Visualisasi Pemain Sepak Bola Indonesia pada DBPedia dengan menggunakan Node2Vec dan Closeness Centrality

Abstract. Visualization of Indonesian Football Players on DBpedia through Node2Vec and Closeness Centrality Implementation. Through Semantic Web, data available on the internet are connected in a large graph. Those data are still raw so that they need to be processed to be an information that can help humans. This research aims to process and analyze the Indonesian soccer player graph by implementing node2vec and closeness centrality algorithm. The graph is modeled through a dataset obtained from the DBpedia by performing a SPARQL query on the SPARQL endpoint. The results of the Node2vec algorithm and closeness centrality are visualized for further analysis. Visualization of node2vec shows that the defenders are distributed over the players. Meanwhile, the result of closeness centrality shows that the strikers have the highest centrality score compared to other positions.Keywords: visualization, node2vec, closeness centralityAbstrak. Dengan adanya web semantik, data yang tersebar di internet dapat saling terhubung dan membentuk suatu graf. Data yang ada pada graf tersebut masih berupa data mentah sehingga perlu dilakukan pengolahan agar data mentah tersebut dapat menjadi informasi yang dapat membantu manusia. Penelitian ini bertujuan untuk melakukan pengolahan dan analisis terhadap graf pemain sepak bola Indonesia dengan mengimplementasikan algoritma node2vec dan closeness centrality. Graf dimodelkan melalui dataset yang didapat dari website DBpedia dengan cara melakukan query SPARQL pada SPARQL endpoint. Hasil dari algoritma node2vec dan closeness centrality divisualisasikan untuk dianalisis. Visualisasi dari node2vec menunjukkan pemain defender tersebar. Hasil closeness centrality menunjukkan bahwa pemain striker memiliki nilai tertinggi daripada posisi lainnya.Kata Kunci: visualisasi, node2vec, closeness centrality

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Discovering Complex Relationships of Drugs over Distributed Knowledgebases

Mobile Computing and Wireless Networks ◽

10.4018/978-1-4666-8751-6.ch070 ◽

2016 ◽

pp. 1572-1591

Author(s):

Juan Li ◽

Ranjana Sharma ◽

Yan Bai

Keyword(s):

Drug Discovery ◽

Semantic Web ◽

Distributed System ◽

Large Scale ◽

The Internet ◽

Full Potential ◽

Web Data ◽

Semantic Relationships ◽

Complex Relationships

Drug discovery is a lengthy, expensive and difficult process. Indentifying and understanding the hidden relationships among drugs, genes, proteins, and diseases will expedite the process of drug discovery. In this paper, we propose an effective methodology to discover drug-related semantic relationships over large-scale distributed web data in medicine, pharmacology and biotechnology. By utilizing semantic web and distributed system technologies, we developed a novel hierarchical knowledge abstraction and an efficient relation discovery protocol. Our approach effectively facilitates the realization of the full potential of harnessing the collective power and utilization of the drug-related knowledge scattered over the Internet.

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Discovering Complex Relationships of Drugs over Distributed Knowledgebases

International Journal of Distributed Systems and Technologies ◽

10.4018/ijdst.2014010102 ◽

2014 ◽

Vol 5 (1) ◽

pp. 22-39

Author(s):

Juan Li ◽

Ranjana Sharma ◽

Yan Bai

Keyword(s):

Drug Discovery ◽

Semantic Web ◽

Distributed System ◽

Large Scale ◽

The Internet ◽

Full Potential ◽

Web Data ◽

Semantic Relationships ◽

Complex Relationships

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Creating RESTful APIs over SPARQL endpoints using RAMOSE

Semantic Web ◽

10.3233/sw-210439 ◽

2021 ◽

pp. 1-19

Author(s):

Marilena Daquino ◽

Ivan Heibi ◽

Silvio Peroni ◽

David Shotton

Keyword(s):

Semantic Web ◽

Service Providers ◽

Query Language ◽

Sparql Query ◽

Direct Access ◽

Technical Solution ◽

Semantic Web Technologies ◽

Web Technologies ◽

Sparql Endpoint ◽

Rdf Data

Semantic Web technologies are widely used for storing RDF data and making them available on the Web through SPARQL endpoints, queryable using the SPARQL query language. While the use of SPARQL endpoints is strongly supported by Semantic Web experts, it hinders broader use of RDF data by common Web users, engineers and developers unfamiliar with Semantic Web technologies, who normally rely on Web RESTful APIs for querying Web-available data and creating applications over them. To solve this problem, we have developed RAMOSE, a generic tool developed in Python to create REST APIs over SPARQL endpoints. Through the creation of source-specific textual configuration files, RAMOSE enables the querying of SPARQL endpoints via simple Web RESTful API calls that return either JSON or CSV-formatted data, thus hiding all the intrinsic complexities of SPARQL and RDF from common Web users. We provide evidence that the use of RAMOSE to provide REST API access to RDF data within OpenCitations triplestores is beneficial in terms of the number of queries made by external users of such RDF data using the RAMOSE API, compared with the direct access via the SPARQL endpoint. Our findings show the importance for suppliers of RDF data of having an alternative API access service, which enables its use by those with no (or little) experience in Semantic Web technologies and the SPARQL query language. RAMOSE can be used both to query any SPARQL endpoint and to query any other Web API, and thus it represents an easy generic technical solution for service providers who wish to create an API service to access Linked Data stored as RDF in a triplestore.

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Research and Development on Semantic Web Data Management

Journal of Software ◽

10.3724/sp.j.1001.2009.03678 ◽

2009 ◽

Vol 20 (11) ◽

pp. 2950-2964 ◽

Cited By ~ 4

Author(s):

Xiao-Yong DU ◽

Yan WANG ◽

Bin LÜ

Keyword(s):

Semantic Web ◽

Research And Development ◽

Data Management ◽

Web Data ◽

Web Data Management

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Learning to detect abnormal semantic web data

Proceedings of the sixth international conference on Knowledge capture - K-CAP '11 ◽

10.1145/1999676.1999713 ◽

2011 ◽

Cited By ~ 1

Author(s):

Yang Yu ◽

Xingjian Zhang ◽

Jeff Heflin

Keyword(s):

Semantic Web ◽

Web Data

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How Industry 4.0 Can Benefit From Semantic Web Technologies and Artefacts

International Journal of Software Science and Computational Intelligence ◽

10.4018/ijssci.2021100105 ◽

2021 ◽

Vol 13 (4) ◽

pp. 64-74

Author(s):

Leila Zemmouchi-Ghomari

Keyword(s):

Cloud Computing ◽

Internet Of Things ◽

Semantic Web ◽

Industry 4.0 ◽

The Internet ◽

Time Data ◽

Semantic Web Technologies ◽

Web Technologies ◽

Real Time Data ◽

The Internet Of Things

Industry 4.0 is a technology-driven manufacturing process that heavily relies on technologies, such as the internet of things (IoT), cloud computing, web services, and big real-time data. Industry 4.0 has significant potential if the challenges currently being faced by introducing these technologies are effectively addressed. Some of these challenges consist of deficiencies in terms of interoperability and standardization. Semantic Web technologies can provide useful solutions for several problems in this new industrial era, such as systems integration and consistency checks of data processing and equipment assemblies and connections. This paper discusses what contribution the Semantic Web can make to Industry 4.0.

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Supporting Complex Thematic, Spatial and Temporal Queries over Semantic Web Data

GeoSpatial Semantics - Lecture Notes in Computer Science ◽

10.1007/978-3-540-76876-0_15 ◽

2007 ◽

pp. 228-246 ◽

Cited By ~ 10

Author(s):

Matthew Perry ◽

Amit P. Sheth ◽

Farshad Hakimpour ◽

Prateek Jain

Keyword(s):

Semantic Web ◽

Web Data

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Learning from Ontology Streams with Semantic Concept Drift

Proceedings of the Twenty-Sixth International Joint Conference on Artificial Intelligence ◽

10.24963/ijcai.2017/133 ◽

2017 ◽

Cited By ~ 7

Author(s):

Jiaoyan Chen ◽

Freddy Lecue ◽

Jeff Z. Pan ◽

Huajun Chen

Keyword(s):

Semantic Web ◽

Data Stream ◽

Concept Drift ◽

Data Distribution ◽

Accurate Prediction ◽

Knowledge Structures ◽

Semantic Concept ◽

Web Data ◽

Semantic Inference

Data stream learning has been largely studied for extracting knowledge structures from continuous and rapid data records. In the semantic Web, data is interpreted in ontologies and its ordered sequence is represented as an ontology stream. Our work exploits the semantics of such streams to tackle the problem of concept drift i.e., unexpected changes in data distribution, causing most of models to be less accurate as time passes. To this end we revisited (i) semantic inference in the context of supervised stream learning, and (ii) models with semantic embeddings. The experiments show accurate prediction with data from Dublin and Beijing.

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