48,043 research outputs found
A Semantic Collaboration Method Based on Uniform Knowledge Graph
The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes
Ontology-based data semantic management and application in IoT- and cloud-enabled smart homes
The application of emerging technologies of Internet of Things (IoT) and cloud computing have increasing the popularity of smart homes, along with which, large volumes of heterogeneous data have been generating by home entities. The representation, management and application of the continuously increasing amounts of heterogeneous data in the smart home data space have been critical challenges to the further development of smart home industry. To this end, a scheme for ontology-based data semantic management and application is proposed in this paper. Based on a smart home system model abstracted from the perspective of implementing users’ household operations, a general domain ontology model is designed by defining the correlative concepts, and a logical data semantic fusion model is designed accordingly. Subsequently, to achieve high-efficiency ontology data query and update in the implementation of the data semantic fusion model, a relational-database-based ontology data decomposition storage method is developed by thoroughly investigating existing storage modes, and the performance is demonstrated using a group of elaborated ontology data query and update operations. Comprehensively utilizing the stated achievements, ontology-based semantic reasoning with a specially designed semantic matching rule is studied as well in this work in an attempt to provide accurate and personalized home services, and the efficiency is demonstrated through experiments conducted on the developed testing system for user behavior reasoning
Patent Analytics Based on Feature Vector Space Model: A Case of IoT
The number of approved patents worldwide increases rapidly each year, which
requires new patent analytics to efficiently mine the valuable information
attached to these patents. Vector space model (VSM) represents documents as
high-dimensional vectors, where each dimension corresponds to a unique term.
While originally proposed for information retrieval systems, VSM has also seen
wide applications in patent analytics, and used as a fundamental tool to map
patent documents to structured data. However, VSM method suffers from several
limitations when applied to patent analysis tasks, such as loss of
sentence-level semantics and curse-of-dimensionality problems. In order to
address the above limitations, we propose a patent analytics based on feature
vector space model (FVSM), where the FVSM is constructed by mapping patent
documents to feature vectors extracted by convolutional neural networks (CNN).
The applications of FVSM for three typical patent analysis tasks, i.e., patents
similarity comparison, patent clustering, and patent map generation are
discussed. A case study using patents related to Internet of Things (IoT)
technology is illustrated to demonstrate the performance and effectiveness of
FVSM. The proposed FVSM can be adopted by other patent analysis studies to
replace VSM, based on which various big data learning tasks can be performed
When Things Matter: A Data-Centric View of the Internet of Things
With the recent advances in radio-frequency identification (RFID), low-cost
wireless sensor devices, and Web technologies, the Internet of Things (IoT)
approach has gained momentum in connecting everyday objects to the Internet and
facilitating machine-to-human and machine-to-machine communication with the
physical world. While IoT offers the capability to connect and integrate both
digital and physical entities, enabling a whole new class of applications and
services, several significant challenges need to be addressed before these
applications and services can be fully realized. A fundamental challenge
centers around managing IoT data, typically produced in dynamic and volatile
environments, which is not only extremely large in scale and volume, but also
noisy, and continuous. This article surveys the main techniques and
state-of-the-art research efforts in IoT from data-centric perspectives,
including data stream processing, data storage models, complex event
processing, and searching in IoT. Open research issues for IoT data management
are also discussed
Natural Notation for the Domestic Internet of Things
This study explores the use of natural language to give instructions that
might be interpreted by Internet of Things (IoT) devices in a domestic `smart
home' environment. We start from the proposition that reminders can be
considered as a type of end-user programming, in which the executed actions
might be performed either by an automated agent or by the author of the
reminder. We conducted an experiment in which people wrote sticky notes
specifying future actions in their home. In different conditions, these notes
were addressed to themselves, to others, or to a computer agent.We analyse the
linguistic features and strategies that are used to achieve these tasks,
including the use of graphical resources as an informal visual language. The
findings provide a basis for design guidance related to end-user development
for the Internet of Things.Comment: Proceedings of the 5th International symposium on End-User
Development (IS-EUD), Madrid, Spain, May, 201
Buzz monitoring in word space
This paper discusses the task of tracking mentions of some topically interesting textual entity from a continuously and dynamically changing flow of text, such as a news feed, the output from an Internet crawler or a similar text source - a task sometimes referred to as buzz monitoring. Standard approaches from the field of information access for identifying salient textual entities are reviewed, and it is argued that the dynamics of buzz monitoring calls for more accomplished analysis mechanisms than the typical text analysis tools provide today. The notion of word space is introduced, and it is argued that word spaces can be used to select the most salient markers for topicality, find associations those observations engender, and that they constitute an attractive foundation for building a representation well suited for the tracking and monitoring of mentions of the entity under consideration
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