Innovative Semantic Web Services for Next Generation Academic Electronic Library via Web 3.0 via Distributed Artificial Intelligence

Chu, Hai-Cheng; Yang, Szu-Wei

doi:10.1007/978-3-642-28487-8_12

Cited by 8 publications

(8 citation statements)

References 14 publications

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“…How to link up the data as efficiently as human brain may be an issue as pointed out as the post Web 2.0 era or the so-called Web 3.0 epoch. For example, Chu and Yang (2012) incubates the establishment of semantic web services for next generation electronic library via Web 3.0, a kind of semantic social web mechanisms. Unlike Web 2.0 which allows the readers to read, edit and rewrite the content online (Li 2011;Li and Ah Pak 2010), it is foreseen that Web 3.0 will be more intelligent than Web 2.0 which integrate Distributed Artificial Intelligence, semantics web and intelligent agent into the ubiquitous networks (Chu and Yang 2012).…”

Section: The Rise and Problem Of Big Datamentioning

confidence: 99%

Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

2014

Risk Engineering

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The previous generation of academia faced the problem of insufficient data, academic journal articles and books. Thanks to the rapid development of World Wide Web since 90s, academic papers published in one place can be viewed in another side of the globe, insufficient literature and data problems have been relieved. Modern academic researchers, however, face another major problem of big data. There are too many irrelevant articles. Much of the time has been spent to search for the relevant articles from the irrelevant ones. One of the major aims of this chapter is to review the problem of big data based on the example of construction safety index. Construction safety is an important issue Worldwide. The development of construction safety index is of particular importance as it provides an objective measurement on the level of safety on sites. This chapter reviews (1) how intelligence is the academic search engine in screening the relevant articles out the irrelevant materials, (2) compare and contrast the methods which are used to construct the construction safety indices with the other indices constructed worldwide.

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Section: The Rise and Problem Of Big Datamentioning

confidence: 99%

Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

2014

Risk Engineering

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“…Regularly, in any case, they check misleading the acquainted structure. This paper inquiries about whether collection to Graph Databases could be a viable procedure for scaling lifted probabilistic derivation and learning strategies . We show that the diagrammatic database request to get both right and deduced numbers can make forefront induction and learning frameworks prone to speedier retrieval, without yielding execution.…”

Section: Related Workmentioning

confidence: 99%

A generic methodology for geo‐related data semantic annotation

Malik

Habib

Khalid

et al. 2018

Concurrency and Computation

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Summary Geo‐related data, also known as spatial data, is represented using a vector used for representing longitude, elevation, and latitude. Specially built systems, well known as Geographical Information Systems (GIS), made use of such data for querying, manipulating, navigation, and analyzing. In the current era of data, science needs to involve smart interactive investigation involving Internet of Data (IoD) on predicting upcoming changes and spatial updates on the map is growing rapidly. To resolve issues concerning real‐time spatial data, transformation using semantic annotation can provide a better way to translate spatial relationships. These spatial relationships will support spatial analysis by linking different cause and effect with the help of reasoning mechanism. This research's major focus is on a data transformation methodology for geo‐related semantic annotation. Spatial dataset gets stored in a database and then transformed into Extensible Markup Language (XML) and Resource Description Framework (RDF). Even for bi‐directional transformation to work properly, we need to map different schema level transformations. A deep research is conducted to consider available mappings, implementations, and updates to further improving data fusion for having better compatibility. Then, transformed data as results get analyzed and discussed based on the data mapping rules formulated. It is aimed to show the importance of reducing the response time of investigation and offer compatibility between the web and semantically enriched spatial data.

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“…There is a hefty portion of Web indexes accessible today; recovering significant data is troublesome. However, to defeat this issue in the Web to recover significant data cleverly, Semantic Web advances are assuming a noteworthy part [1][2][3][4]. Enormous detecting information is produced persistently in the Internet of Things (IoT).…”

Section: Introductionmentioning

confidence: 99%

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

Malik

Ahmad

Farhan

et al. 2016

International Journal of Distributed Sensor Networks

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The Internet of Things (IoT) is involved in dealing with physical items, gadgets, vehicles, structures, and different things that are inserted into hardware, programming, sensors, and system availability, which empowers these items to gather and trade information. Improving extraction of sensor-based data for energy awareness and then annotating it and converting it into semantically enabled form for analyzing results with the use of improved tools and applications are the focus of this research. However, as the amount of real time data gets huge, it becomes difficult to track results when needed at once. Reconciliation of heterogeneous information sources into an interlinked data is a standout among the most pertinent difficulties for some learning based systems these days. This paper forms suitable elements by a methodology for adjustment of heterogeneous sensor-based Web assets, where different tools and applications like weather detection for self-observing and self-diagnostics use dispersed human specialists and learning. The proposed general model uses a capability of the Semantic Web innovation and concentrates on the part of a semantic adjustment of existing broadly utilized models of information representation to Resource Description Framework (RDF) based semantically rich arrangement. This work is valuable for sorting out and inquiry of the detecting information in the Internet of Things.

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Innovative Semantic Web Services for Next Generation Academic Electronic Library via Web 3.0 via Distributed Artificial Intelligence

Cited by 8 publications

References 14 publications

Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

Relevant Data in the Rising Tide of Big Data: A Text-Mining Analysis in Construction Safety Index

A generic methodology for geo‐related data semantic annotation

Multiagent Semantical Annotation Enhancement Model for IoT-Based Energy-Aware Data

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