Building the Knowledge Graph for Zakat (KGZ) in Indonesian Language

Abstract

In Indonesia, philanthropy is identical to Zakat. Zakat belongs to a specific domain because it has its characteristics of knowledge. This research studied knowledge graph in the Zakat domain called KGZ which is conducted in Indonesia. This area is still rarely performed, thus it becomes the first knowledge graph for Zakat in Indonesia. It is designed to provide basic knowledge on Zakat and managing the Zakat in Indonesia. There are some issues with building KGZ, firstly, the existing Indonesian named entity recognition (NER) is non-restricted and general-purpose based which data is obtained from a general source like news. Second, there is no dataset for NER in the Zakat domain. We define four steps to build KGZ, involving data acquisition, extracting entities and their relationship, mapping to ontology, and deploying knowledge graphs and visualizations. This research contributed a knowledge graph for Zakat (KGZ) and a building NER model for Zakat, called KGZ-NER. We defined 17 new named entity classes related to Zakat with 272 entities, 169 relationships and provided labelled datasets for KGZ-NER that are publicly accessible. We applied the Indonesian-Open Domain Information Extractor framework to process identifying entities’ relationships. Then designed modeling of information using resources description framework (RDF) to build the knowledge base for KGZ and store it to GraphDB, a product from Ontotext. This NER model has a precision 0.7641, recall 0.4544, and F1-score 0.5655. The increasing data size of KGZ is required to discover all of the knowledge of Zakat and managing Zakat in Indonesia. Moreover, sufficient resources are required in future works.

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

“…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.…”

“…This method can identify known terms and concepts in the unstructured or semi-structured text, but at the same time it also relies on updating. The ontology approach provides additional advantages in terms of making further reasoning and knowledge acquisition for the extracted concepts [23], [30].…”

Improving Indonesian Named Entity Recognition for Domain Zakat Using Conditional Random Fields

“…For example, the name of a person, organization, location, and something else in a text [1]- [3]. The main problem with Named Entity Recognition (NER) is ambiguous words or phrases, so to reduce this ambiguity, a BIO (Beginning, Inside, Outside) format is needed when labeling data [4] [5]. The unclear boundary determination is another problem with Named Entity Recognition (NER).…”

“…Improving Indonesian Named Entity Recognition for Domain Zakat using Conditional Random Fields Nur Febriana Widiyanti 1 , Husni Teja Sukmana 2* , Khodijah Hulliyah 3 , Dewi Khairani 4 , Lee Kyung Oh 5 132 local contextual characteristics, both formal and informal [15]. So managing Named Entity Recognition (NER) for Indonesians is challenging and complex.…”

“…Some issues related to Indonesian NER can be found in [12]. Zakat can be categorized as a specific domain [4], thus, the chance for making or increasing the performance of this area is still vast. Research related to Named Entity Recognition (NER) in Indonesian for the zakat domain has been carried out by [4] by using stanford-NER approach.…”