OWL Axioms Research Articles

SPARQL Query Generator (SQG)

This paper describes a program—SPARQL Query Generator (SQG)—which takes as input an OWL ontology, a set of object descriptions in terms of this ontology and an OWL class as the context, and generates relatively large numbers of queries about various types of descriptions of objects expressed in RDF/OWL. The intent is to use SQG in evaluating data representation and retrieval systems from the perspective of OWL semantics coverage. While there are many benchmarks for assessing the efficiency of data retrieval systems, none of the existing solutions for SPARQL query generation focus on the coverage of the OWL semantics. Some are not scalable since manual work is needed for the generation process; some do not consider (or totally ignore) the OWL semantics in the ontology/instance data or rely on large numbers of real queries/datasets that are not readily available in our domain of interest. Our experimental results show that SQG performs reasonably well with generating large numbers of queries and guarantees a good coverage of OWL axioms included in the generated queries.

An Ontology-Based Kinematics Problem Solver Using Qualitative and Quantitative Knowledge

One of the major tasks involved in the development of a knowledge-based problem solver is domain knowledge representation and reasoning. In this paper, we address this task for a knowledge-based kinematics word problem solver that solves problems from the domain of kinematics automatically, where solving each problem involves identifying the value of a specific unknown quantity referred to, within the problem. Knowledge about kinematics domain is captured at two levels: quantitative level and a more abstract qualitative level. We leverage OWL (Web Ontology Language) and RDF (Resource Description Framework) rules to represent both qualitative and quantitative knowledge of the domain in a single framework. We build an ontology, wherein we identify a fixed number of classes and properties that provide a vocabulary to formally represent a domain qualitatively and quantitatively. We then define the kinematics domain in terms of these classes and properties using RDF rules and OWL axioms. This is then used as a knowledge base (KB) to a kinematics problem solver. The input to this solver is represented as an RDF graph, called the problem scenario graph. Inference based on the OWL axioms and RDF rules in the KB adds knowledge, that is required to solve the problem, to the problem scenario graph. The knowledge enriched problem scenario graph is then used by an external reasoner to infer the value of the unknown quantity in the problem. We created a dataset of around 100 problems from the domain to provide a qualitative analysis of the solver by describing the various failure modes with examples.

Translating JSON Schema logics into OWL axioms for unified data validation on a digital manufacturing platform

Abstract JSON (JavaScript Object Notation) is a prevalent data format used in cloud-based platforms that support composable digital manufacturing workflows. The current work presents a method to translate the logics found in JSON Schema into OWL axioms, in order to facilitate ontology-based unified data validation with JSON data. The specific contributions of this paper include the demonstration of using a formal ontology for the logic translation and data validation, a technique for disambiguating implicit relations found in JSON Schema as explicit OWL properties, and mapping JSON Schema validation keywords to equivalent OWL expressions.

Ontology Model for Process Level Capabilities of Manufacturing Resources

The promise of distributed cloud manufacturing (CM) is to manufacture products using shared resources, both asset-full (machines, tools, vehicles) and asset-light (design, analysis, inspection, management, maintenance), which can be provisioned flexibly and rapidly with minimal management and service provider interaction. One of the key enabler of CM are virtual enterprises (VE), which offer manufacturing resources as virtual services (SaaS, HaaS, PaaS) in a cloud based marketplace. Currently, virtualization and provisioning of diverse array of manufacturing resources face challenges from the heterogeneity in representation and communication protocols, as well as lack of integration with legacy practices in the organizations. Aiming to increase interoperability, a number of formal ontologies were developed by researchers in the past, to leverage on semantic data integration and validation. In spite of their success in providing axiomatic description and common taxonomy to classify manufacturing resources from different domains, models of representing the capabilities of the resources (i.e. expected quality of services they offer) were often overlooked. In this research, we present an ontology model to represent capabilities of manufacturing machine-tools at the process level, often called process boundaries (measured by process capability index) in industries. The definition of the capability is derived based on the foundational ontology called ‘Basic Formal Ontology’ (BFO). The primary contribution of this extension is a set of OWL axioms which can be used to assert facts about modal future (possibilia) – ultimately enabling us to associate process specific performance metrics to the semantic models of virtual machine-tools.

A practical approach to modelling and validating integrity constraints in the Semantic Web

Efforts have been made in the Semantic Web to combine rules with ontologies. One result of these efforts is the development of the Semantic Web Rule Language (SWRL) which is designed to integrate closely with the Web Ontology Language (OWL). Both SWRL and OWL adhere to the open-world semantics of first-order logic, and thus are not suitable for modelling integrity constraints in applications where complete knowledge about some parts of the domain can be assumed. In this paper, we investigate this problem and present a practical approach to modelling and validating constraints in the Semantic Web. Building on existing work, we show that by employing a constraint semantics for both OWL and SWRL, we can model common constraints as OWL axioms or SWRL rules. We also show that by using a query reduction technique, we can validate constraints using existing OWL/SWRL reasoners. Finally, we demonstrate the usefulness of our approach via a real-world case study.

Possibilistic testing of OWL axioms against RDF data

We develop the theory of a possibilistic framework for OWL 2 axiom testing against RDF datasets, as an alternative to statistics-based heuristics. The intuition behind it is to evaluate the credibility of OWL 2 axioms based on the evidence available in the form of a set of facts contained in a chosen RDF dataset. To achieve it, we first define the notions of development, content, support, confirmation and counterexample of an axiom. Then we use these notions to define the possibility and necessity of an axiom and its acceptance/rejection index combining both of them. Finally, we report a practical application of the proposed framework to test SubClassOf axioms against the DBpedia RDF dataset.

Temporal representation and reasoning in OWL 2

The representation of temporal information has been in the center of intensive research activities over the years in the areas of knowledge representation, databases and more recently, the Semantic Web. The proposed approach extends the existing framework of representing temporal information in ontologies by allowing for representation of concepts evolving in time (referred to as “dynamic” information) and of their properties in terms of qualitative descriptions in addition to quantitative ones (i.e., dates, time instants and intervals). For this purpose, we advocate the use of natural language expressions, such as “before” or “after”, for temporal entities whose exact durations or starting and ending points in time are unknown. Reasoning over all types of temporal information (such as the above) is also an important research problem. The current work addresses all these issues as follows: The representation of dynamic concepts is achieved using the “4D-fluents” or, alternatively, the “N-ary relations” mechanism. Both mechanisms are thoroughly explored and are expanded for representing qualitative and quantitative temporal information in OWL. In turn, temporal information is expressed using either intervals or time instants. Qualitative temporal information representation in particular, is realized using sets of SWRL rules and OWL axioms leading to a sound, complete and tractable reasoning procedure based on path consistency applied on the existing relation sets. Building upon existing Semantic Web standards (OWL), tools and member submissions (SWRL), as well as integrating temporal reasoning support into the proposed representation, are important design features of our approach.

Research on a knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics

Fault diagnosis is a critical activity in PHM (Prognostics and Health Management) of machine tools due to its great significance in such efforts as prolonging lifespan, improving production efficiency, and reducing production costs. An efficient knowledge model is necessary to build an intelligent fault diagnosis system. There have been several achievements in knowledge representation and modelling. However, due to their various purposes and depths, the established knowledge models are less compatible, reusable or transplantable, which restricts knowledge sharing and integration. A knowledge modelling methodology for fault diagnosis of machine tools based on formal semantics (KMM-MTFD) is proposed in this paper to build an open, shared, and scalable ontology-based knowledge model of fault diagnosis of various machine tools (OKM-MTFD). First, the proposed predicate-logic-based analysis method of fault elements is adopted to study the fault diagnosis domain and extract the common domain knowledge, which enables the establishment of the core ontology of OKM-MTFD to assure formal semantics. Next, using the proposed two-stage classification method of fault elements and external ontology reference methods, the core ontology can be extended into OKM-MTFD for a type or a specific machine tool. The knowledge reasoning and querying methods based on OWL axioms, SWRL rules, special fault attributes and SPARQL are provided to utilize the knowledge base efficiently. Finally, an ontology-based knowledge model and knowledge base of a hobbing machine tool is presented to exemplify the validity of the proposed KMM-MTFD.

Expressing Biomedical Ontologies in Natural Language for Expert Evaluation.

We report on a study of our custom Hootation software for the purposes of assessing its ability to produce clear and accurate natural language phrases from axioms embedded in three biomedical ontologies. Using multiple domain experts and three discrete rating scales, we evaluated the tool on clarity of the natural language produced, fidelity of the natural language produced from the ontology to the axiom, and the fidelity of the domain knowledge represented by the axioms. Results show that Hootation provided relatively clear natural language equivalents for a select set of OWL axioms, although the clarity of statements hinges on the accuracy and representation of axioms in the ontology.

Ontology-Based Representation and Reasoning in Building Construction Cost Estimation in China

Cost estimation is one of the most critical tasks for building construction project management. The existing building construction cost estimation methods of many countries, including China, require information from several sources, including material, labor, and equipment, and tend to be manual, time-consuming, and error-prone. To solve these problems, a building construction cost estimation model based on ontology representation and reasoning is established, which includes three major components, i.e., concept model ontology, work item ontology, and construction condition ontology. Using this model, the cost estimation information is modeled into OWL axioms and SWRL rules that leverage the semantically rich ontology representation to reason about cost estimation. Based on OWL axioms and SWRL rules, the cost estimation information can be translated into a set of concept models, work items, and construction conditions associated with the specific construction conditions. The proposed method is demonstrated in Protégé 3.4.8 through case studies based on the Measurement Specifications of Building Construction and Decoration Engineering taken from GB 50500-2013 (the Chinese national mandatory specifications). Finally, this research discusses the limitations of the proposed method and future research directions. The proposed method can help a building construction cost estimator extract information more easily and quickly.

Owlcpp: a C++ library for working with OWL ontologies.

BackgroundThe increasing use of ontologies highlights the need for a library for working with ontologies that is efficient, accessible from various programming languages, and compatible with common computational platforms.ResultsWe developed owlcpp, a library for storing and searching RDF triples, parsing RDF/XML documents, converting triples into OWL axioms, and reasoning. The library is written in ISO-compliant C++ to facilitate efficiency, portability, and accessibility from other programming languages. Internally, owlcpp uses the Raptor RDF Syntax library for parsing RDF/XML and the FaCT++ library for reasoning. The current version of owlcpp is supported under Linux, OSX, and Windows platforms and provides an API for Python.ConclusionsThe results of our evaluation show that, compared to other commonly used libraries, owlcpp is significantly more efficient in terms of memory usage and searching RDF triple stores. owlcpp performs strict parsing and detects errors ignored by other libraries, thus reducing the possibility of incorrect semantic interpretation of ontologies. owlcpp is available at http://owl-cpp.sf.net/ under the Boost Software License, Version 1.0.Electronic supplementary materialThe online version of this article (doi:10.1186/s13326-015-0035-z) contains supplementary material, which is available to authorized users.

An ontology for Autism Spectrum Disorder (ASD) to infer ASD phenotypes from Autism Diagnostic Interview-Revised data

ObjectiveOur goal is to create an ontology that will allow data integration and reasoning with subject data to classify subjects, and based on this classification, to infer new knowledge on Autism Spectrum Disorder (ASD) and related neurodevelopmental disorders (NDD). We take a first step toward this goal by extending an existing autism ontology to allow automatic inference of ASD phenotypes and Diagnostic & Statistical Manual of Mental Disorders (DSM) criteria based on subjects’ Autism Diagnostic Interview-Revised (ADI-R) assessment data. Materials and methodsKnowledge regarding diagnostic instruments, ASD phenotypes and risk factors was added to augment an existing autism ontology via Ontology Web Language class definitions and semantic web rules. We developed a custom Protégé plugin for enumerating combinatorial OWL axioms to support the many-to-many relations of ADI-R items to diagnostic categories in the DSM. We utilized a reasoner to infer whether 2642 subjects, whose data was obtained from the Simons Foundation Autism Research Initiative, meet DSM-IV-TR (DSM-IV) and DSM-5 diagnostic criteria based on their ADI-R data. ResultsWe extended the ontology by adding 443 classes and 632 rules that represent phenotypes, along with their synonyms, environmental risk factors, and frequency of comorbidities. Applying the rules on the data set showed that the method produced accurate results: the true positive and true negative rates for inferring autistic disorder diagnosis according to DSM-IV criteria were 1 and 0.065, respectively; the true positive rate for inferring ASD based on DSM-5 criteria was 0.94. DiscussionThe ontology allows automatic inference of subjects’ disease phenotypes and diagnosis with high accuracy. ConclusionThe ontology may benefit future studies by serving as a knowledge base for ASD. In addition, by adding knowledge of related NDDs, commonalities and differences in manifestations and risk factors could be automatically inferred, contributing to the understanding of ASD pathophysiology.

An Ontological and Semantic Approach for the Construction Risk Inferring and Application

Construction risk and safety management is a knowledge-intensive task. In addition to the construction risk knowledge itself, construction process knowledge is also important to the construction safety, since the different construction objects and processes selection often imply different potential risks may occur. However, most of construction risk management systems only focused on the construction risk knowledge itself. The process management and risk management usually are handled separately. In this paper, we explore to combine the construction process knowledge reuse methodology with the ontological modeling and semantic inferring approach to facilitate considering the risk issue during construction process reuse to directly improve the construction risk and safety management level. A meta ontology model is proposed to integrate risk knowledge domain with the risk monitor object domain. The meta model can be instantiated with the domain specific terms and relations and finally results into the specific domain model for specific application. The domain risk model for the deep foundation pit excavation using support systemcontiguous bored pile retaining wall system is shown as an illustrative example, in which the construction risk path knowledge is extracted via Fault Tree Analysis (FTA). In order to represent the construction risk knowledge in a computer-interpretable and semantically inferable way, the semantic web technique is used to represent the knowledge into OWL axioms and SWRL rules. Finally, based on the ontological model, the semantic inferring and application mechanism of construction risk knowledge are discussed and illustrated in the Protg platform. The proposed approach enables the construction risk knowledge to be presented in a computer-interpretable and semantically inferable way. The risk-oriented ontology model can be used as the basic structure of knowledge-based risk management system.

Ontology-based semantic modeling of regulation constraint for automated construction quality compliance checking

Abstract Regulations play an important role in assuring the construction quality. However, due to the large amount of regulation needs considered, the construction quality compliance checking against regulations can be cumbersome/time-consuming and error prone. In order to give more computerized support to the construction quality compliance checking against regulations, an ontology-based semantic modeling approach of regulation constraints is explored. A meta model for construction quality inspection and evaluation i.e. CQIEOntology is proposed in this paper. Based on CQIEontology, the regulation constraints are modeled into OWL axioms and SWRL rules. By these OWL axioms and SWRL rules, the regulation provisions imposed on construction quality inspection can be translated into a set of inspection tasks, and get associated with the specific construction tasks. Once the construction starts, the applicable inspection tasks, including a series of quality checking and evaluation, will be reminded and recommended. Obviously, the proposed approach facilitates taking the construction quality compliance checking as a paralleling activity to the construction rather than an afterthought, and helps the inspectors in quality inspection. Finally, the approach is demonstrated in Protege 3.4.6 through case studies based on regulation examples taken from “Code for Acceptance of Construction Quality of Building Foundation (GB50202-2002) and the validation and discussion are given for it.”

Integrity Constraints in OWL

In many data-centric semantic web applications, it is desirable to use OWL to encode the Integrity Constraints (IC) that must be satisfied by instance data. However, challenges arise due to the Open World Assumption (OWA) and the lack of a Unique Name Assumption (UNA) in OWL’s standard semantics. In particular, conditions that trigger constraint violations in systems using the ClosedWorld Assumption (CWA), will generate new inferences in standard OWL-based reasoning applications. In this paper, we present an alternative IC semantics for OWL that allows applications to work with the CWA and the weak UNA. Ontology modelers can choose which OWL axioms to be interpreted with our IC semantics. Thus application developers are able to combine open world reasoning with closed world constraint validation in a flexible way. We also show that IC validation can be reduced to query answering under certain conditions. Finally, we describe our prototype implementation based on the OWL reasoner Pellet.

A semantic web approach to biological pathway data reasoning and integration

This paper describes the use of semantic web technology and Description Logic (DL) for facilitating the integration of molecular pathway data, which is illustrated by an Web Ontology Language (OWL)-based transformation of a more complex pathway structure (Reactome) into a simpler one (HPRD). The process starts by adding OWL axioms to BioPAX, a pathway interchange standard. The axioms are designed for mapping BioPAX-formatted Reactome interactions to “molecular binding event” interactions, which can be easily aligned with the HPRD data. Using an automated OWL reasoner, we find overlapping and non-overlapping molecular interactions between the two pathway datasets. The paper demonstrates the potential of semantic web and its enabling technologies in biological pathway data reasoning and integration.

A Semantic Web Approach to Biological Pathway Data Reasoning and Integration

This paper describes the use of semantic web technology and Description Logic (DL) for facilitating the integration of molecular pathway data, which is illustrated by an OWL-based transformation of a more complex pathway structure (Reactome) into a simpler one (HPRD). The process starts by adding OWL axioms to BioPAX, a pathway interchange standard. The axioms are designed for mapping BioPAX-formatted Reactome interactions to molecular binding event interactions, which can be easily aligned with the HPRD data. Using an automated OWL reasoner, we find overlapping and non-overlapping molecular interactions between the two pathway datasets. The paper demonstrates the potential of semantic web and its enabling technologies in biological pathway data reasoning and integration.