scholarly journals Analisis dan Perancangan Software WordNet Bahasa Indonesia dengan Graph Database

2020 ◽  
Vol 2 (2) ◽  
pp. 198-209
Author(s):  
Seta Murdha Pamungkas ◽  
Muhammad Ainul Yaqin ◽  
Kurnia Z. Matondang ◽  
Asfilia N. Anggraini ◽  
Abd. Charis Fauzan
Keyword(s):  
Graph Model ◽  
Graph Database ◽  
Online Data ◽  
Bahasa Indonesia

Paper ini bertujuan untuk merancang sebuah WordNet dengan menggunakan database bermetode graph yang akan dirancang dengan tahap awal pengelompokan kata hingga tahap akhir yaitu  pengimplementasian dalam bentuk aplikasi siap pakai. Penelitian ini merancang aplikasi WordNet siap pakai yang mengimplementasikan database berbasis graph dengan menggunakan Metode Waterfall yang akan menjadi dasar alur dalam perancangan aplikasi. Uji coba dalam penelitian ini berupa: pemilahan kata, input kata ke dalam database, pemberian relasi antar kata, uji coba setiap kata dengan kata yang lain, dan implementasi database ke dalam sebuah aplikasi yang siap pakai. Pada penelitian ini menghasilkan sebuah rancangan WordNet yang menggunakan Bahasa Indonesia dengan penggunaan database berbasis graph model sebagai tempat penyimpanan data. Rancangan WordNet tersebut lalu diimplementasikan ke dalam sebuah aplikasi yang siap pakai. Data yang digunakan dalam penelitian didapatkan dari KBBI dan Tesaurus yang dapat diakses melalui media online. Data dikumpulkan secara berelasi hingga membentuk relasi Sinonim, Antonim, Imbuhan, dan Kata Dasar. Penelitian ini menggunakan relasi yang spesifik untuk menghubungkan antar node, tetapi tidak menggunakannya untuk pencarian jarak antar node. Penelitian ini menggabungkan hasil dari penelitian perancangan WordNet dan perancangan database dengan metode graph hingga membentuk sebuah aplikasi siap pakai.

scholarly journals The Emotional Geography of International Students in Online Bahasa Indonesia Learning during the COVID-19 Pandemic

2020 ◽  
Vol 10 (S3) ◽  
pp. 161-179
Author(s):  
Gatut Susanto ◽  
Suparmi ◽  
Endah Yulia Rahayu
Keyword(s):  
International Students ◽  
Qualitative Case Study ◽  
Emotional Experience ◽  
Online Data ◽  
Emotional Geography ◽  
Case Study Design ◽  
Positive Feelings ◽  
Negative Feelings ◽  
Bahasa Indonesia

This article reports a case study that explores the emotional geography of 25 international students from 12 countries in learning bahasa Indonesia for foreigners virtually during the COVID-19 pandemic. Grounded in a qualitative case study design, the recruited participants were interviewed about their emotional experience of learning bahasa Indonesia online. Data were garnered from the interviews, classroom observations, and students’ testimonials. They were analyzed with Hargreaves’s (2001) emotional geography theory. Findings showed that online bahasa Indonesia learning affects the emotional geography of international students. The international students experienced such positive feelings as intimacy, safety, happiness, seriousness, and successfulness. However, they also experienced such negative feelings as confusion, anxiety, and shock situated in online bahasa Indonesia learning. This indicates that international students should have positive feelings and maintain such feelings in order to succeed in online bahasa Indonesia learning.

scholarly journals Constructing Crop Portraits Based on Graph Databases Is Essential to Agricultural Data Mining

Information ◽  
2021 ◽  
Vol 12 (6) ◽  
pp. 227
Author(s):  
Yue-Xin Shi ◽  
Bo-Kai Zhang ◽  
Yong-Xiang Wang ◽  
Han-Qian Luo ◽  
Xiang Li
Keyword(s):  
Big Data Analytics ◽  
Graph Model ◽  
Original Data ◽  
Popular Science ◽  
Theoretical Research ◽  
Graph Database ◽  
Science Data ◽  
Comprehensive Description ◽  
Agricultural Knowledge ◽  
Crop Variety

Neo4j is a graph database that can use not only data, but also data relationships. Crop portraits, a kind of property graph, model the crop entity in the real world based on data to realize the networked management of crop knowledge. The existing crop knowledge base has shortcomings such as single crop variety, incomplete description, and lack of agricultural knowledge. Constructing crop portraits can provide a comprehensive description of crops and make up for these shortcomings. This research used agricultural question-and-answer data and popular science data obtained by text crawling as the original data, selected labels to establish a crop portrait that including three categories (crops, pesticides, and diseases and pests), and used the graph database (Neo4j) to store and display these portrait data. Information mining found that the crop portrait revealed the occurrence trend of diseases and pests, exhibited a nonintrinsic connection between different diseases and pests, and provided a variety of pesticides to choose from for control of diseases and pests. The results showed that constructing crop portraits is beneficial to agricultural analysis, and has practical application values and theoretical research prospects in the field of big data analytics.

scholarly journals A Graph Database Model for Knowledge Extracted from Place Descriptions

2018 ◽  
Author(s):  
Hao Chen ◽  
Maria Vasardani ◽  
Stephan Winter ◽  
Martin Tomko
Keyword(s):  
Management System ◽  
Graph Model ◽  
Rich Source ◽  
Extended Model ◽  
Graph Database ◽  
Contextual Knowledge ◽  
Database Model ◽  
Place Descriptions

Everyday place descriptions provide a rich source of knowledge about places and their relative locations. This research proposes a place graph model for modeling this spatial, non-spatial, and contextual knowledge from place descriptions. The model extends a prior place graph, and overcomes a number of limitations. The model is implemented using the Neo4j graph database, and a management system has also been developed that allows operations including querying, mapping, and visualizing the stored knowledge in an extended place graph. Then three experimental tasks, namely georeferencing, reasoning, and querying, are selected to demonstrate the superiority of the extended model.

scholarly journals COMBAT-TB-NeoDB: fostering tuberculosis research through integrative analysis using graph database technologies

2019 ◽  
Author(s):  
Thoba Lose ◽  
Peter van Heusden ◽  
Alan Christoffels
Keyword(s):  
Graph Model ◽  
Cost Effective ◽  
Supplementary Information ◽  
Omics Data ◽  
Graph Database ◽  
Biological Databases ◽  
Supplementary Data ◽  
Genomic Technologies ◽  
Effective Generation ◽  
Federated Queries

Abstract Motivation Recent advancements in genomic technologies have enabled high throughput cost-effective generation of ‘omics’ data from M.tuberculosis (M.tb) isolates, which then gets shared via a number of heterogeneous publicly available biological databases. Albeit useful, fragmented curation negatively impacts the researcher’s ability to leverage the data via federated queries. Results We present Combat-TB-NeoDB, an integrated M.tb ‘omics’ knowledge-base. Combat-TB-NeoDB is based on Neo4j and was created by binding the labeled property graph model to a suitable ontology namely Chado. Combat-TB-NeoDB enables researchers to execute complex federated queries by linking prominent biological databases, and supplementary M.tb variants data from published literature. Availability and implementation The Combat-TB-NeoDB (https://neodb.sanbi.ac.za) repository and all tools mentioned in this manuscript are freely available at https://github.com/COMBAT-TB. Supplementary information Supplementary data are available at Bioinformatics online.

scholarly journals DESIGN AND EVALUATION OF A BIM-GIS INTEGRATED INFORMATION MODEL USING RDF GRAPH DATABASE

2021 ◽  
Vol VIII-4/W2-2021 ◽  
pp. 175-182
Author(s):  
A.-H. Hor ◽  
G. Sohn
Keyword(s):  
Performance Metrics ◽  
Graph Model ◽  
Information Model ◽  
Query Languages ◽  
Semantic Integration ◽  
Graph Database ◽  
Graph Databases ◽  
Rdf Graph ◽  
Functional Components ◽  
Integrated Information

Abstract. The semantic integration modeling of BIM industry foundations classes and GIS City-geographic markup language are a milestone for many applications that involve both domains of knowledge. In this paper, we propose a system design architecture, and implementation of Extraction, Transformation and Loading (ETL) workflows of BIM and GIS model into RDF graph database model, these workflows were created from functional components and ontological frameworks supporting RDF SPARQL and graph databases Cypher query languages. This paper is about full understanding of whether RDF graph database is suitable for a BIM-GIS integrated information model, and it looks deeper into the assessment of translation workflows and evaluating performance metrics of a BIM-GIS integrated data model managed in an RDF graph database, the process requires designing and developing various pipelines of workflows with semantic tools in order to get the data and its structure into an appropriate format and demonstrate the potential of using RDF graph databases to integrate, manage and analyze information and relationships from both GIS and BIM models, the study also has introduced the concepts of Graph-Model occupancy indexes of nodes, attributes and relationships to measure queries outputs and giving insights on data richness and performance of the resulting BIM-GIS semantically integrated model.

scholarly journals Active Rules in a Graph Database Environment

10.29007/1w4k ◽  
2020 ◽  
Author(s):  
Ying Jin ◽  
Vadlamannati Bharath ◽  
Jinaliben Shah
Keyword(s):  
Graph Model ◽  
Database Systems ◽  
Graph Database ◽  
Business Logic ◽  
Active System ◽  
Active Rules ◽  
Nosql Databases ◽  
Execution Model ◽  
Relational Database Systems ◽  
Language Specification

With the rapid growth of data nowadays, new types of database systems are emerging in order to handle big data, known as NoSQL databases. One type of NoSQL databases is graph database, which uses the graph model to present data and the relationships among data. Existing graph database systems are passive compared to traditional relational database systems that allow automatic event handling through active rules. This paper describes our approach of incorporating active rules into graph databases, allowing users to specify business logic in a declarative manner. The active system has been built on top of a passive graph database to react to events automatically. Our focus is to specify business rules declaratively rather than enforce integrity constraint using rules. Our system consists of a language framework and an execution model. Language specification will further be illustrated by on a motivating example that shows the use of rules in an application context. The paper also describes the design and implementation of the execution model in detail.

scholarly journals Sistem Analisis Sentimen pada Ulasan Produk Menggunakan Metode Naive Bayes

2018 ◽  
Vol 4 (2) ◽  
pp. 113
Author(s):  
Billy Gunawan ◽  
Helen Sasty Pratiwi ◽  
Enda Esyudha Pratama
Keyword(s):  
Naive Bayes ◽  
Confusion Matrix ◽  
Naïve Bayes ◽  
Frequency Data ◽  
Online Data ◽  
Inverse Document Frequency ◽  
Term Frequency ◽  
Document Frequency ◽  
Bahasa Indonesia

Sistem analisis sentimen merupakan sistem yang digunakan untuk melakukan proses analisis otomatis pada ulasan produk online bahasa Indonesia untuk memperoleh informasi meliputi informasi sentimen yang merupakan bagian dari ulasan online. Data tersebut diklasifikasikan menggunakan Naive Bayes. Sistem analisis sentimen dibagi menjadi 5 (lima) tahap, yaitu crawling, pre-processing, pembobotan kata, pembentukan model dan klasifikasi sentimen. Pada pembobotan kata digunakan metode TF-IDF (Term Frequency – Inverse Document Frequency). Data yang ada akan diklasifikasikan ke dalam 5 (lima) kelas, yaitu sangat negatif, negatif, netral, positif dan sangat positif. Data tersebut kemudian akan dievaluasi menggunakan pengujian confusion matrix dengan parameter akurasi, recall, dan precision. Hasil pengujian menunjukkan pada pengujian 3 kelas (negatif, netral dan positif) hasil terbaik didapatkan pada 90% data latih dan 10% data uji dengan nilai akurasi 77.78%, recall 93.33% dan precision 77.78% dan pada pengujian 5 kelas hasil terbaik didapatkan pada 90% data latih dan 10% data uji  dengan nilai akurasi 59.33 %, recall 58.33 % dan precision 59.33 %. Hasil prediksi kelas data uji yang relevan dibandingkan antara kelas sentimen yang ditandai supervisor dan kelas sentimen yang dihasilkan oleh sistem analisis sentimen walaupun belum sepenuhnya akurat.

Reliability of MTurk Data From Masters and Workers

2020 ◽  
Vol 41 (1) ◽  
pp. 30-36
Author(s):  
Steven V. Rouse
Keyword(s):  
Cognitive Abilities ◽  
Online Survey ◽  
Quality Data ◽  
Standard Samples ◽  
Online Data ◽  
Approval Ratings ◽  
Cognitive Abilities Test ◽  
Elite Status ◽  
Master Status ◽  
Amazon's Mechanical Turk

Abstract. Previous research has supported the use of Amazon’s Mechanical Turk (MTurk) for online data collection in individual differences research. Although MTurk Masters have reached an elite status because of strong approval ratings on previous tasks (and therefore gain higher payment for their work) no research has empirically examined whether researchers actually obtain higher quality data when they require that their MTurk Workers have Master status. In two different online survey studies (one using a personality test and one using a cognitive abilities test), the psychometric reliability of MTurk data was compared between a sample that required a Master qualification type and a sample that placed no status-level qualification requirement. In both studies, the Master samples failed to outperform the standard samples.

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