Complete Ontology Research Articles

OntoDin: An Islamic Ontology of Quran and Hadith

In the religion of Islam, the Quran and Hadith are considered the principal sources of legislation, and they comprise a large amount of knowledge in an unstructured textual form. Islamic texts are organized in such a way that automatic exploitation is both challenging and difficult to achieve. Lately, researchers have been interested in developing a formal description of Qur’an and Hadith semantics based on ontologies to allow computer systems to leverage their knowledge. The main problem in this field is the lack of a complete ontology that allows for a thorough understanding of Islamic texts. In this article, we attempted to solve this problem by developing an ontology covering the maximum amount of data possible. For the Hadith ontology, we opted for a method using only protégé plugins, where we based our work on Comma-Separated Values (CSV) files containing Hadith texts in Arabic and English with all the related data. This method allowed us to ensure the correctness of the results. The final result of this research is a complete Hadith ontology containing a total of 6 classes and 58 487 individuals, of which 34 373 are Hadiths in Arabic and English, with all the related data such as the list of narrators, the book, the chapter mentioned, and the topic it discusses. To further extend the ontology, we merged the Hadith ontology with full pre-existing Qur’an ontologies to build the OntoDin ontology, representing the Islamic texts with 51 classes and 168 122 individuals in Arabic and English.

Towards building knowledge by merging multiple ontologies with CoMerger: A partitioning-based approach

Ontologies are the prime way of organizing data in the Semantic Web. Often, it is necessary to combine several, independently developed ontologies to obtain a complete representation of a domain of interest. The complementarity of existing ontologies can be leveraged by merging them. Existing approaches for ontology merging mostly implement a binary merge. However, with the growing number and size of relevant ontologies across domains, scalability becomes a central challenge. A multi-ontology merging technique offers a potential solution to this problem. We present Co Merger, a scalable multiple ontologies merging method. It takes as input a set of source ontologies and existing mappings across them and generates a merged ontology. For efficient processing, rather than successively merging complete ontologies pairwise, we group related concepts across ontologies into partitions and merge first within and then across those partitions. In both steps, user-specified subsets of generic merge requirements (GMRs) are taken into account and used to optimize outputs. The experimental results on well-known datasets confirm the feasibility of our approach and demonstrate its superiority over binary strategies. A prototypical implementation is freely accessible through a live web portal.

La ontología del capital puede mejor ser explicada por el concepto de derechos de propiedad

The debate about the nature of capital has been portrayed as a dichotomy between capital as composed by material goods and capital as funds expressed in monetary terms. It has been recognized in the literature that the different ontologies discussed offer ‘at best a very partial account’ and that is the reason why a more comprehensive ontology of capital is needed. This paper explores how much that portrait is accurate historically and as a depiction of the current state of the debate about the nature of capital. It concludes with the idea that such a view is in general a correct one, and that the notion of property claims over goods that exist in the real world is the answer to the need for a more complete ontology of capital.

Approach for Reengineering Transformation from Relational Database to OWL Ontology

Ontology resources are more important for organizing knowledge. It has become an important task to improve the efficiency of ontology construction. How to generate ontology automatically from database is an emerging task in ontology buliding. In this paper we present a reengineering process that transforms relational databases to ontologies. Our goal is to have a well representation of the domain semantics to create a more complete Ontology

An Ontology of Emotion Process to Support Sentiment Analysis

Sentiment analysis is used to mine text data from many sources, including blogs, support forums, and social media, in order to extract customers’ opinions and attitudes. The results can be used to make important assessments about a customer’s attitude toward a company and if and how a company should respond. However, much research on sentiment analysis uses simple classification, where the polarity of a text that is mined is classified as positive, negative, or neutral. This research creates an ontology of emotion process to support sentiment analysis, with an emphasis on obtaining a more fine-grained assessment of sentiment than polarity. The ontology is grounded in a theory of emotion process and consists of concepts that capture the generation of emotion all the way from the occurrence of an event to the resulting behaviors of the person expressing the sentiment. It includes two lexicons: one for affect and one for appraisal. The ontology is applied to posts obtained from customer support forums of large companies to show its applicability in a multilevel evaluation. Doing so provides an example of a complete ontology assessment effort.

OntoSLAM: An Ontology for Representing Location and Simultaneous Mapping Information for Autonomous Robots

Autonomous robots are playing an important role to solve the Simultaneous Localization and Mapping (SLAM) problem in different domains. To generate flexible, intelligent, and interoperable solutions for SLAM, it is a must to model the complex knowledge managed in these scenarios (i.e., robots characteristics and capabilities, maps information, locations of robots and landmarks, etc.) with a standard and formal representation. Some studies have proposed ontologies as the standard representation of such knowledge; however, most of them only cover partial aspects of the information managed by SLAM solutions. In this context, the main contribution of this work is a complete ontology, called OntoSLAM, to model all aspects related to autonomous robots and the SLAM problem, towards the standardization needed in robotics, which is not reached until now with the existing SLAM ontologies. A comparative evaluation of OntoSLAM with state-of-the-art SLAM ontologies is performed, to show how OntoSLAM covers the gaps of the existing SLAM knowledge representation models. Results show the superiority of OntoSLAM at the Domain Knowledge level and similarities with other ontologies at Lexical and Structural levels. Additionally, OntoSLAM is integrated into the Robot Operating System (ROS) and Gazebo simulator to test it with Pepper robots and demonstrate its suitability, applicability, and flexibility. Experiments show how OntoSLAM provides semantic benefits to autonomous robots, such as the capability of inferring data from organized knowledge representation, without compromising the information for the application and becoming closer to the standardization needed in robotics.

Ontology Engineering for Gastric Dystemperament in Persian Medicine.

Developing an ontology can help collecting and sharing information in traditional medicine including Persian medicine in a well-defined format. The present study aimed to develop an ontology for gastric dystemperament in the Persian medicine. This was a mixed-methods study conducted in 2019. The first stage was related to providing an ontology requirements specification document. In the second stage, important terms, concepts, and their relationships were identified via literature review and expert panels. Then, the results derived from the second stage were refined and validated using the Delphi method in three rounds. Finally, in the fourth stage, the ontology was evaluated in terms of consistency and coherence. In this study, 241 concepts related to different types of gastric dystemperament, diagnostic criteria, and treatments in the Persian medicine were identified through literature review and expert panels, and 12 new concepts were suggested during the Delphi study. In total, after performing three rounds of the Delphi study, 233 concepts were identified. Finally, an ontology was developed with 71 classes, and the results of the evaluation study revealed that the ontology was consistent and coherent. In this study, an ontology was created for gastric dystemperament in the Persian medicine. This ontology can be used for designing future systems, such as case-based reasoning and expert systems. Moreover, the use of other evaluation methods is suggested to construct a more complete and precise ontology.

An ontology proposal for a corpus of letters of Vincenzo Bellini: formal properties of physical structure and the case of rotated texts

In this paper the formal OntoBelliniLetters ontology is described concerning the corpus of Vincenzo Bellini's letters kept at the Belliniano Civic Museum of Catania. This ontology is part of a wider project - the BellinInRete project - one of whose aims is the development of a more general and complete ontology for the whole of Vincenzo Bellini's legacy preserved in the museum. The main concepts and relations building up the ontology knowledge base are described and discussed and some formal properties of them are presented. The ontology schema is inspired by the CIDOC Conceptual Reference Model (CIDOC CRM).

ONCAR: an ontology-based approach for car automation modelling

Smart cars are vehicles conceived with technological components to ease the driving and the management of the car, and to assist drivers with safety and security, energy saving and comfort services. However, the design of these complex systems involves diverse aspects including automated behaviours and user's needs and information. A modelling language with a semantic aspect will allow a close modelisation to these requirements. Also, adequate design methods to ensure the effectiveness of such systems are needed. Yet, research to design intelligent vehicles is still limited. In this paper, we aim to offer a complete design method. For that, we define a complete ontology for car automation.

ONCAR: an ontology-based approach for car automation modelling

Smart cars are vehicles conceived with technological components to ease the driving and the management of the car, and to assist drivers with safety and security, energy saving and comfort services. However, the design of these complex systems involves diverse aspects including automated behaviours and user's needs and information. A modelling language with a semantic aspect will allow a close modelisation to these requirements. Also, adequate design methods to ensure the effectiveness of such systems are needed. Yet, research to design intelligent vehicles is still limited. In this paper, we aim to offer a complete design method. For that, we define a complete ontology for car automation.

The common data environment features from the building life cycle perspective

One of the conditions for informational support of the construction projects’ life cycle and, in particular, the capital construction projects is the organization of a common data environment or, as it is called abroad, the Common Data Environment. The common data environment, being an integral part of information modeling technologies in construction, is multilevel in nature. The article draws attention to the fact that the aforementioned research object at present is considered mainly from the standpoint of foreign national standards in the field of information modeling in construction, which considers the common data environment mainly at the level of electronic document management in daily work in the design and operation of buildings and structures. Modern domestic technologies for the organization of construction should necessarily consider buildings and structures in the context of their life cycle, and not the life path, as a sequential set of data state phases. This article discusses the common data environment features from the perspective of managing the buildings’ life cycle. Using the analytical method of similarity and the dialectical method to describe the qualitative change in the model at a new consideration level, it is hypothesized that when moving from the considering interactions within the life cycle stage to the level of interactions between the life cycle stages, the concept of “data” gives a way to the “knowledge” concept. And in order to provide a complete ontology of the buildings and structures’ “life cycle” concept, a model using knowledge share is proposed.

A Proposal of a Complete Ontology Modeling the Learner Profile to Integrate into Adaptive Hypermedia Systems

A Proposal of a Complete Ontology Modeling the Learner Profile to Integrate into Adaptive Hypermedia Systems

CBN: Constructing a clinical Bayesian network based on data from the electronic medical record.

The process of learning candidate causal relationships involving diseases and symptoms from electronic medical records (EMRs) is the first step towards learning models that perform diagnostic inference directly from real healthcare data. However, the existing diagnostic inference systems rely on knowledge bases such as ontology that are manually compiled through a labour-intensive process or automatically derived using simple pairwise statistics. We explore CBN, a Clinical Bayesian Network construction for medical ontology probabilistic inference, to learn high-quality Bayesian topology and complete ontology directly from EMRs. Specifically, we first extract medical entity relationships from over 10,000 deidentified patient records and adopt the odds ratio (OR value) calculation and the K2 greedy algorithm to automatically construct a Bayesian topology. Then, Bayesian estimation is used for the probability distribution. Finally, we employ a Bayesian network to complete the causal relationship and probability distribution of ontology to enhance the ontology inference capability. By evaluating the learned topology versus the expert opinions of physicians and entropy calculations and by calculating the ontology-based diagnosis classification, our study demonstrates that the direct and automated construction of a high-quality health topology and ontology from medical records is feasible. Our results are reproducible, and we will release the source code and CN-Stroke knowledge graph of this work after publication.1.

Building an effective and efficient background knowledge resource to enhance ontology matching

Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F-measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources.

Gastroenterology Ontology Construction Using Synonym Identification and Relation Extraction

Ontology plays an increasingly important role in knowledge management and the semantic Web. However, ontology cannot perform well in realistic diagnosis reasoning unless it contains timely and accurate medical information and its individual items display all attributes of the categories they belong to. In this paper, we present a method that extracts synonyms along with concepts and their relationships for gastroenterology ontology construction. Specifically, we reuse the existing ontology as the basis for ontology completion. In addition, we conduct synonym identification through a combined application of global context features, local context features, and medical-specific features, and incorporate dependency information into deep neural networks for relation extraction. The extracted information is merged for ontology completion. Experimental results demonstrate that the proposed synonym identification and relation extraction method achieves the best performance compared with state-of-the-art methods and also builds a more complete ontology compared with existing gastroenterology disease ontologies. Our results are reproducible, and we will release the source code and ontology of this work after publication: https://github.com/shenyingpku/gastrointestinal_owl

Building an Effective and Efficient Background Knowledge Resource to Enhance Ontology Matching

Ontology matching is critical for data integration and interoperability. Original ontology matching approaches relied solely on the content of the ontologies to align. However, these approaches are less effective when equivalent concepts have dissimilar labels and are structured with different modeling views. To overcome this semantic heterogeneity, the community has turned to the use of external background knowledge resources. Several methods have been proposed to select ontologies, other than the ones to align, as background knowledge to enhance a given ontology-matching task. However, these methods return a set of complete ontologies, while, in most cases, only fragments of the returned ontologies are effective for discovering new mappings. In this article, we propose an approach to select and build a background knowledge resource with just the right concepts chosen from a set of ontologies, which improves efficiency without loss of effectiveness. The use of background knowledge in ontology matching is a double-edged sword: while it may increase recall (i.e., retrieve more correct mappings), it may lower precision (i.e., produce more incorrect mappings). Therefore, we propose two methods to select the most relevant mappings from the candidate ones: (1) a selection based on a set of rules and (2) a selection based on supervised machine learning. Our experiments, conducted on two Ontology Alignment Evaluation Initiative (OAEI) datasets, confirm the effectiveness and efficiency of our approach. Moreover, the F-measure values obtained with our approach are very competitive to those of the state-of-the-art matchers exploiting background knowledge resources.

Semantic text-based image retrieval with multi-modality ontology and DBpedia

PurposeThe purpose of this study is to reduce the semantic distance by proposing a model for integrating indexes of textual and visual features via a multi-modality ontology and the use of DBpedia to improve the comprehensiveness of the ontology to enhance semantic retrieval.Design/methodology/approachA multi-modality ontology-based approach was developed to integrate high-level concepts and low-level features, as well as integrate the ontology base with DBpedia to enrich the knowledge resource. A complete ontology model was also developed to represent the domain of sport news, with image caption keywords and image features. Precision and recall were used as metrics to evaluate the effectiveness of the multi-modality approach, and the outputs were compared with those obtained using a single-modality approach (i.e. textual ontology and visual ontology).FindingsThe results based on ten queries show a superior performance of the multi-modality ontology-based IMR system integrated with DBpedia in retrieving correct images in accordance with user queries. The system achieved 100 per cent precision for six of the queries and greater than 80 per cent precision for the other four queries. The text-based system only achieved 100 per cent precision for one query; all other queries yielded precision rates less than 0.500.Research limitations/implicationsThis study only focused on BBC Sport News collection in the year 2009.Practical implicationsThe paper includes implications for the development of ontology-based retrieval on image collection.Originality valueThis study demonstrates the strength of using a multi-modality ontology integrated with DBpedia for image retrieval to overcome the deficiencies of text-based and ontology-based systems. The result validates semantic text-based with multi-modality ontology and DBpedia as a useful model to reduce the semantic distance.

OntoPilot: New Software to Simplify and Accelerate Ontology Development and Deployment

Ontologies (formal descriptions of the concepts and relationships that can exist in knowledge domains) provide the conceptual foundations for modern knowledge representation systems and knowledge bases, and they are a core component of automated reasoning pipelines that infer new facts from existing data. Although a variety of software tools are available for authoring and manipulating ontologies, ontology development remains a technically demanding, time consuming, and often tedious task. In this talk, I will introduce OntoPilot, new software that endeavors to at least partially replace the current patchwork of disparate ontology development tools with a single, unified development framework that is easy to learn and requires minimal knowledge of the technical details of ontology description languages. OntoPilot uses familiar tabular document formats (e.g., spreadsheets) as ontology "source code", which means that domain experts without detailed knowledge of ontology-related technologies can still directly participate in the development process. OntoPilot supports a complete ontology development workflow, including reusing terms from external ontologies, defining new terms, managing releases, and generating end-user documentation. Furthermore, OntoPilot makes it easy to build inference pipelines that use ontologies to generate novel insights from existing data. OntoPilot is open-source software, supports all major operating systems, and is freely available at https://github.com/stuckyb/ontopilot/.

Research and Implementation of Mine Risk Area Semantic Retrieval System based on Ontology

In this paper, the authors first analyze the research and development of semantic retrieval technology based on ontology, and introduce the key theoretical knowledge of ontology and semantic web, including the definition, description language, classification and semantic web. Secondly, this thesis makes a deep research on the ontology based semantic retrieval including semantic annotation, Chinese word segmentation, ontology storage, semantic association algorithm and so on. At the same time, it analyzes and introduces the construction principle, methods and tools involved in ontology. Finally, this paper designs and implements a complete ontology based semantic retrieval system based on the ontology of coal mine risk domain ontology, and verifies the feasibility of the system and the theory, and provides the basis for the development and popularization of the ontology based semantic retrieval technology.

Framework for NFC-Based Intelligent Agents: A Context-Awareness Enabler for Social Internet of Things

Context-aware applications are required to be aware of user context and ambient intelligent to support nonintrusive human-computer interaction. However, the uncertain real-world environments make it difficult for a system to perceive enough environmental contexts and achieve user's goal. Therefore, this study proposes a framework for developing an NFC-enabled intelligent agent, which combines the NFC technique with context-acquisition, ontology-knowledgebase, and semantic-adaptation modules to be aware of location, time, device, and activity contexts with respect to personal and social profiles. To cope with the uncertain environment, a credit-based incentive scheme is also proposed to encourage social cooperation and thereby enlarge the value of personal perceptions. By developing a complete ontology knowledgebase, the proposed framework can incorporate with social-cooperation schemes to recommend relevant services for supporting reactive action, proactive achievement, and social cooperation. The resultant social-advertising system shows that this framework can support a wide-range of different functionalities and is indispensable to an NFC-based intelligent agent for social Internet of things.