Narrow Results

The advancement of the semantic web and Linked Open Data (LOD) cloud has led to the creation and integration of various knowledge bases defined by ontologies. A significant challenge within the LOD paradigm is identifying resources that refer to the same real-world object to enable large-scale data integration and sharing. In this context, instance matching has emerged as a key solution, linking co-referent instances from heterogeneous data sources using owl:sameAs links. Traditional approaches focus on schema-level matching but often fail to address property-level heterogeneity. Moreover, given the large scale of instances, examining all possible instance pairs is impractical. This paper proposes a scalable and efficient instance-matching approach using MongoDb (Humongous database) and Lucene. MongoDb stores instances at any scale and Lucene uses inverted indexes to identify matching candidates. Experiments on the instance matching track from the Ontology Alignment Evaluation Initiative (OAEI’2022) show that our approach matches the F-measure score of RE-Miner, the top performer in OAEI’2020, while surpassing all other participants in OAEI’2020, 2021 and 2022. Additionally, it operates 17 times faster than RE-Miner, four times faster than Lily and 15 times faster than LogMap, the fastest in OAEI’2020, 2021 and 2022, respectively. Moreover, we evaluate our approach on other knowledge bases from OAEI’2010. Once again, our approach gets highly competitive resuts compared to state-of-the-art approaches.

Ontologies are attracting increasing attention in software engineering research due to their ability to precisely model the semantic aspects of systems. Enriching software system models using ontology principles, especially in the process of developing interactive systems like Service-Oriented Architecture (SOA), can lead to the automated production of high-quality codes. Additionally, ontology-aware specification of the service from the abstract to the concrete level leads to early precise extraction of the service required by the users. This paper introduces a model-driven ontology-aware service development process to reduce the burden of code generation. This integrated approach utilizes a stepwise refinement methodology facilitated by a novel refinement algorithm to automate SOA development. The effectiveness of the approach is evaluated using three proposed parameters which examine the characteristics of the refined model in each refining step through some practical SOA case studies. Finally, we calculate the recall, precision, F-measure, accuracy, and also time analysis of discovered querying services for various scenarios in AWS and Netflix before and after applying ontology. The results show an average of 17% improvement in these metrics after applying ontology.

Bioinformatics can be considered as a bridge between life science and computer science, where high performance computational platforms and software are required to manage complex biological data. In this paper we present PROTEUS, a Grid-based Problem Solving Environment that integrates ontology and workflow approaches to enhance composition and execution of bioinformatics application on the Grid. Architecture and preliminary experimental results are reported.

In this paper we consider theories in which reality is described by some underlying variables, λ. Each value these variables can take represents an ontic state (a particular state of reality). The preparation of a quantum state corresponds to a distribution over the ontic states, λ. If we make three basic assumptions, we can show that the distributions over ontic states corresponding to distinct pure states are nonoverlapping. This means that we can deduce the quantum state from a knowledge of the ontic state. Hence, if these assumptions are correct, we can claim that the quantum state is a real thing (it is written into the underlying variables that describe reality). The key assumption we use in this proof is ontic indifference — that quantum transformations that do not affect a given pure quantum state can be implemented in such a way that they do not affect the ontic states in the support of that state. In fact this assumption is violated in the Spekkens toy model (which captures many aspects of quantum theory and in which different pure states of the model have overlapping distributions over ontic states). This paper proves that ontic indifference must be violated in any model reproducing quantum theory in which the quantum state is not a real thing. The argument presented in this paper is different from that given in a recent paper by Pusey, Barrett and Rudolph. It uses a different key assumption and it pertains to a single copy of the system in question.

Discrete manufacturing system generates a large amount of data and information because of the development of information technology. Hence, a management mechanism is urgently required. In order to incorporate knowledge generated from manufacturing data and production experience, a knowledge network model of the energy consumption in the discrete manufacturing system was put forward based on knowledge network theory and multi-granularity modular ontology technology. This model could provide a standard representation for concepts, terms and their relationships, which could be understood by both human and computer. Besides, the formal description of energy consumption knowledge elements (ECKEs) in the knowledge network was also given. Finally, an application example was used to verify the feasibility of the proposed method.

Diabetes mellitus is a common chronic disease in recent years. According to the World Health Organization, the estimated number of diabetic patients will increase 56% in Asia from the year 2010 to 2025, where the number of anti-diabetic drugs that doctors are able to utilize also increase as the development of pharmaceutical drugs. In this paper, we present a recommendation system for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques, where fuzzy rules are used to represent knowledge to infer the usability of the classes of anti-diabetic drugs based on fuzzy reasoning techniques. We adopt the "Medical Guidelines for Clinical Practice for the Management of Diabetes Mellitus" provided by the American Association of Clinical Endocrinologists to build the ontology knowledge base. The experimental results show that the proposed anti-diabetic drugs recommendation system gets the same accuracy rate as the one of Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) and it is better than Chen et al.'s method (R. C. Chen, Y. H. Huang, C. T. Bau and S. M. Chen, Expert Syst. Appl.39(4) (2012) 3995–4006.) due to the fact that it can deal with the semantic degrees of patients' tests and can provide different recommend levels of anti-diabetic drugs. It provides us with a useful way for anti-diabetic drugs selection based on fuzzy reasoning and ontology techniques.

With the advent of the Web era, data has exploded, with tens of thousands of textual data being generated every day. Traditional text sentiment analysis methods are mainly based on lexicon and machine learning-based methods. These methods show certain limitations as the data size increases. Here, we propose a new knowledge map design method based on convolutional neural network. For the academic literature data crawled from HowNet and Baidu Academic Website with the theme of computer, the corresponding ontology database, the fusion application of multiple data sources, and the mapping of different ontology libraries through data fusion are constructed for data sources in different fields. The global ontology library then uses the entity alignment and entity link methods for knowledge acquisition and fusion. Finally, the convolutional neural network is used for training and testing. The experimental results show that the subject search task can not only obtain the book through the convolution network, the effective academic literature in the question bank can also be used to obtain the relevance of the keyword in the search results, which verifies the effectiveness of the method.

The digital processing of content resources has subverted the traditional paper content processing model and has also spread widely. The digital resources processed by text structure need to be structured and processed by professional knowledge, which can be saved as a professional digital content resource of knowledge base and provide basic metadata for intelligent knowledge service platform. The professional domain-based knowledge system construction system platform explored in this study is designed based on natural language processing. Natural language processing is an important branch of artificial intelligence, which is the application of artificial intelligence technology in linguistics. The system first extracts the professional thesaurus and domain ontology in the digital resources and then uses the new word discovery algorithm based on the label weight designed by artificial intelligence technology to intelligently extract and clean the new words of the basic thesaurus. At the same time, the relationship system between knowledge points and elements is established to realize the association extraction of targeted knowledge points, and finally the output content is enriched from knowledge points into related knowledge systems. In order to improve the scalability and universality of the system, the extended architecture of the thesaurus, algorithms, computational capabilities, tags, and exception thesaurus was taken into account when designing. At the same time, the implementation of “artificial intelligence $+$ manual assistance” was adopted. On the basis of improving the system availability, the experimental basis of the optimization algorithm is provided. The results of this research will bring an artificial intelligence innovation after the digitization to the publishing industry and will transform the content service into an intelligent service based on the knowledge system.

With the rapid growth of image collections, image classification and annotation has been active areas of research with notable recent progress. Bag-of-Visual-Words (BoVW) model, which relies on building visual vocabulary, has been widely used in this area. Recently, attention has been shifted to the use of advanced architectures which are characterized by multi-level processing. Hierarchical Max-Pooling (HMAX) model has attracted a great deal of attention in image classification. To improve image classification and annotation, several approaches based on ontologies have been proposed. However, image classification and annotation remain a challenging problem due to many related issues like the problem of ambiguity between classes. This problem can affect the quality of both classification and annotation results. In this paper, we propose an ontology-based image classification and annotation approach. Our contributions consist of the following: (1) exploiting ontological relationships between classes during both image classification and annotation processes; (2) combining the outputs of hypernym–hyponym classifiers to lead to a better discrimination between classes; and (3) annotating images by combining hypernym and hyponym classification results in order to improve image annotation and to reduce the ambiguous and inconsistent annotations. The aim is to improve image classification and annotation by using ontologies. Several strategies have been experimented, and the obtained results have shown that our proposal improves image classification and annotation.

Reading and interpreting the medical image still remains the most challenging task in radiology. Through the important achievement of deep Convolutional Neural Networks (CNN) in the context of medical image classification, various clinical applications have been provided to detect lesions from Magnetic Resonance Imaging (MRI) and Computed Tomography (CT) scans. In the diagnosis process for the liver cancer from Dynamic Contrast-Enhanced MRI (DCE-MRI), radiologists consider three phases during contrast injection: before injection, arterial phase, and portal phase for instance. Even if the contrast agent helps in enhancing the tumoral tissues, the diagnosis may be very difficult due to the possible low contrast and pathological tissues surrounding the tumors (cirrhosis). Alongside, in the medical field, ontologies have proven their effectiveness to solve several clinical problems such as offering shareable terminologies, vocabularies, and databases. In this article, we propose a multi-label CNN classification approach based on a parallel preprocessing algorithm. This algorithm is an extension of our previous work cited in the International Conference on Pattern Recognition and Artificial Intelligence (ICPRAI) 2020. The aim of our approach is to ameliorate the detection of HCC lesions and to extract more information about the detected tumor such as the stage, the localization, the size, and the type thanks to the use of ontologies. Moreover, the integration of such information has improved the detection process. In fact, experiments conducted by testing with real patient cases have shown that the proposed approach reached an accuracy of 93% using MRI patches of $64\times 64$ pixels, which is an improvement compared with our previous works.

Ontology user portraits describe the semantic structure of users’ interests. It is very important to study the similar relationship between user portraits to find the communities with overlapping interests. The hierarchical characteristics of user interest can generate multiple similarity relations, which is conducive to the formation of interest clusters. This paper proposed a method of overlapping community detection combining the hierarchical characteristics of user interest and the module distribution entropy of node. First, a hierarchical user interest model was constructed based on the ontology knowledge base to measure the multi-granularity topic similarity of users. Then, a heterogeneous hypergraph was established by using the multi-granularity topic similarity and the following similarity of users to represent the interest network. Based on the mechanism of module distribution entropy of nodes, the community detection algorithm was applied to identify the interested community. The real performance of the proposed algorithm on multiple networks was verified by experiments. The experimental results show that the proposed algorithm is better than the typical overlapping community detection algorithm in terms of accuracy and recall rate.

Ontology, a model of knowledge representation and storage, has had extensive applications in pharmaceutics, social science, chemistry and biology. In the age of “big data”, the constructed concepts are often represented as higher-dimensional data by scholars, and thus the sparse learning techniques are introduced into ontology algorithms. In this paper, based on the alternating direction augmented Lagrangian method, we present an ontology optimization algorithm for ontological sparse vector learning, and a fast version of such ontology technologies. The optimal sparse vector is obtained by an iterative procedure, and the ontology function is then obtained from the sparse vector. Four simulation experiments show that our ontological sparse vector learning model has a higher precision ratio on plant ontology, humanoid robotics ontology, biology ontology and physics education ontology data for similarity measuring and ontology mapping applications.

Different proposals exist to represent the software maintenance process. However most of them are very informal or too focussed on a specific goal. We have developed a semi-formal ontology where the main concepts, according to the literature related to software maintenance, have been described. This ontology, besides representing static aspects, also represents dynamic issues related to the management of software maintenance projects. In order to develop an ontology a suitable methodology should also be followed. REFSENO was the methodology used in this work. The ontology that this work presents is not a preliminary idea but it has already been used in software maintenance environments, such as MANTIS, which is currently working successfully.

The rapid growth in the demand for embedded systems and the increased complexity of embedded software pose an urgent need for advanced embedded software development techniques. Software technology is shifting toward semi-automated code generation and integration of systems from components. Component-based development (CBD) techniques can significantly reduce the time and cost for developing software systems. Furthermore, effective component retrieval is a fundamental issue in CBD. In this paper, we address the issues in designing software repositories for embedded software components. We develop an On-line Repository for Embedded Software (ORES) to facilitate component management and retrieval. ORES uses an ontology-based approach to facilitate repository browsing and effective search. To allow easy browsing of ORES, we analyze the typical ontology relations for software components and develop a Merging and Echoing technique to convert the ontology into a hierarchy suitable for browsing, but without the loss of any critical semantic information contained in the ontology. We also develop an algorithm for grouping search results based on the ontology. Thus, we can display search result groups to avoid having to display a large number of search results or having to prune the results and risk reducing the recall factor. Another important aspect in embedded software is the set of nonfunctional requirements and properties. In ORES, we develop an XML-based specification method to capture nonfunctional properties as well as functional characteristics of components and enable retrieval of relevant components based on these specifications.

After years of experience in object-oriented design, software engineers have accumulated a great deal of knowledge in the design and construction of object-oriented systems: important contributions to this field including principles, heuristics, lessons learned, bad smells, refactorings, and so on, with the resultant major improvements in software development. However, this large body of knowledge is still not well organized, its terminology is ambiguous, and it is very difficult to make practical use of the contributions made. In this regard, we believe it is important to define an ontology in order to structure and unify design knowledge, since a good understanding of the experience derived from practical work is critical for software engineers. This ontology could be used to improve communication between software engineers, inter-operability among designs, design re-usability, design knowledge searching and specification, software maintenance, knowledge acquisition, etc. In the ontology we incorporate knowledge specific to both domain and technology. Such an organized body of knowledge could also be used for registering and documenting design rationale issues.

Recently, semantic web has received substantial attention from the research community. Semantic web aims to provide a new framework that can enable knowledge sharing and reusing. Semantic web is a collection of web technologies that include a number of markup languages such as RDF, OWL and RDFS. These markup languages are used for modeling a domain ontology. By using ontology to model resources, humans and computers (software agents) can have a consensus on the resource structure. The use of these technologies allows the creation of a more effective web search system. In this paper, we modeled travel domain ontology by using Web Ontology Language (OWL). Instead of inviting an expert to model the ontology, we created the travel ontology by collecting and analyzing the structural information from a number of travel related websites. Besides, we implemented an intelligent ontology-based Multi-Agent System for sighTseER (MASTER), which is constructed by using semantic web technologies. MASTER integrates Global Positioning System (GPS), ontology and agent technologies to support location awareness for providing the precise navigation and classify the tourist information for the users. The system was tested on 30 novice users. 83% of the users felt that the system can help tourists find tourist information in Hong Kong.

Success in a knowledge economy requires effectively using existing knowledge to create new knowledge. Security for knowledge sharing in enterprises is critical for protecting intellectual assets. This study develops the functional framework of a knowledge management system (KMS) with knowledge access control for effectively and securely sharing knowledge within an enterprise or across teams. The functional framework of the proposed KMS includes the following nine layers: user interface layer, knowledge access control and security layer, knowledge representation layer, knowledge process layer, conceptual knowledge layer, knowledge index layer, transport layer, middleware layer and physical knowledge layer. A method of conceptual knowledge representation in the knowledge representation layer is then proposed. Finally, an ontology-based knowledge access control model based on role-based access control (RBAC) model and the conceptual knowledge representation method is proposed for managing user knowledge privileges in a knowledge sharing enterprise. The proposed method can enhance (1) precision in describing knowledge and knowledge relationships, (2) ensure security of knowledge access and sharing within an enterprise and (3) accurately and rapidly identify user knowledge access privileges.

Although RDF ontologies are expressed based on XML syntax, existing methods to protect XML documents are not suitable for securing RDF ontologies. The graph style and inference feature of RDF ontologies demands new methods for access control. Driven by this goal, this paper proposes a query-oriented model for RDF ontology access control. The model adopts the concept of ontology view to rewrite user queries. In our approach, ontology views define accessible ontology concepts and instances a user can visit, and enables a controlled inference capability for the user. The design of the views guarantees that the views are free of conflict. Based on that, the paper describes algorithms for rewriting queries according to different views, and provides a system architecture along with an implemented prototype. In the evaluation, the system exhibits a promising result in terms of effectiveness and soundness.

Given the advent of knowledge-based economies and virtual-enterprise business models, enterprises get the knowledge not only from themselves but also from others. Distributed case-based reasoning systems (DCBRS) play important roles in knowledge-driven virtual enterprises by supporting knowledge sharing.

This study develops a novel mechanism for ontology-based distributed case-based reasoning using ontology and a proposed multistage algorithm to effectively support knowledge sharing within a virtual enterprise environment. Tasks involved in this study are as follows: (i) design an ontology-based distributed case-based reasoning architecture and procedure, (ii) develop techniques related to the ontology-based distributed case-based reasoning, and (iii) implement an ontology-based distributed case-based reasoning mechanism. Developing methods associated with ontology-based distributed case-based reasoning involves the definition and representation of a user query model, definition and representation of a knowledge case model, definition and establishment of knowledge case index structure, and development of a distributed knowledge case retrieval and knowledge case adaptation methods. Study results will facilitate heterogeneous knowledge sharing among enterprises participating in a virtual enterprise.

In recent years, 3D media have become more and more widespread and have been made available in numerous online repositories. A systematic and formal approach for representing and organizing shape-related information is needed to share 3D media, to communicate the knowledge associated to shape modeling processes and to facilitate its reuse in useful cross-domain usage scenarios. In this paper we present an initial attempt to formalize an ontology for digital shapes, called the Common Shape Ontology (CSO). We discuss about the rationale, the requirements and the scope of this ontology, we present in detail its structure and describe the most relevant choices related to its development. Finally, we show how the CSO conceptualization is used in domain-specific application scenarios.