Linked data based semantic similarity and data mining

Author(s):  
Hao Sheng ◽  
Huajun Chen ◽  
Tong Yu ◽  
Yelei Feng
2022 ◽  
pp. 60-72
Author(s):  
Blessing Babawale Amusan ◽  
Adepero Olajumoke Odumade

There is no doubt that data mining and linked data can enhance library service delivery. Data mining aspects such as text and image mining will enable libraries to have access to data that can be used to discover new knowledge aid planning for effective service delivery or service improvement. Also, linked data will enable libraries connect with other libraries to share such data that can enhance job performance leading to enhanced productivity, improved service delivery, and wider visibility and access to library resources.


Author(s):  
Pedro Fonseca-Ortiz ◽  
Hector G. Ceballos

"Semantic Web Technology proposes the use of linked data and ontologies as a mean for providing meaning to information. Even though several tools for the analysis and visualization of linked data exist, these tools require a lot of specialized knowledge to fulfill a purpose. Additionally, this complexity hardens its use for nonexperienced users therefore limiting semantic web applications. This paper describes a tool that combines the use of a recommendation system and an intuitive dynamic user interface for navigating linked data. The tool guides the user to find resources of interest by highlighting those related to his search intention. This is, the platform learns on the fly the user interest and makes recommendations based on the connections between resources."


Author(s):  
Eliot Bytyçi ◽  
Besmir Sejdiu ◽  
Arten Avdiu ◽  
Lule Ahmedi

The Internet of Things (IoT) vision is to connect uniquely identifiable devices that surround us to the Internet, which is best described through ontologies. Thereby, new emerging technologies such as wireless sensor networks (WSN) are recognized as an essential enabling component of the IoT today. Hence, given the increasing interest to provide linked sensor data through the Web either following the Semantic Web Enablement (SWE) standard or the Linked Data approach, there is a need to also explore those data for potential hidden knowledge through data mining techniques utilized by a domain ontology. Following that rationale, a new lightweight IoT architecture SEMDPA has been developed. It supports linking sensors and other devices, as well as people via a single web by mean of a device-person-activity (DPA) crossroad ontology. The architecture is validated by mean of three rich-in-semantic services: contextual data mining over WSN, semantic WSN web enablement, and Linked WSN data. SEMDPA could be easily extensible to capture semantics of input sensor data from other domains as well.


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