Information Extraction from Twitter Using DBpedia Ontology: Indonesia Tourism Places

Rosyiq, Ahmad; Hayah, Aina Rahmah; Hidayanto, Achmad Nizar; Naisuty, Meisuchi; Suhanto, Agus; Budi, Nur Fitriah Avuning

doi:10.1109/icimcis48181.2019.8985194

Cited by 6 publications

(4 citation statements)

References 5 publications

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“…The articles were then extracted and mapped according on author, task, Bulletin of Electr Eng & Inf ISSN: 2302-9285  Application of named entity recognition (NER) method for Indonesian datasets: a review (Indra Budi) 973 dataset, and method/technique (see Table 3). It is clear from the table above that several NER studies with Indonesian datasets have been carried out for the following tasks: complaint classification [19], quote identification [9], [20], flood monitoring extraction [7], traffic monitoring [8], [21], tourist [22], zakat [23], lipstick product reviews [24], and various model combination tests for twitter [25]- [28], online news [6], [29]- [31], and Wikipedia [32], [33]. Building a knowledge graph for zakat involves data acquisition, extracting entities and their relationships, mapping to ontologies, and applying knowledge graphs and visualizations.…”

Section: Slr Resultsmentioning

confidence: 99%

Application of named entity recognition method for Indonesian datasets: a review

Budi

Suryono

2023

Bulletin EEI

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A name entity (NE) is a proper name that designates a person, location, or organization. For humans, named entity recognition (NER) is a straightforward process insofar as many named entities are self-names, and most of them have initial capital letters and can be easily recognized, but it is very difficult for machines. This study discusses research trends in the application of NER to Indonesian datasets, particularly as it concerns certain tasks, datasets, methods/techniques, and entity labels. By conducting a systematic literature review (SLR) and bibliometric analysis with VOSviewer, this article hopes to provide opportunities for adopting old methods, combining models from previous research, and even proposing new methods. In addition, the motivation for doing SLR at NER is to look for new strategies in the supervision of financial technology (Fintech). If machines can find illegal Fintech entities on social media and online news, it can help the government to block these illegal Fintech entities. To this end, this study provides an overview of research trends in applying the NER method to Bahasa Indonesia (Indonesian) datasets, including the extraction of news articles, the monitoring of floods, and traffic.

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Section: Slr Resultsmentioning

confidence: 99%

Application of named entity recognition method for Indonesian datasets: a review

Budi

Suryono

2023

Bulletin EEI

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“…The results show that CNN + BLSTM produces better performance than C4.5, Ink, and Naive Bayes. [10] NER I am using DBpedia Ontology as a knowledge base for information extraction on Indonesian tourism places from Twitter. NER using OBIE is done to detect place names.…”

Section: Recent Workmentioning

confidence: 99%

“…One of the studies that use this semantic method in Indonesian is Rosyiq [10], who uses DBpedia Ontology as knowledge based for extracting information on Indonesian tourism places from Twitter. Rosyik performed Named Entity Recognition using Ontology-Based Information Extraction (OBIE) to detect place names.…”

Section: Semantic Approach Of Information Extractionmentioning

confidence: 99%

Indonesian Information Extraction : Challenges and Opportunities

Puspitarani

2021

JATISI

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Information extraction is part of natural language processing, aiming to find, retrieve, or process information. The data source for information extraction is text. Text cannot be separated from people's daily lives. Through text, a lot of confidential information can be obtained. To produce information, the unstructured text will be converted into structured data. There are many approaches that researchers take to this process. Most of the studies are in English. Therefore, this paper will present current research trends, challenges, and information extraction opportunities using Indonesian.

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“…However, those studies never connect Indonesian OEI to preexisting knowledge database. The closest we have is Rosyiq et al (2019), which is an attempt to extract DBPedia entities from English tweets. However, the DBPedia entities are extremely limited to Indonesian entities of dbpedia.org/ontology/Place.…”

Section: Introductionmentioning

confidence: 99%

Automatic Open Wikidata Information Extraction from Indonesian Text

Doxolodeo

2024

Preprint

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Open Information Extraction can be used to aid NLP pipeline, especially if it is linked to a knowledge graph. This is the first attempt to perform OIE in Indonesian text by leveraging pre-existing massive knowledge graph such as Wikidata. The model were given "easier" dataset sample to classify and "harder" dataset sample to classify based on its predicate's RDFS label. It achieves 70.14% F1 on the easier one and 46.68% F1 on the harder one. We also experimented with a pipeline that extracts triple "from the open", but the model struggles with triple that conveys relationship with two geographical place.

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Information Extraction from Twitter Using DBpedia Ontology: Indonesia Tourism Places

Cited by 6 publications

References 5 publications

Application of named entity recognition method for Indonesian datasets: a review

Application of named entity recognition method for Indonesian datasets: a review

Indonesian Information Extraction : Challenges and Opportunities

Automatic Open Wikidata Information Extraction from Indonesian Text

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