Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

Kivikangas, Petri; Ishizuka, Mitsuru

doi:10.1109/icsc.2012.50

Cited by 8 publications

(4 citation statements)

References 2 publications

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“…The often-used format for ontology representation is the resource description framework [32,33], sometimes extended by the web ontology language [27,29]. Articles devoted to identifying "relatedness" of concepts to each other using the path between them do not cover the weighted approach [29,30,34,35]. However, in [36] the authors raise the issue of using the weighted approach for text categorization, whereas each document is considered as a graph to apply a categorization algorithm.…”

Section: Related Literaturementioning

confidence: 99%

“…Most current work in the literature is rather focused on contextual representation, considering "relatedness" identification rather than semantic similarity [33], which allows interrelating the concepts without a need for predefined knowledge about synonyms, hyponyms, or meronymy. For instance, in [35] the authors solely target the issue of finding the hyponyms in a specific ontology, which limits the approach to a specific ontology containing specific relation types. The issue of importing ontologies is raised in several works (e.g., [29,31,37]), whereas in [38], the authors propose to use a graph database as a "hub" to aggregate the data for further analysis.…”

Section: Related Literaturementioning

confidence: 99%

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Semantic Data Management for a Virtual Factory Collaborative Environment

et al. 2019

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Recent developments in the area of cyber-physical systems (CPSs) and Internet of Things (IoT) are among the drivers for the emergence of the Industry 4.0 concept, setting new requirements for the architecture, technology, and design approaches of modern industrial systems. Industry 4.0 assumes a higher level of intelligence, and thus autonomy of the systems and subsystems, and a larger focus on the analysis of gathered data for further utilization. The Virtual Factory Open Operating System (vf-OS) project is intended to respond to some of these key challenges, in particular for the smart factory application domain. Complementarily, data and knowledge storage and processing are also in the scope of vf-OS. This article introduces the semantic management component of vf-OS, which aims to analyze the interrelations among stored entities, as well as to define the closeness among them to generate meaningful suggestions, which can be later used by other subsystems or operators in a user-friendly way. The semantic managing system makes use of non relational approaches, namely a graph database, which enables data to be represented as graphs for further semantic querying. The developed prototype and an illustrative application case are also presented.

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Section: Related Literaturementioning

confidence: 99%

Section: Related Literaturementioning

confidence: 99%

Semantic Data Management for a Virtual Factory Collaborative Environment

et al. 2019

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“…The part of the Neo4j storage engine that stores properties is known as the PropertyStore. In general, Neo4j database allows storing data as nodes connected by arcs [27]. Neo4j have been used to implement social graph databases.…”

Section: Social Graph Approachmentioning

confidence: 99%

Towards a Social Graph Approach for Modeling Risks in Big Data and Internet of Things (IoT)

Johny

Sotiriadis

Asimakopoulou

et al. 2014

2014 International Conference on Intelligent Networking and Collaborative Systems

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The discovery and integration of big data and Internet of Things (IoTs) highlight new challenges in the area of risks. This work focuses on the analysis of literature review approaches by presenting a study that includes works for resource discovery and data integration, social search engines, ranking techniques, and social graphs in order to provide a cross comparison and a preliminary evaluation study. The approaches are analyzed in order to define theoretical key requirements that could enable the utilization of social graphs towards the discovery and modeling of interconnected entities in big data and IoT scenarios.

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“…This is due to the minimal research on ontology using Neo4j with excellence as a tool for graph database, ontology and semantic. Kivikangas [10] described that Semantic data is easily represented as graphs, provides graph database more natural abstraction for such data than relational database. While Miller [11] said that the graph databases have a natural application to biology, semantics, network systems, and recommender who require this type of data model only they can offer.Graph database is also described by Malhotra [12] as the evolution of technical knowledge representation based on semantic web.…”

Section: Introductionmentioning

confidence: 99%

Modeling Ontology and Semantic Network of Regulations in Customs and Excise

2017

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Regulations in customs and Excise have an important function in regulating legally every policy and decision making. Thus, the type of rules are made varies according to the hierarchy of the regulator. This regulation is also changing, following policy developments that occured in the higgest government. then, what if this organization has not been able to manage its regulatory archiving? even regulatory changes can not be tracked? So, we need a well-organized system that can accomodate all of the rules and the associated changes and connectedness with other type of regulations. This system will help us to provide convenience to users to search, manage and track the history of changes as well as the relationship between the rules used by the organization. This paper propose the design of an ontologybased semantic network using a graph database that use neo4j 2.3.1 as a solution. In the application that uses the sample data, we found 13 types of nodes that contains 242 child nodes and 22 type of relation that contain 548 relation that connect all the node within 3305ms.

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Improving Semantic Queries by Utilizing UNL Ontology and a Graph Database

Cited by 8 publications

References 2 publications

Semantic Data Management for a Virtual Factory Collaborative Environment

Semantic Data Management for a Virtual Factory Collaborative Environment

Towards a Social Graph Approach for Modeling Risks in Big Data and Internet of Things (IoT)

Modeling Ontology and Semantic Network of Regulations in Customs and Excise

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