Ontology Language Research Articles

An efficient algorithm for reasoning over OWL EL ontologies with nominal schemas

Abstract Nominal schemas have been proposed as an extension to Description Logics (DL), the knowledge representation paradigm underlying the Web Ontology Language (OWL). They provide for a very tight integration of DL and rules. Nominal schemas can be understood as syntactic sugar on top of OWL. However, this naive perspective leads to inefficient reasoning procedures. In order to develop an efficient reasoning procedure for the language ${\mathcal {E}\mathcal {L}\mathcal {V}^{++}}$, which results from extending the OWL profile language OWL EL with nominal schemas, we propose a transformation from ${\mathcal {E}\mathcal {L}\mathcal {V}^{++}}$ ontologies into Datalog-like rule programs that can be used for satisfiability checking and assertion retrieval. The use of this transformation enables the use of powerful Datalog engines to solve reasoning tasks over ${\mathcal {E}\mathcal {L}\mathcal {V}^{++}}$ ontologies. We implement and then evaluate our approach on several real-world, data-intensive ontologies, and find that it can outperform state-of-the-art reasoners such as Konclude and ELK. As a lesser side result we also provide a self-contained description of a rule-based algorithm for ${\mathcal {E}\mathcal {L}^{++}}$, which does not require a normal form transformation.

A Semantic Approach for Building System Operations: Knowledge Representation and Reasoning

Artificial intelligence is set to transform the next generation of intelligent buildings through the application of information and semantic data models and machine learning algorithms. Semantic data models enable the understanding of real-world data for building automation, integration and control applications. This article explored the use of semantic models, a subfield of artificial intelligence, for knowledge representation and reasoning (KRR) across a wide variety of applications in building control, automation and analytics. These KRR-enabled applications include context-aware control of mechanical systems, building energy auditing and commissioning, indoor air monitoring, fault detection and diagnostics (FDD) of mechanical equipment and systems and building-to-grid integration. To this end, this work employed the Apache Jena Application Programming Interface (API) to develop KRR and integrate it with some domain-specific ontologies expressed in the Resource Description Framework (RDF) and Web Ontology Language (OWL). The ontology-driven rules were represented using Jena rule formalisms to enable the inference of implicit information from data asserted in the ontologies. Moreover, SPARQL (SPARQL Query Language for RDF) was used to query the knowledge graph and obtain useful information for a variety of building applications. This approach enhances building analytics through multi-domain knowledge integration; spatial and temporal reasoning for monitoring building operations, and control systems and devices; and the performance of compliance checking. We show that existing studies have not leveraged state-of-the-art ontologies to infer information from different domains. While the proposed semantic infrastructure and methods in this study demonstrated benefits for different building applications applicable to mechanical systems, the approach also has great potential for lighting, shading and security applications. Multi-domain knowledge integration that includes spatial and temporal reasoning allows the optimization of the performance of building equipment and systems.

A Description Logic Based Ontology for Knowledge Representation in Process Planning for Laser Powder Bed Fusion

Laser powder bed fusion (LPBF) provides a rapid and cost-effective solution for fabricating metallic parts with near full density and high precision, strength, and stiffness directly from metallic powders. In LPBF, process variables are widely recognised as fundamental factors that have important effect on the quality of the built parts. However, activity of designing process variables for LPBF, i.e., process planning for LPBF, still heavily depends on knowledge from domain experts. This necessitates a knowledge base that enables the capture, representation, inference, and reuse of existing knowledge. In this paper, a description logic (DL) based ontology for knowledge representation in process planning for LPBF is presented. Firstly, a set of top-level DL entities and specific DL entities and semantic web rule language (SWRL) rules for part orientation, support generation, model slicing, and path planning are created to construct the ontology. The application of the ontology is then illustrated via process planning on an LPBF part. Finally, the benefits of the ontology are demonstrated through a few examples. The demonstration results show that the ontology has rigorous computer-interpretable semantics, which provides a semantic enrichment model for LPBF process planning knowledge and enables automatic consistency checking of the ontology, knowledge reasoning on the ontology, and semantic query from the ontology. This would lay solid foundation for development of a process planning tool with autonomous decision-making capability.

BIM-Based Dynamic Construction Safety Rule Checking Using Ontology and Natural Language Processing

Real-time identification and prevention of safety risks in dynamic construction activities are demanded by construction safety managers to cope with the growing complexity of the construction site. Most of the studies on BIM-based construction safety inspection and prevention use data from the planning and design stage. Meanwhile, safety managers still need to spend a lot of time gathering reports about construction safety risks in certain periods or areas from inferred results in BIM. Therefore, this paper proposed an automatic safety risk identification and prevention mechanism for the construction process by integrating a safety rule library based on ontology technology and Natural Language Processing. An automatic inspection mechanism integrating BIM and safety rules is constructed, and a presentation mechanism of intelligent detection results based on Natural Language Processing is designed. The construction process safety rule checking system was developed, and the effectiveness of the system was verified by a case study. The outcome of this paper contributes to the development and application of ontology in construction safety research, and the NLP-based safety rule checking result presentation will benefit safety inspectors and construction managers in practice.

Efficient Composition Algorithm Strategies on Semantic Web Services Using Nature Inspired Approach

Web services are used all around the world for commercial purposes. It works majorly by Universal Resource Locators, which are stored in the repository UDDI. The different web service has to be composed for creating new business services. It is time dependent for composing more web services, which is massive. A semantically described WSDL description solves automatic composition of web services. Also with ontological models and languages, such as RDF, OWL, and SWRL, the better automatic discovery, selecting, integration, and composition of services have been done. Service requirements, such as connectivity, quality-of-service properties, correctness, and scalability, need to be satisfied. For achieving these, many evolutionary algorithms will be used for improving quality of service parameters. This paper describes many optimizations algorithms which will be used efficiently in optimizing composition techniques in semantic web.

The Malware Detection Approach in the Design of Mobile Applications

Background: security has become a major concern for smartphone users in line with the increasing use of mobile applications, which can be downloaded from unofficial sources. These applications make users vulnerable to penetration and viruses. Malicious software (malware) is unwanted software that is frequently used by cybercriminals to launch cyber-attacks. Therefore, the motive of the research was to detect malware early before infection by discovering it at the application-design level and not at the code level, where the virus will have already damaged the system. Methods: in this article, we proposed a malware detection method at the design level based on reverse engineering, the unified modeling language (UML) environment, and the web ontology language (OWL). The proposed method detected “Data_Send_Trojan” malware by designing a UML model that simulated the structure of the malware. Then, by generating the ontology of the model, and using RDF query language (SPARQL) to create certain queries, the malware was correctly detected. In addition, we proposed a new classification of malware that was suitable for design detection. Results: the proposed method detected Trojan malware that appeared 552 times in a sample of 600 infected android application packages (APK). The experimental results showed a good performance in detecting malware at the design level with precision and recall of 92% and 91%, respectively. As the dataset increased, the accuracy of detection increased significantly, which made this methodology promising.

An Effective Resource Matching Scheme Based on a Novel Unified Descriptive Model for Modern Manufacturing Industry Systems

In order to effectively solve the problem of heterogeneous design/manufacturing/service resources and isolation in the whole lifecycle and realize unified description of design/manufacturing/service resources and resource sharing across subjects and stages, this paper proposes a hierarchical and modularized ontology-based resource-unified descriptive model, according to the characteristics of design/manufacturing/service resources. We analyze all kinds of properties of the resources, design a specific descriptive model of ontology, function, and service, ensure the consistency and independence of resource descriptions, and use the OWL (Web Ontology Language) ontology descriptive language and Protégé tools to verify. Then, based on the unified descriptive model, a resource matching method based on multi-level tags is proposed, which matches the task request with the resources in the resource library, selects the resources that meet the task request, and guarantees the resource sharing across subjects and stages. The resource matching work first performs task description and decomposition, and uses information entropy and rough set theory to sort the importance of subtasks, then uses the semantic similarity algorithm to complete multi-level tags’ matching. Finally, two examples are used to prove the feasibility and effectiveness of the experiment.

The Interactive Method for Language Science and Some Salient Results

The use of information technology in linguistic research gave rise in the 1950s to what is known as Natural Language Processing, but that framework was created without paying due attention to the need for logical reconstruction of linguistic concepts which were borrowed directly from barely (or even not at all) formalised structural linguistics. The Computer-aided Acquisition of Semantic Knowledge project (CASK) based on the Knowledge Discovery in Databases technology (KDD) enabled us to interact with computers while gathering and improving our knowledge about languages.
 Thus, with the help of data mining tools, as a result of revisiting two sorts of generally admitted linguistic theories (the Predicate Argument Structure theory and Information Structure theory), we succeeded in improving these local linguistic approaches by proposing to unify the Associative Semantics (AS) theory (in which we introduced the concept of ortho-information) with the Meta-Informative Centering (MIC) theory (in which we described the meta-informative layer of natural languages). The resulting Distributed Grammar (DG) program (sketched out in this paper) treats, in addition to the above types of information, the third one, para-information (concerning identity and alikeness with respect to context and language ontology) which – despite many studies – had no uniform theoretical background in general linguistics. This DG program aims to lay the foundations for creating the theoretical background of Conceptual Linguistics.

An adaptive deep learning model towards fault diagnosis of hydraulic piston pump using pressure signal

Hydraulic pump is a critical component of a hydraulic transmission system. Its normal and stable operation will directly affect the reliability of the whole system. Owing to the complexity and concealment of fault information, it is crucial to explore an effective method for fault diagnosis of hydraulic piston pump. The obvious change of vibration signal can be observed when different fault types occur, and the implied characteristics of pressure signals are also varying. In this research, an adaptive convolutional neural network (CNN) is constructed based on the ontology representation of pump. First, time–frequency characteristics of pressure signals are obtained via continuous wavelet transform. Second, a deep CNN is established by setting initial hyperparameters. Third, Bayesian optimization is employed to achieve automatic learning of main important hyperparameters to construct an adaptive CNN. The proposed method has better performance for fault diagnosis of hydraulic piston pump compared with other modern intelligent methods.

On the ontology of language: A critique of trait theory

In this article, we present a comparative analysis of the ontological assumptions about language at play within structural linguistics and trait theory. The ontological discussions demonstrate a fundamental incongruence between trait theory’s aim of description and its assumptions about language. This discussion guides a broader analysis of the structuring effect of the ontology of language within the study of subjectivity. We also develop the proposition that an integrative reading of structural linguistics and the phenomenology of speaking can be a constructive way to think coherently about language as system and language as subjectivity by insisting on speech as corporeal intention and reference and, thereby, lived.

Novel approach for semantic similarity cross ontology

Measuring <span>semantic similarity between terms is a crucial step in information retrieval and integration since it necessitates semantic content matching. Even though several models have been proposed to measure semantic similarity, these models are not able to effectively quantify the weight of relevant items that affect the semantic similarity judgment process. In this study, we present a new method for measuring semantic similarity between cross-ontologies, that consists of hybridizing node-based approaches such as WuP and Reda with the weight of similarity computed using WordNet. The proposed approach has been experimented to show its efficiency with two ontologies, configuration management tool (CMT) and ConfOf, from the conference domaine in the web ontology language (OWL) ontologies benchmark OAEI 2015 and evaluated using two metrics: density and cohesion.</span>

Henri Lefebvre's conception of nature-society in the revolutionary project of autogestion

Henri Lefebvre's intricate material-dialectical approach to the nature-society problematic, taken together with his advocacy of a praxis oriented to total transformation from the ground up through autogestion, offers a unified, critical, and dialectical approach to political ecology. Unfortunately, his work in these areas has too often been interpreted as divided and fragmentary, splitting his radical analysis of the production of space-time from his critical praxis related to autogestion. We offer a corrective to this by elaborating briefly on his use of Marx's material-dialectical approach, outlining how Lefebvre brings this method to bear on the nature-society problematic, and how his theorization of autogestion points to a radical praxis aimed at overcoming the social-ecological contradictions of capital. His engagement with Marx's theory of metabolic rift, and his advocacy of a radical project of autogestion as part of the critique of everyday life, serve to place the underlying issue of alienation in spatial terms, offering geography a transformative perspective that avoids positing closed systems and attempting to exhaust the various meanings assigned to nature. In this, Lefebvre demonstrates how the nature-society problematic overflows issues of ontological framing and language, calling for a unity of radical theory and practice to overcome the separations.

The Xenopus phenotype ontology: bridging model organism phenotype data to human health and development

BackgroundOntologies of precisely defined, controlled vocabularies are essential to curate the results of biological experiments such that the data are machine searchable, can be computationally analyzed, and are interoperable across the biomedical research continuum. There is also an increasing need for methods to interrelate phenotypic data easily and accurately from experiments in animal models with human development and disease.ResultsHere we present the Xenopus phenotype ontology (XPO) to annotate phenotypic data from experiments in Xenopus, one of the major vertebrate model organisms used to study gene function in development and disease. The XPO implements design patterns from the Unified Phenotype Ontology (uPheno), and the principles outlined by the Open Biological and Biomedical Ontologies (OBO Foundry) to maximize interoperability with other species and facilitate ongoing ontology management. Constructed in Web Ontology Language (OWL) the XPO combines the existing uPheno library of ontology design patterns with additional terms from the Xenopus Anatomy Ontology (XAO), the Phenotype and Trait Ontology (PATO) and the Gene Ontology (GO). The integration of these different ontologies into the XPO enables rich phenotypic curation, whilst the uPheno bridging axioms allows phenotypic data from Xenopus experiments to be related to phenotype data from other model organisms and human disease. Moreover, the simple post-composed uPheno design patterns facilitate ongoing XPO development as the generation of new terms and classes of terms can be substantially automated.ConclusionsThe XPO serves as an example of current best practices to help overcome many of the inherent challenges in harmonizing phenotype data between different species. The XPO currently consists of approximately 22,000 terms and is being used to curate phenotypes by Xenbase, the Xenopus Model Organism Knowledgebase, forming a standardized corpus of genotype–phenotype data that can be directly related to other uPheno compliant resources.

ViLO — A Core Ontology of Vietnamese Legal Documents

Legal ontologies play a key role in various legal applications and have been broadly used by many stakeholders. Innovative systems and ontologies in the law hold potential to conduct legal research. With the needs for legal information management in smart applications, especially for Vietnamese law, it is vitally important to construct core legal ontologies for knowledge representation. This study proposes a core ontology for Vietnamese legal documents which covers general legal domain called as ViLO. The ViLO ontology mainly consists of related institutions of Vietnamese political system, types and structures of legal documents. The method of the NeOn-based collaborations among domain experts and ontology engineers was conducted to build up the ViLO ontology. Through FOCA-based validation results, the proposed method was shown to be effective and efficient. The resulting ontology was demonstrated to be reliable and enriched. The ViLO ontology is supposed to be a basis for further constructions of domain ontologies and artificial intelligence applications in Vietnamese law.

A multiplatform reasoning engine for the Semantic Web of Everything

The Internet of Everything and Semantic Web can be joined by giving more intelligence to pervasive systems. To that end, reasoning capabilities should be enabled even for very resource-constrained embedded devices. This paper presents Tiny-ME (the Tiny Matchmaking Engine), a matchmaking and reasoning engine for the Web Ontology Language (OWL), designed and implemented with a compact and portable C core. Main features are high resource efficiency and multiplatform support, spanning containerized microservices, desktops, mobile devices, and embedded boards. The OWLlink interface has been extended to enable non-standard reasoning services for matchmaking in Web, Cloud, and Edge computing. A prototype evaluation is proposed, including a case study on the Pixhawk Unmanned Aerial Vehicle (UAV) autopilot and performance highlights.

Identifying Adversaries’ Signatures Using Knowledge Representations of Cyberattack Techniques on Cloud Infrastructure

Advanced Persistent Threats (APTs) have increased in parallel to growing cloud infrastructure and cloud Software-as-a-Service (SaaS) needs, exposing new vulnerabilities within the cloud environment. Moreover, APT groups are becoming more sophisticated and organised which needs to be addressed by the research community to enable faster response and more importantly, prevent threats within the domain. The MITRE ATT&CK Cloud framework offers one of the leading structured inventories within this context. Our research is to expose patterns and signatures of a select group of APT’s on the MITRE Cloud Framework by using Formal Concept Analysis (FCA) to construct a “lattice graph” and an ontology. The goal is to develop a better conceptualisation of the MITRE ATT&CK Cloud Matrix framework for cyber security experts to be able to proactively act upon adversary techniques. The MITRE ATT&CK framework was retrieved, cleaned, and pre-processed to construct the lattice and ontology using data cleaning methods, FCA tools such as Concept Explorer, and the Web Ontology Language (OWL), with additional symbolic reasoning and inference generation. This resulted in knowledge representations/graphs, which are highly efficient representations of this knowledge field. The underlying linkages between techniques and targets specific to those APTs are further exposed and enriched and presented visually and integrated into the ontology. The ontology gives formalisation to associations and implications between techniques, tactics, and APTs – enabling cyber security practitioners to forecast potential targets and techniques based on their scenario, but also to attribute certain technique patterns and signatures to individual APTs. Cyber security practitioners can query from this knowledge graph and formulate strategic proactive measures. From these findings, the applications and constraints of the APTs’ cyber-attack techniques and their associated patterns were determined. The findings provide a guideline for future additional research in the field of AI knowledge representation in cybersecurity, as well as highlighting certain limitations in this field of research.

193 Arachnoid Cysts as Genetically-encoded Harbingers of Neurodevelopmental Pathology

INTRODUCTION: Arachnoid cysts (ACs) are the most common congenital intracranial lesions. While familial cases implicate genetic factors, their ultimate cause remains unknown. Herein, we describe a multi-omics approach to disease state characterization including traditional whole exome sequencing, integrative genomics with analytics of large bulk-RNA and single cell RNA sequencing (scRNAseq) data, and a novel phenomics technique using natural language processing and Human Phenotypic Ontological (HPO) language clustering. METHODS: Whole exome sequencing was performed on 1560 individuals (520 parent-offspring "trios"). Genes with likely pathogenic mutations were defined as those with >1 de novo variant (DNV) reaching threshold for exome-wide significance (p<2.5 x 10-6) and having high pLI score (>0.9). This high-confidence pathogenic gene set and a secondary probable pathogenic gene set (>1 DNV, regardless of exome-wide significance or pLI score) were analyzed in bulk-RNA and scRNAseq datasets to assess spatiotemporal and cell-type relationships of gene expression. Phenomic analysis was conducted by subjecting patient medical records to a natural-language-processing (NLP) algorithm. Phenotypes were catalogued with the Human Phenotype Ontology (HPO) language tree and mapped with uniform manifold approximation and projection (UMAP) clustering. RESULTS: 7 genes reached our pathogenic threshold, ADNP, ARIDB1, KDM5C, PURA, FOXP1, MAP2K1, and SCN2A. Strikingly, all but SCN2A play critical roles in epigenetic regulation of gene expression in the developing brain. Bulk-RNA datasets reveal that these genes converge in networks essential for chromatin remodeling and mRNA processing. scRNAseq datasets revealed an association with excitatory and inter-neurons known to play a role in developmental delay. HPO clustering revealed four major phenotypic clusters defined by neurodevelopmental delay (NDD), seizures, both or asymptomatic. In a subgroup of traditionally phenotyped patients, there was statistically significant enrichment of neuraxial phenotypes - including NDD - among those harboring DNVs. CONCLUSION: This novel association between ACs and multiple epigenetic regulators of gene expression provides the first insight into intracranial AC pathobiology. HPO clustering reveals clinically relevant subsets of ACs and implies that traditionally symptomatic or asymptomatic ACs may be harbingers of genetically-driven aberrant neurodevelopment.

Використання систем організації знань на основі онтологій у WIKI-ресурсах

The paper considers the theoretical foundations of knowledge organization systems (KOSs) in intelligent ontology-based applications. The aim of this study is to analyze the use of different types of KOSs to organize and improve the knowledge base of semantic Wiki resources that contain heterogeneous multimedia content of large volume and have a complex structure integrated knowledge from different domains. The dialects of the OWL ontology representation language and their expressiveness for representing special cases of ontologies used in KOSs are considered. The criteria for the classification of KOSs and sphere of their usage are analyzed. Formal model of ontology for semantic Wiki resource is proposed. This model is integrated with various implementing means for different types of relations between objects in the Semantic MediaWiki environment based on templates. Problems of access and retrieval of information in these resources and methods of their solving from the KOSs point of view are considered. The software implementation of the proposed approach with the example of the portal version of the Great Ukrainian Encyclopedia (e-VUE) is realized. The urgency of the problem intensifies by the need for national information resources in martial law situation, for which the determining factors of effective information processing are both the ability to obtain satisfaction of complex information needs and the relevance of the information obtained. This increases the importance of official government portals that integrate reliable data from various fields of knowledge and prevent possible misrepresentation (both accidental and malicious) of information in resources with open content generation.

Graphol: A Graphical Language for Ontology Modeling Equivalent to OWL 2

In this paper we study Graphol, a fully graphical language inspired by standard formalisms for conceptual modeling, similar to the UML class diagram and the ER model, but equipped with formal semantics. We formally prove that Graphol is equivalent to OWL 2, i.e., it can capture every OWL 2 ontology and vice versa. We also present some usability studies indicating that Graphol is suitable for quick adoption by conceptual modelers that are familiar with UML and ER. This is further testified by the adoption of Graphol for ontology representation in several industrial projects.

A multipurpose TNM stage ontology for cancer registries

BackgroundPopulation-based cancer registries are a critical reference source for the surveillance and control of cancer. Cancer registries work extensively with the internationally recognised TNM classification system used to stage solid tumours, but the system is complex and compounded by the different TNM editions in concurrent use. TNM ontologies exist but the design requirements are different for the needs of the clinical and cancer-registry domains. Two TNM ontologies developed specifically for cancer registries were designed for different purposes and have limitations for serving wider application. A unified ontology is proposed to serve the various cancer registry TNM-related tasks and reduce the multiplication effects of different ontologies serving specific tasks. The ontology is comprehensive of the rules for TNM edition 7 as required by cancer registries and designed on a modular basis to allow extension to other TNM editions.ResultsA unified ontology was developed building on the experience and design of the existing ontologies. It follows a modular approach allowing plug in of components dependent upon any particular TNM edition. A Java front-end was developed to interface with the ontology via the Web Ontology Language application programme interface and enables batch validation or classification of cancer registry records. The programme also allows the means of automated error correction in some instances. Initial tests verified the design concept by correctly inferring TNM stage and successfully handling the TNM-related validation checks on a number of cancer case records, with a performance similar to that of an existing ontology dedicated to the task.ConclusionsThe unified ontology provides a multi-purpose tool for TNM-related tasks in a cancer registry and is scalable for different editions of TNM. It offers a convenient way of quickly checking validity of cancer case stage information and for batch processing of multi-record data via a dedicated front-end programme. The ontology is adaptable to many uses, either as a standalone TNM module or as a component in applications of wider focus. It provides a first step towards a single, unified TNM ontology for cancer registries.