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نتیجه جستجو - Semantic reasoning

تعداد مقالات یافته شده: 8
ردیف عنوان نوع
1 Semantic approach to compliance checking of underground utilities
رویکرد معنایی برای بررسی انطباق ابزارهای زیرزمینی-2020
Utility regulations stipulate the spatial configurations between underground utilities and their surroundings to avoid interferences and disruptions of utility services. Utility compliance checking aims to detect spatial noncompliances in underground utilities by examining geospatial data of utilities and their surroundings against textual data of utility regulations. However, the integration of heterogeneous utility geospatial and textual data for compliance checking remains a big challenge. This paper presents a semantic approach to integrate heterogeneous data and enable automated compliance checking of underground utilities through logic and spatial reasoning. The approach consists of the following key components: (1) four interlinked ontologies that provide the semantic schema for heterogeneous data relevant to utility compliance checking, (2) two data convertors for the conversion of heterogeneous data from proprietary formats into a common and interoperable format following the semantic schema, and (3) a query mechanism with spatial extensions for the detection of noncompliant utility instances. The approach was tested on a sample utility database, and the results demonstrate the success of the proposed approach in the integration of heterogeneous data from multiple sources and automated detection of spatial non-compliances in underground utilities. In addition to utility compliance checking, the approach can be extended to other application cases where both data integration from multiple sources and spatial reasoning are required.
Keywords: Ontology | Data integration | Semantic reasoning | Utility compliance checking
مقاله انگلیسی
2 Achieving security-by-design through ontology-driven attribute-based access control in cloud environments
دستیابی به امنیت توسط طراحی از طریق کنترل دسترسی مبتنی بر ویژگی شناسی مبتنی بر هستی شناسی در محیط های ابری-2019
The constantly increasing number of cyberattacks worldwide raise significant security concerns that generally deter small, medium and large enterprises from adopting the cloud paradigm and benefitting from the numerous advantages that it offers. One way to alleviate these concerns is to devise suitable policies that infuse adequate access controls into cloud services. However, the dynamicity inherent in cloud environments, coupled with the heterogeneous nature of cloud services, hinders the formulation of effective and interoperable access control policies that are suitable for the underlying domain of application. To this end, this work proposes an approach to the semantic representation of access control policies and, in particular, to the semantic representation of the context expressions incorporated in such policies. More specifically, the proposed approach enables stakeholders to accurately define the structure of their policies, in terms of relevant knowledge artefacts, and thus infuse into these policies their particular security and business requirements. This clearly leads to more effective policies, whilst it enables semantic reasoning about the abidance of policies by the prescribed structure. In order to alleviate the scalability concerns associated with semantic reasoning, the proposed approach introduces a reference implementation that extends XACML 3.0 with an expert system fused with reasoning capabilities through the incorporation of suitable meta-rules.
Keywords: Context-aware security | Ontologies | Access control policies | Data privacy | Security-by-design | Semantic reasoning
مقاله انگلیسی
3 Semantic reasoning in service robots using expert systems
استدلال معنایی در روبات های کارگر با استفاده از سیستم های خبره-2019
This paper presents the semantic-reasoning module of VIRBOT, our proposed architecture for service robots. We show that by combining symbolic AI with digital-signal processing techniques this module achieves competitive performance. Our system translates a voice command into an unambiguous representation that helps an inference engine, built around an expert system, to perform action and motion planning. First, in the natural-language interpretation process, the system generates two outputs: (1) conceptual dependence, expressing the linguistic meaning of the statement, and (2) verbal confirmation, a paraphrase in natural language that is repeated to the user to confirm that the command has been correctly understood. Then, a conceptual-dependency interpreter extracts semantic role structures from the input sentence and looks for such structures in a set of known interpretation patterns. We evaluate this approach in a series of skill-specific semantic-reasoning experiments. Finally, we demonstrate our system in the general-purpose service robot test of the RoboCup-at-Home international competition, where incomplete information is given to a robot and the robot must recognize and request the missing information, and we compare our results with a series of baselines from the competition where our proposal performed best.
Keywords: Service robots | Semantic reasoning | Knowledge representation
مقاله انگلیسی
4 Towards a dynamic discovery of smart services in the social internet of things
به سوی کشف پویای خدمات هوشمند در اینترنت اشیاء اجتماعی -2017
The paradigm of the Social Internet of Things (SIoT) boosts a new trend wherein the connectivity and user friendliness benefits of Social Network Services (SNS) are exhibited within the network of connected objects, i.e. the Internet of Things (IoT). The SIoT exceeds the more traditional paradigm of IoT with an enhanced intelligence and context-awareness. In this paper, a novel service framework based on a cognitive reasoning approach for dy namic SIoT services discovery in smart spaces is proposed. That is, reasoning about users’ situational needs, preferences, and other social aspects along with users’ surrounding en vironment is proposed for generating a list of situation-aware services which matches users’ needs. This reasoning approach is then implemented as a proof-of-concept proto type, namely Airport Dynamic Social, within a smart airport. Finally, an empirical study to evaluate the reasoning approach’s efficiency shows improved services adaptability to situ ational needs compared to common approaches proposed in literature.
Keywords: Social Internet of Things (SIoT) | Internet of Things (IoT) | Context-awareness | Semantic reasoning | Services discovery | Service framework
مقاله انگلیسی
5 Network security assessment using a semantic reasoning and graph based approach
ارزیابی امنیت شبکه با استفاده از استدلال معنایی و رویکرد مبتنی بر گراف-2017
Owing to the high value of business data, sophisticated cyber-attacks targeting enterprise networks have become more prominent, with attackers trying to penetrate deeper into and reach wider from the compromised machines. An important security requirement is that domain experts and network administrators have a common vocabulary to share security knowledge and quickly help each other respond to new threats. We propose an innovative ontology and graph-based approach for security assessment. An ontology is designed to represent security knowledge such as that of assets, vulnerabilities, and attacks in a com mon form. Using the inference abilities of the ontological model, an efficient system frame work is proposed to generate attack graphs and assess network security. The performance of the proposed system is evaluated on test networks of differing sizes and topologies.
Keywords: Network security | Security ontology | Attack graph | Semantic reasoning
مقاله انگلیسی
6 Semantic Reasoning for Context-Aware Internet of Things Applications
استدلال معنایی برای برنامه های کاربردی متن-آگاه اینترنت اشیاء -2017
Acquiring knowledge from continuous and heterogeneous data streams is a prerequisite for Internet of Things (IoT) applications. Semantic technologies provide comprehensive tools and applicable methods for representing, integrating, and acquiring knowledge. However, resourceconstraints, dynamics, mobility, scalability, and real-time requirements introduce challenges for applying these methods in IoT environments. We study how to utilize semantic IoT data for reasoning of actionable knowledge by applying stateof-the-art semantic technologies. For performing these studies, we have developed a semantic reasoning system operating in a realistic IoT environment. We evaluate the scalability of different reasoning approaches, including a single reasoner, distributed reasoners, mobile reasoners, and a hybrid of them. We evaluate latencies of reasoning introduced by different semantic data formats. We verify the capabilities of promising semantic technologies for IoT applications through comparing the scalability and real-time response of different reasoning approaches with various semantic data formats. Moreover, we evaluate different data aggregation strategies for integrating distributed IoT data for reasoning processes.
Index Terms: Internet of Things (IoT) | knowledge repre sentations | reasoning | resource description framework (RDF) | semantic technologies
مقاله انگلیسی
7 Big data and semantics management system for computer networks
سیستم مدیریت معنایی و داده های بزرگ برای شبکه های کامپیوتری-2017
We define “Big Networks” as those that generate big data and can benefit from big data management in their operations. Examples of big networks include the current Internet and the emerging Internet of things and social networks. The ever-increasing scale, complexity and heterogeneity of the Internet make it harder to discover emergent and anomalous behavior in the network traffic. We hypothesize that en dowing the otherwise semantically-oblivious Internet with “memory” management mimicking the human memory functionalities would help advance the Internet capability to learn, conceptualize and effectively and efficiently store traffic data and behavior, and to more accurately predict future events. Inspired by the functionalities of human memory, we proposed a distributed network memory management system, termed NetMem, to efficiently store Internet data and extract and utilize traffic semantics in matching and prediction processes. In particular, we explore Hidden Markov Models (HMM), Latent Dirichlet Allo cation (LDA), and simple statistical analysis-based techniques for semantic reasoning in NetMem. Addi tionally, we propose a hybrid intelligence technique for semantic reasoning integrating LDA and HMM to extract network semantics based on learning patterns and features with syntax and semantic dependen cies. We also utilize locality sensitive hashing for reducing dimensionality. Our simulation study using real network traffic demonstrates the benefits of NetMem and highlights the advantages and limitations of the aforementioned techniques.
Keywords:Network management|Big data|Bio-inspired design|Semantics reasoning|Pattern learning|Hybrid intelligence
مقاله انگلیسی
8 Big data and semantics management system for computer networks
داده های بزرگ و سیستم مدیریت معناشناسی برای شبکه های کامپیوتری-2017
We define “Big Networks” as those that generate big data and can benefit from big data management in their operations. Examples of big networks include the current Internet and the emerging Internet of things and social networks. The ever-increasing scale, complexity and heterogeneity of the Internet make it harder to discover emergent and anomalous behavior in the network traffic. We hypothesize that en dowing the otherwise semantically-oblivious Internet with “memory” management mimicking the human memory functionalities would help advance the Internet capability to learn, conceptualize and effectively and efficiently store traffic data and behavior, and to more accurately predict future events. Inspired by the functionalities of human memory, we proposed a distributed network memory management system, termed NetMem, to efficiently store Internet data and extract and utilize traffic semantics in matching and prediction processes. In particular, we explore Hidden Markov Models (HMM), Latent Dirichlet Allo cation (LDA), and simple statistical analysis-based techniques for semantic reasoning in NetMem. Addi tionally, we propose a hybrid intelligence technique for semantic reasoning integrating LDA and HMM to extract network semantics based on learning patterns and features with syntax and semantic dependen cies. We also utilize locality sensitive hashing for reducing dimensionality. Our simulation study using real network traffic demonstrates the benefits of NetMem and highlights the advantages and limitations of the aforementioned techniques.
Keywords:Network management|Big data|Bio-inspired design|Semantics reasoning|Pattern learning|Hybrid intelligence
مقاله انگلیسی
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