Although independent from TOQL, this work suggests a mechanism for representing time evolving concepts in ontologies based on the four-dimensional perdurantist mechanism. However, TOQL prevents users from being familiar with the representation of time in ontologies. To show proof of concept, an application has been developed that supports translation and execution of TOQL queries on temporal ontologies combined with a reasoning mechanism based on event calculus. A real world temporal ontology is also implemented on which several TOQL example queries are processed and discussed.

2021

This dissertation introduces a methodology to perform live time-traversal queries on RDF datasets and software based on this procedure. It offers a solution to manage provenance and change-tracking in the Semantic Web. Although they are crucial factors in ensuring verifiability and trust, there is no standard metadata representation model to achieve this result. Moreover, the leading knowledge bases -DBpedia, Wikidata, Yago, and the Dynamic Linked Data Observatorydo not use RDF to track changes and enable time-agnostic queries. The OpenCitations provenance model was adopted to fill this gap. It expresses provenance metadata via the Provenance Ontology and saves deltas in separate named graphs as SPARQL update strings. This approach enables to restore an entity to a specific snapshot in a straightforward way by applying the inverse operations, i.e., deletions instead of additions and vice versa. Additionally, only those portions that are strictly necessary to answer the user's question are recovered to avoid reconstructing all past versions of a dataset before running a time traversal query. This method was implemented in a Python library following the Test-Driven Development, and benchmarks were executed on a dataset created for the purpose. This procedure has proven effective for any materialization. Regarding structured queries, they are efficient if all subjects are known or deductible, while isolated triples require a significant amount of time and resources. Future research should focus on optimizing specific queries containing isolated triples to avoid reconstructing portions of the past that are not needed to fulfill the query. To date, the Time-Agnostic Library is the only software enabling all the time-related retrieval functionalities without pre-indexing data. It can materialize versions and deltas and run structured queries on them, either considering the entire dataset history or a specified time interval.

Proceeding of the 17th international conference on World Wide Web - WWW '08, 2008

Supporting fast access to large RDF stores has been one of key challenges for enabling use of the Semantic Web in real-life applications, more so in sensor-based systems where large amounts of historic data needs to be stored. We propose a semantics-based temporal view mechanism that enables faster access to time-varying data by caching into memory only the required subset of RDF triples. We describe our experience of implementing such a framework in the context of a wide area network monitoring system. Our preliminary results show that our solution significantly improves client access time and scales well for moderate data sets.

Information and Computation, 2017

DBpedia and other RFD-encoded Knowledge Bases (KB)s give users access to encyclopedic knowledge via SPARQL queries. As the world evolves, the KBs are updated, and the history of entities and their properties becomes of great interest. Thus, we need powerful tools and friendly interfaces to query histories and flashback to the past. Here, we propose (i) a point-based temporal extension of SPARQL, called SPARQL T , which enables simple and concise expression of temporal queries, and (ii) an extension of Wikipedia Infoboxes to support user-friendly by-example temporal queries implemented by mapping them into SPARQL T. Our main-memory RDF-TX system supports such queries efficiently using Multi-Version B+ trees, compressed indexes, and query optimization techniques, which achieve performance and scalability, as demonstrated by experiments on historical datasets including Cliopedia derived from Wikipedia dumps. We finally discuss how provenance information can be used to add valid-time features to these transaction-time KBs.

2009

In this paper, we present a multi-temporal RDF database model employing triple timestamping with temporal elements, which best preserves in the multi-temporal setting the scalability property enjoyed by triple storage technologies. The data model is equipped with manipulation operations which allow knowledge engineers to maintain a multi-temporal RDF database in order to manage temporal versions of an ontology.

2011

Page 1. TELEIOS FP7-257662 Deliverable D2.1 A data model and query language for an extension of RDF with time and space Manolis Koubarakis, Kostis Kyzirakos, Babis Nikolaou, Michael Sioutis, Stavros Vassos, and Consortium Members April 1, 2012 Status: Final Scheduled Delivery Date: 28 February, 2011 Page 2.

Big Data Mining and Analytics

Temporal ontologies allow to represent not only concepts, their properties, and their relationships, but also time-varying information through explicit versioning of definitions or through the four-dimensional perdurantist view. They are widely used to formally represent temporal data semantics in several applications belonging to different fields (e.g., Semantic Web, expert systems, knowledge bases, big data, and artificial intelligence). They facilitate temporal knowledge representation and discovery, with the support of temporal data querying and reasoning. However, there is no standard or consensual temporal ontology query language. In a previous work, we have proposed an approach named JOWL (temporal OWL 2 from temporal JSON, where OWL 2 stands for "OWL 2 Web Ontology Language" and JSON stands for "JavaScript Object Notation"). JOWL allows (1) to automatically build a temporal OWL 2 ontology of data, following the Closed World Assumption (CWA), from temporal JSON-based big data, and (2) to manage its incremental maintenance accommodating their evolution, in a temporal and multi-schema-version environment. In this paper, we propose a temporal ontology query language for JOWL , named SQWRL (temporal SQWRL), designed as a temporal extension of the ontology query language-Semantic Query-enhanced Web Rule Language (SQWRL). The new language has been inspired by the features of the consensual temporal query language TSQL2 (Temporal SQL2), well known in the temporal (relational) database community. The aim of the proposal is to enable and simplify the task of retrieving any desired ontology version or of specifying any (complex) temporal query on time-varying ontologies generated from time-varying big data. Some examples, in the Internet of Healthcare Things (IoHT) domain, are provided to motivate and illustrate our proposal.

IEEE Transactions on Systems, Man, and Cybernetics, Part B (Cybernetics), 2000

Through its interoperability and reasoning capabilities, the Semantic Web opens a realm of possibilities for developing intelligent systems on the Web. The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies, the cornerstone of the Semantic Web. However, up until now no standard way of expressing time and time-dependent information in OWL has been provided. In this paper, we present a temporal extension of the very expressive fragment SHIN (D) of the OWL-DL language resulting in the tOWL language. Through a layered approach we introduce three extensions: i) Concrete Domains, which allows the representation of restrictions using concrete domain binary predicates, ii) Temporal Representation, which introduces timepoints, relations between timepoints, intervals, and Allen's 13 interval relations into the language, and iii) TimeSlices/Fluents, which implements a perdurantist view on individuals and allows for the representation of complex temporal aspects, such as process state transitions. We illustrate the expressiveness of the newly introduced language by using an example from the financial domain.

2004

We present our experience on reusing time ontologies in Fund Finder, a semantic web application of the EU Esperonto project. On the one hand, we show a set of time ontologies implemented in different machine readable languages. Such ontologies are analyzed considering a series of features typical of time models (e.g., if they consider different granularities or different time zones). On the other hand, we present the specification of time modeling necessities for Fund Finder. Finally, we choose the ontology which fits best with the specifications.