Representing simmodel in the web ontology language

Many building energy performance (BEP) simulation tools, such as EnergyPlus and DOE-2, use custom schema definitions (IDD and BDL respectively) as opposed to standardised schema definitions (defined in XSD, EXPRESS, and so forth). A Simulation Domain Model (SimModel) was therefore proposed earlier, representative for a new interoperable XML-based data model for the building simulation domain. Its ontology aims at moving away from tool-specific, non-standard nomenclature by implementing an industry-validated terminology aligned with the Industry Foundation Classes (IFC). In this paper, we document our ongoing efforts to make building simulation data more interoperable with other building data. In order to be able to better integrate SimModel information with other building information, we have aimed at representing this information in the Resource Description Framework (RDF). A conversion service has been built that is able to parse the SimModel ontology in the form of XSD schemas and output a SimModel ontology in OWL. In this article, we document this effort and give an indication of what the resulting SimModel ontology in OWL can be used for

A Temporal Web Ontology Language

The Web Ontology Language (OWL) is the most expressive standard language for modeling ontologies on the Semantic Web. 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 3 extensions: i) Concrete Domains, that allows the representation of restrictions using concrete domain binary predicates, ii) Temporal Representation, that introduces timepoints, relations between timepoints, intervals, and Allen’s 13 interval relations into the language, and iii) TimeSlices/Fluents, that 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 providing a TBox representation of Leveraged Buy Out (LBO) processes in financial applications and an ABox representation of one specific LBO

WebProt\'eg\'e: A Cloud-Based Ontology Editor

We present WebProt\'eg\'e, a tool to develop ontologies represented in the Web Ontology Language (OWL). WebProt\'eg\'e is a cloud-based application that allows users to collaboratively edit OWL ontologies, and it is available for use at https://webprotege.stanford.edu. WebProt\'ege\'e currently hosts more than 68,000 OWL ontology projects and has over 50,000 user accounts. In this paper, we detail the main new features of the latest version of WebProt\'eg\'e

Web Ontology Language (OWL)

Web Ontology Language (OWL) is a core world wide web consortium [W3C] standard Knowledge representation language for the Semantic Web

Representing chord sequences in OWL

Chord symbols and progressions are a common way to describe musical harmony. In this paper we present SEQ, a pattern representation using the Web Ontology Language OWL DL and its application to modelling chord sequences. SEQ provides a logical representation of order information, which is not available directly in OWL DL, together with an intuitive notation. It therefore allows the use of OWL reasoners for tasks such as classification of sequences by patterns and determining subsumption relationships between the patterns. The SEQ representation is used to express distinctive pattern obtained using data mining of multiple viewpoints of chord sequences

MIREOT: the Minimum Information to Reference an External Ontology Term

While the Web Ontology Language (OWL) provides a mechanism to import ontologies, this mechanism is not always suitable. First, given the current state of editing tools and the issues they have working with large ontologies, direct OWL imports have sometimes proven impractical for day-to-day development. Second, ontologies chosen for integration may be under active development and not aligned with the chosen design principles. Importing heterogeneous ontologies in their entirety may lead to inconsistencies or unintended inferences. In this paper we propose a set of guidelines for importing required terms from an external resource into a target ontology. We describe the guidelines, their implementation, present some examples of application, and outline future work and extensions