Semantic Ontology Research Articles

A review of auditing techniques for the Unified Medical Language System.

ObjectiveThe study sought to describe the literature related to the development of methods for auditing the Unified Medical Language System (UMLS), with particular attention to identifying errors and inconsistencies of attributes of the concepts in the UMLS Metathesaurus.Materials and MethodsWe applied the PRISMA (Preferred Reporting Items for Systematic Reviews and Meta-Analyses) approach by searching the MEDLINE database and Google Scholar for studies referencing the UMLS and any of several terms related to auditing, error detection, and quality assurance. A qualitative analysis and summarization of articles that met inclusion criteria were performed.ResultsEighty-three studies were reviewed in detail. We first categorized techniques based on various aspects including concepts, concept names, and synonymy (n = 37), semantic type assignments (n = 36), hierarchical relationships (n = 24), lateral relationships (n = 12), ontology enrichment (n = 8), and ontology alignment (n = 18). We also categorized the methods according to their level of automation (ie, automated systematic, automated heuristic, or manual) and the type of knowledge used (ie, intrinsic or extrinsic knowledge).ConclusionsThis study is a comprehensive review of the published methods for auditing the various conceptual aspects of the UMLS. Categorizing the auditing techniques according to the various aspects will enable the curators of the UMLS as well as researchers comprehensive easy access to this wealth of knowledge (eg, for auditing lateral relationships in the UMLS). We also reviewed ontology enrichment and alignment techniques due to their critical use of and impact on the UMLS.

Ontology Based Semantic Understanding for 3D Indoor Scenes

A scene understanding method of combining semantic segmentation and ontology description is proposed in this paper. The method not only obtains the object of each part of the scene but also gets the relationship between the parts of the scene. Firstly, to realize the semantic segmentation of indoor scenes for 3D point clouds, PointNet is used in this paper as a tool for processing point clouds, and the S3DIS as PointNet datasets for training and testing. Secondly, in order to combine semantic segmentation and ontology, an ontology to describe the relationship between objects and scenes of the semantic segmentation result is used in this paper. Finally, we build indoor scenes ontology based on IndoorGML and use Protégé to display the spatial position ontology of the indoor scene.

Polysemy and Synonymy Detection in Ontology Engineering

Polysemy, when a single term has multiple meanings, and synonymy, when multiple terms have the same meaning, are common phenomena in linguistics as well as in scientific knowledge. In ontology engineering, it is vital to detect the synonyms annotations and the multiple inheritances because of polysemy. The persistence of these issues in the semantic description of a knowledge domain causes problematic interoperability and data processing. The disambiguation of the entities, properties and relationships sense in a semantic web ontology significantly improves linked data generation and information retrieval. We explore the synonymy and polysemy in the setting of a cardiology terminology generated from textbooks on the basis of field coverage, professionals’ associations’ recommendations and bibliometrics, for the building of a cardiologic ontology. From 56,134 terms collected we found that 67.7% were unique. The indexed terms included single words, compound words and multi-word expressions. The frequency of their appearances in the combined master index was calculated and used as a marker of their significance. To cope with the linguistic polysemy and synonymy of terms, we examined them in WordNet, MeSH and BioPortal, as well as by latent semantic analysis (LSA) through singular value decomposition (SVD). Through these approaches we managed to identify and decipher semantic associations and relationships between the terms. We proposed a roadmap for ontology building from scratch by utilizing intrinsic and extrinsic knowledge resources and reuse of metadata. We anticipate that this approach is applicable in ontology engineering of different knowledge domains for relationships setting and linked data contextualization

Review of built heritage modelling: Integration of HBIM and other information techniques

Abstract Built heritage documentation involves the 3D modelling of the geometry (typically using 3D computer graphics, photogrammetry and laser scanning techniques) and information management of semantic knowledge (i.e., using Geographic Information System (GIS) and ontology tools). The recent developed Building Information Modelling (BIM) technique combines 3D modelling and information management. One of its modern application is heritage documentation and has generated a new concept of Historic/Heritage Building Information Modelling (HBIM). This paper summarises the applications of these information techniques on the built heritage documentation. We utilise Web of Science Collection to monitor the publications on built heritage documentation. We analyse the research trend in heritage modelling by comparing the attention paid by researchers before and during the 2010s. The results show that photogrammetry is always the most popular method in heritage modelling. More and more works in heritage modelling have begun to use laser scanning, computer science, GIS and especially BIM techniques. Ontologies and 3D computer graphics are traditional ways for heritage documentation. Moreover, we pay attention to the roles of BIM on heritage documentation and conduct a detailed discussion on how to extend the HBIM capabilities by integrating with other techniques. The integration provides possible enhanced functions in HBIM, including accurate parametric modelling from computer graphics, automatic semantic segmentation of 3D point cloud from reality-based modelling, spatial information management and analysis by GIS, and knowledge modelling by ontology.

A Conceptual Model of Indonesian Question Answering System based on Semantic Web

This study aims to develop a conceptual model of question answering system in Bahasa using Semantic web technology. A trigger was built to periodically collect data scattered on the internet in a data center to shorten the query process on data without reducing the data completeness. The test case domain was automotive. This study used four types of ontologies: main ontology, ontologies from manufacturers, dealers, and rentals. This research has some novelties, such as solving the weakness of distributed and centralized data and combining the advantages of both by storing distributed data into a pool (centralized data) periodically, then querying the pool. Furthermore, it makes contribution by providing a conceptual model of Indonesian question answering system, particularly in automotive domain. This has some implications, such as extend the body of knowledge of computer science, especially semantic web and ontology fields.

Text Semantic Annotation: A Distributed Methodology Based on Community Coherence

Text annotation is the process of identifying the sense of a textual segment within a given context to a corresponding entity on a concept ontology. As the bag of words paradigm’s limitations become increasingly discernible in modern applications, several information retrieval and artificial intelligence tasks are shifting to semantic representations for addressing the inherent natural language polysemy and homonymy challenges. With extensive application in a broad range of scientific fields, such as digital marketing, bioinformatics, chemical engineering, neuroscience, and social sciences, community detection has attracted great scientific interest. Focusing on linguistics, by aiming to identify groups of densely interconnected subgroups of semantic ontologies, community detection application has proven beneficial in terms of disambiguation improvement and ontology enhancement. In this paper we introduce a novel distributed supervised knowledge-based methodology employing community detection algorithms for text annotation with Wikipedia Entities, establishing the unprecedented concept of community Coherence as a metric for local contextual coherence compatibility. Our experimental evaluation revealed that deeper inference of relatedness and local entity community coherence in the Wikipedia graph bears substantial improvements overall via a focus on accuracy amelioration of less common annotations. The proposed methodology is propitious for wider adoption, attaining robust disambiguation performance.

Ontologies for geospatial information: Progress and challenges ahead

Over the past 50 years or so the representation of spatial information within computerized systems has been widely addressed and developed in order to provide suitable data manipulation, analysis, and visualisation mechanisms. The range of applications is unlimited and nowadays impacts almost all sciences and practices. However, current conceptualisations and numerical representations of geospatial information still require the development of richer abstract models that match the complexity of spatial and temporal information. Geospatial ontologies are promising modelling alternatives that might favour the implementation and sharing of geographical information. The objective of this vision paper is to provide a short introduction to the principles behind semantic ontologies and how they can be applied to complex geospatial information, by evaluating their potential and limitations.

Software Agent-Centric Semantic Social Network for Cyber-Physical Interaction and Collaboration

Considerable research has recently focused on integrating cyber-physical systems in a social context. However, several challenges remain concerning appropriate methodologies, frameworks and techniques for supporting socio-cyber-physical collaboration. Existing systems do not recognize how cyber-physical resources can be socially connected so that they interact in collaborative decision-making like humans. Furthermore, the lack of semantic representations for heterogeneous cyber-social-collaborative networks limits integration, interoperability and knowledge discovery from their underlying data sources. Semantic Web ontology models can help to overcome this limitation by semantically describing and interconnecting cyber-physical objects and human participants in a social space. This research addresses the establishment of both cyber-physical and human relationships and their interactions within a social-collaborative network. We discuss how nonhuman resources can be represented as socially connected nodes and utilized by software agents. A software agent-centric Semantic Social-Collaborative Network (SSCN) is then presented that provides functionality to represent and manage cyber-physical resources in a social network. It is supported by an extended ontology model for semantically describing human and nonhuman resources and their social interactions. A software agent has been implemented to perform some actions on behalf of the nonhuman resources to achieve cyber-physical collaboration. It is demonstrated within a real-world decision support system, GRiST (www.egrist.org), used by mental-health services in the UK.

Autonomous Navigation Framework for Intelligent Robots Based on a Semantic Environment Modeling

Humans have an innate ability of environment modeling, perception, and planning while simultaneously performing tasks. However, it is still a challenging problem in the study of robotic cognition. We address this issue by proposing a neuro-inspired cognitive navigation framework, which is composed of three major components: semantic modeling framework (SMF), semantic information processing (SIP) module, and semantic autonomous navigation (SAN) module to enable the robot to perform cognitive tasks. The SMF creates an environment database using Triplet Ontological Semantic Model (TOSM) and builds semantic models of the environment. The environment maps from these semantic models are generated in an on-demand database and downloaded in SIP and SAN modules when required to by the robot. The SIP module contains active environment perception components for recognition and localization. It also feeds relevant perception information to behavior planner for safely performing the task. The SAN module uses a behavior planner that is connected with a knowledge base and behavior database for querying during action planning and execution. The main contributions of our work are the development of the TOSM, integration of SMF, SIP, and SAN modules in one single framework, and interaction between these components based on the findings of cognitive science. We deploy our cognitive navigation framework on a mobile robot platform, considering implicit and explicit constraints for autonomous robot navigation in a real-world environment. The robotic experiments demonstrate the validity of our proposed framework.

Risk Response for Municipal Solid Waste Crisis Using Ontology-Based Reasoning.

Many cities in the world are besieged by municipal solid waste (MSW). MSW not only pollutes the ecological environment but can even induce a series of public safety crises. Risk response for MSW needs novel changes. This paper innovatively adopts the ideas and methods of semantic web ontology to build an ontology-based reasoning system for MSW risk response. Through the integration of crisis information and case resources in the field of MSW, combined with the reasoning ability of Semantic Web Rule Language (SWRL), a system of rule reasoning for risk transformation is constructed. Knowledge extraction and integration of MSW risk response can effectively excavate semantic correlation of crisis information along with key transformation points in the process of crisis evolution through rule reasoning. The results show that rule reasoning of transformation can effectively improve intelligent decision-making regarding MSW risk response.

Enhanced semantic representation of coaxiality with double material requirements

SummaryIn order to improve the assemblability and reduce the cost of the parts, the material requirements can be applied to the toleranced feature and the datum feature of the coaxiality at the same time in the practical application. How to realize the information exchange between different heterogeneous systems and how to read and explain the semantics of this complex tolerance specification automatically is one of the urgent problems to be solved in intelligent digital manufacturing. Ontology described in OWL2 DL and SWRL language has strict mathematical basis of description logic, and the knowledge represented by it has clear semantics and can be read and understood directly by computer. In addition, because of its consistency checking, knowledge reasoning, and flexible query ability, it becomes the most potential knowledge representation technology in the future. On the other hand, the semantic interpretation of tolerance specification by the new generation GPS theory based on skin model has the advantages of clear semantics, no ambiguity, and consistency at different stages of the product life cycle. In this article, the enhanced semantic representation ontology for this kind of coaxiality tolerance specification is constructed based on the advantages of the new GPS theory and ontology technology and the effectiveness of the method is verified and analyzed through a case.

Explicitly semantic representation of pattern and combined geometrical specification

AbstractModel‐based definition (MBD) containing all product and manufacturing information (PMI) has become the data source of future product development because of its incomparable advantages over traditional two‐dimensional drawings. In the whole life cycle of product development, it is an urgent problem to realize the sharing of geometric tolerance specification information of PMI among different CAx (CAD, CAE, CAM, etc.) and the automatic interpretation of its semantics by computer. In this paper, the semantic representation ontology of pattern and combined geometrical specification is studied based on the new generation GPS theory and the construction process is introduced in detail through an example. Benefiting from the logic‐based semantics of the web ontology language (OWL) and semantic web rule language (SWRL), the pattern and combined geometrical specification semantic can be interpreted directly by computer and has the advantages of clear, unambiguous, and consistent at different stages of the product life cycle. At the same time, it can automatically check the consistency of knowledge, reason new knowledge, and semantic query.

A Knowledge Representation Model Based on Select and Test Algorithm for Diagnosing Breast Cancer

There exist several terminal diseases whose fatality rate escalates with time of which breast cancer is a frontline disease among such. Computer aided systems have also been well researched through the use intelligent algorithms capable of detecting, diagnosing, and proffering treatment for breast cancer. While good research breakthrough has been attained in terms of algorithmic solution towards diagnosis of breast cancer, however, not much has been done to sufficiently model knowledge frameworks for diagnostic algorithms that are knowledge-based. While Select and Test (ST) algorithm have proven relevant for implementing diagnostic systems, through support for reasoning, however the knowledge representation pattern that enables inference of missing or ambiguous data still limits the effectiveness of ST algorithm. This paper therefore proposes a knowledge representation model to systematically model knowledge to aid the performance of ST algorithm. Our proposal is specifically targeted at developing systematic knowledge representation for breast cancer. The approach uses the ontology web language (OWL) to implement the design of the knowledge model proposed. This study aims at carefully crafting a knowledge model whose implementation seamlessly work with ST algorithm. Furthermore, this study adapted the proposed model into an implementation of ST algorithm an obtained an improved performance compared to the simple knowledge model proposed by the author of ST algorithm. Our knowledge mode resulted in an accuracy gain of 23.5% and obtained and AUC of (0.49, 1.0). This proposed model has therefore shown that combining an inference-oriented knowledge model with an inference-oriented reasoning algorithm improves the performance of computer aided diagnostic (CADx) systems. In future, we intend to enhance the proposed model to support rules. Keywords— Semantic web, ontology, OWL, breast cancer, Select and Test (ST) algorithm, knowledge representation

RETRACTED ARTICLE: Hybrid method for mining rules based on enhanced Apriori algorithm with sequential minimal optimization in healthcare industry

Data mining may enable healthcare organizations, with analysis of the different prospects and connection between seemingly unrelated information, to anticipate trends in the patient’s medical condition and behavior. Raw data are large and heterogeneous from healthcare organizations. It needs to be collected and arranged, and its integration enables medical information systems to be integrated in a united way. Health data mining offers unlimited possibilities to evaluate numerous less obvious or secret data models utilizing common techniques for study. Association rule mining (ARM) is an effective technique for detecting the connection of the data which are the most commonly used and influential algorithms in ARM for an Apriori algorithm. However, it generates a large amount of rules and does not guarantee the efficiency and value of the knowledge created. In order to overcome this issue, an enhanced Apriori algorithm (EAA) based on the knowledge of a context ontology (EAA-SMO) methodology for sequential minimal optimization (SMO) is suggested. The simple knowledge is to establish the ideas of ontology as a hierarchical structure of the conceptual clusters of specific subjects, which comprises “similar” concepts that mean an exact category of the knowledge within the domain. There is an interesting rule for each cluster based on the correlation between the items. In addition, the rule developed is classified as a prediction model for anomaly detection based on SMO regression. The experimental analysis demonstrates the proposed method improved 2% of accuracy and minimizes the execution time by 25% when compared to semantic ontology.

Web Objects Based Contextual Data Quality Assessment Model for Semantic Data Application

Due to the convergence of advanced technologies such as the Internet of Things, Artificial Intelligence, and Big Data, a healthcare platform accumulates data in a huge quantity from several heterogeneous sources. The adequate usage of this data may increase the impact of and improve the healthcare service quality; however, the quality of the data may be questionable. Assessing the quality of the data for the task in hand may reduce the associated risks, and increase the confidence of the data usability. To overcome the aforementioned challenges, this paper presents the web objects based contextual data quality assessment model with enhanced classification metric parameters. A semantic ontology of virtual objects, composite virtual objects, and services is also proposed for the parameterization of contextual data quality assessment of web objects data. The novelty of this article is the provision of contextual data quality assessment mechanisms at the data acquisition, assessment, and service level for the web objects enabled semantic data applications. To evaluate the proposed data quality assessment mechanism, web objects enabled affective stress and teens’ mood care semantic data applications are designed, and a deep data quality learning model is developed. The findings of the proposed approach reveal that, once a data quality assessment model is trained on web objects enabled healthcare semantic data, it could be used to classify the incoming data quality in various contextual data quality metric parameters. Moreover, the data quality assessment mechanism presented in this paper can be used to other application domains by incorporating data quality analysis requirements ontology.

Knowledge Representation with Ontologies and Semantic Web Technologies to Promote Augmented and Artificial Intelligence in Systems Engineering

ABSTRACTThis article discusses knowledge representation using ontologies and semantic web technologies to enable artificial intelligence (AI) for Systems Engineering. Technology trends indicate new methods and tools for digital engineering will incorporate AI and machine learning (ML) technologies. ML techniques support classification, clustering, and association identification, but struggle to explain the rationale for decision making, where multi‐domain semantic modeling and rule‐based reasoning can excel. Knowledge representation plays a key role in applying this type of AI. Ontologies are a means to domain modeling and reasoning required across Digital Thread domains instantiated in digital system models (DSM). These evolve over time as digital twins, which co‐evolve with physical instantiations of a DSM. Semantic technologies and ontologies formalize knowledge as an enabler for reasoning, with interoperable ontologies enabling reason about systems engineering across domains.

Modeling and reasoning of IoT architecture in semantic ontology dimension

The architecture for IoT is the primary foundation for designing and implementing the System of Internet of things. This paper discusses the theory, method, tools and practice of modeling and reasoning the architecture of the Internet of Things system from the dimension of semantic ontology. This paper breaks the way of static ontology modeling, and proposes an implementation framework for real-time and dynamic ontology modeling of the IoT system from the running system. According to the actual needs of the health cabin IoT system and the combination of theory and practice, the system architecture model of the semantic ontology dimension of IoT is built. Then, based on the reasoning rules of the ontology model, the model is reasoned by Pellet reasoning engine which injects the atom of the custom reasoning built-ins into the source code. In this way we have realized the automatic classification and attribute improvement of resources and behaviors of the IoT system, the real-time working state detection and fault diagnosis of the IoT system, and the automatic control of the IoT system and resources.

Things2Vec: Semantic Modeling in the Internet of Things With Graph Representation Learning

The advent of fifth generation (5G) enables the Internet of Things (IoT) to connect a massive number of things. The interaction and communication among these things generate an enormous amount of context-aware data that is semantically diverse. Traditional data representation approaches, such as semantic annotation, ontology, and semantic Web technology are rule based, which lack flexibility and adaptability when applied to IoT. To address the challenge, this article mainly focuses on the problem of semantic representation, which is essential for processing and fusion of IoT data. To serve as a bridge, we propose a high-level framework, namely, Things2Vec, which aims to produce the latent semantic representations from the interaction of things through the graph embedding technique. These semantic representations benefit various IoT semantic analysis tasks, such as the IoT service recommendation and automation of things. In Things2Vec, we utilize the graph to model the function sequence relationships that are generated by the interaction of things, which is called the IoT context graph. Since these function sequence relationships are heterogeneous in terms of semantics, it causes general graph embeddings to fail to learn complete information. Thus, we propose a biased random walk procedure, which is tailored to capture the neighborhoods of nodes with different types of semantic relationships. Extensive experiments are carried out, and our results show that the proposed method can effectively capture the semantic relationships among context-aware data in IoT.

Development of Semantic Ontology Modeling in Knowledge Representation of Balinese Gamelan Instruments

Indonesia has many types of cultural and artistic heritage, which one is Balinese gamelan. Knowledge of Balinese gamelan still tends to be less explicitly collected. This result the cultural heritage knowledge, especially the Balinese gamelan challenging to be learned by the young and future Balinese generation. Therefore, the knowledge of Balinese gamelan information should be documented well, especially in the digital form. In this research, we develop an ontology for Balinese cultural heritage, specifically the gamelan Bali. This ontology can be used to capture, document, and represent knowledge surrounds the Balinese gamelan domain. The construction of the ontology model was carried out using the Methontology methodology. The gamelan ontology has 112 classes, 31 datatype properties, 53 object properties, and 289 instances. Further research needs to carry out in order to evaluate and improve the quality of ontology, and follow-up by the implementation into a semantic web-based application.

Internet Data Analysis Methodology for Cyberterrorism Vocabulary Detection, Combining Techniques of Big Data Analytics, NLP and Semantic Web

This article presents a methodology for the analysis of data on the Internet, combining techniques of Big Data analytics, NLP and semantic web in order to find knowledge about large amounts of information on the web. To test the effectiveness of the proposed method, webpages about cyberterrorism were analyzed as a case study. The procedure implemented a genetic strategy in parallel, which integrates (Crawler to locate and download information from the web; to retrieve the vocabulary, using techniques of NLP (tokenization, stop word, TF, TFIDF), methods of stemming and synonyms). For the pursuit of knowledge was built a dataset through the description of a linguistic corpus with semantic ontologies, considering the characteristics of cyber-terrorism, which was analyzed with the algorithms, Random Forests (parallel), Boosting, SVM, neural network, K-nn and Bayes. The results reveal a percentage of the 95.62% accuracy in the detection of the vocabulary of cyber-terrorism, which were approved through cross validation, reaching 576% time savings with parallel processing.