τJOWL:A Systematic Approach to Build and Evolve a Temporal OWL 2 Ontology Based on Temporal JSON Big Data

Nowadays,ontologies,which are defined under the OWL 2 Web Ontology Language(OWL 2),are being used in several fields like artificial intelligence,knowledge engineering,and Semantic Web environments to access data,answer queries,or infer new knowledge.In particular,ontologies can be used to model the semantics of big data as an enabling factor for the deployment of intelligent analytics.Big data are being widely stored and exchanged in JavaScript Object Notation(JSON)format,in particular by Web applications.However,JSON data collections lack explicit semantics as they are in general schema-less,which does not allow to efficiently leverage the benefits of big data.Furthermore,several applications require bookkeeping of the entire history of big data changes,for which no support is provided by mainstream Big Data management systems,including Not only SQL(NoSQL)database systems.In this paper,we propose an approach,namedJOWL(temporal OWL 2 from temporal JSON),which allows users(i)to automatically build a temporal OWL 2 ontology of data,following the Closed World Assumption(CWA),from temporal JSON-based big data,and(ii)to manage its incremental maintenance accommodating the evolution of these data,in a temporal and multi-schema environment.

τSQWRL:A TSQL2-Like Query Language for Temporal Ontologies Generated from JSON Big Data

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 namedτJOWL(temporal OWL 2 from temporal JSON,where OWL 2 stands for"OWL 2 Web Ontology Language"and JSON stands for"JavaScript Object Notation").τJOWL 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 forτJOWL,namedτSQWRL(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.

Knowledge Representation and Reasoning for Complex Time Expression in Clinical Text

Temporal information is pervasive and crucial in medical records and other clinical text,as it formulates the development process of medical conditions and is vital for clinical decision making.However,providing a holistic knowledge representation and reasoning framework for various time expressions in the clinical text is challenging.In order to capture complex temporal semantics in clinical text,we propose a novel Clinical Time Ontology(CTO)as an extension from OWL framework.More specifically,we identified eight timerelated problems in clinical text and created 11 core temporal classes to conceptualize the fuzzy time,cyclic time,irregular time,negations and other complex aspects of clinical time.Then,we extended Allen’s and TEO’s temporal relations and defined the relation concept description between complex and simple time.Simultaneously,we provided a formulaic and graphical presentation of complex time and complex time relationships.We carried out empirical study on the expressiveness and usability of CTO using real-world healthcare datasets.Finally,experiment results demonstrate that CTO could faithfully represent and reason over 93%of the temporal expressions,and it can cover a wider range of time-related classes in clinical domain.