Axiom-Based Feedback Cycle for Relation Extraction in Ontology Learning from Text

Informasi yang tersebar pada berbagai sumber di internet banyak ditujukan hanya untuk manusia saja. Sementara itu, muncul kebutuhan agar informasi tersebut tidak hanya bisa dibaca dan dipahami oleh manusia saja, tetapi juga oleh mesin. Informasi dalam format yang dapat dipahami oleh mesin dapat digunakan untuk berbagai keperluan, misalnya: menjadi basis pengetahuan untuk penalaran, sharing knowledge antar mesin, semantic search, visualisasi informasi, dsb. Ontology learning adalah metode yang dapat mengekstrak informasi dari teks tidak terstruktur pada suatu dokumen atau halaman web untuk kemudian diubah menjadi basis pengetahuan dalam format yang dapat dipahami oleh mesin, yaitu dalam bentuk ontologi. Metode tersebut terdiri dari beberapa tahapan, yaitu: preprocessing, ekstraksi konsep, ekstraksi relasi, dan evaluasi. Preprocessing menyiapkan korpus uji sehingga siap untuk masuk kedalam metode ekstraksi konsep, yang menggunakan algoritma entropy concept extraction, pada bagian ekstraksi relasi digunakan algoritma subcat relation extraction, sedangkan pada bagian evaluasi ontologi menggunakan metode expert evaluation. Hasil akhir menunjukkan akurasi pada nilai 89.84% untuk ekstraksi konsep, 93.02% untuk ekstraksi relasi, dengan kepercayaan terhadap ekstraksi relasi pada prosentase 71.15%. Kata kunci: ontology learning, entropy concept extraction, subcat relation extraction.

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Towards linked open data enabled ontology learning from text

Proceedings of the 18th International Conference on Information Integration and Web-based Applications and Services - iiWAS '16 ◽

10.1145/3011141.3011184 ◽

2016 ◽

Author(s):

Meisam Booshehri ◽

Peter Luksch

Keyword(s):

Open Data ◽

Linked Open Data ◽

Ontology Learning ◽

Learning From Text

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Learning domain taxonomies: the TaxoLine approach

International Journal of Web Information Systems ◽

10.1108/ijwis-04-2017-0024 ◽

2017 ◽

Vol 13 (3) ◽

pp. 281-301 ◽

Cited By ~ 5

Author(s):

Omar El Idrissi Esserhrouchni ◽

Bouchra Frikh ◽

Brahim Ouhbi ◽

Ismail Khalil Ibrahim

Keyword(s):

Execution Time ◽

Design Methodology ◽

State Of The Art ◽

Relation Extraction ◽

Ontology Learning ◽

Conditional Mutual Information ◽

Web Documents ◽

Content Type ◽

Innovative Methodology

Purpose The aim of this paper is to present an online framework for building a domain taxonomy, called TaxoLine, from Web documents automatically. Design/methodology/approach TaxoLine proposes an innovative methodology that combines frequency and conditional mutual information to improve the quality of the domain taxonomy. The system also includes a set of mechanisms that improve the execution time needed to build the ontology. Findings The performance of the TaxoLine framework was applied to nine different financial corpora. The generated taxonomies are evaluated against a gold-standard ontology and are compared to state-of-the-art ontology learning methods. Originality/value The experimental results show that TaxoLine produces high precision and recall for both concept and relation extraction than well-known ontology learning algorithms. Furthermore, it also shows promising results in terms of execution time needed to build the domain taxonomy.

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Ontology Learning from Text: Tasks and Challenges for Machine Learning (Extended Abstract)

Intelligent Information Processing and Web Mining ◽

10.1007/978-3-540-36562-4_48 ◽

2003 ◽

pp. 453-455

Author(s):

Jörg-Uwe Kietz

Keyword(s):

Machine Learning ◽

Ontology Learning ◽

Learning From Text

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Self-Supervised Chinese Ontology Learning from Online Encyclopedias

The Scientific World JOURNAL ◽

10.1155/2014/848631 ◽

2014 ◽

Vol 2014 ◽

pp. 1-13 ◽

Cited By ~ 6

Author(s):

Fanghuai Hu ◽

Zhiqing Shao ◽

Tong Ruan

Keyword(s):

Machine Learning ◽

Structural Information ◽

Relation Extraction ◽

Knowledge Bases ◽

The Self ◽

Supervised Machine Learning ◽

Ontology Learning ◽

High Coverage ◽

Category Labels ◽

Training Examples

Constructing ontology manually is a time-consuming, error-prone, and tedious task. We present SSCO, a self-supervised learning based chinese ontology, which contains about 255 thousand concepts, 5 million entities, and 40 million facts. We explore the three largest online Chinese encyclopedias for ontology learning and describe how to transfer the structured knowledge in encyclopedias, including article titles, category labels, redirection pages, taxonomy systems, and InfoBox modules, into ontological form. In order to avoid the errors in encyclopedias and enrich the learnt ontology, we also apply some machine learning based methods. First, we proof that the self-supervised machine learning method is practicable in Chinese relation extraction (at least for synonymy and hyponymy) statistically and experimentally and train some self-supervised models (SVMs and CRFs) for synonymy extraction, concept-subconcept relation extraction, and concept-instance relation extraction; the advantages of our methods are that all training examples are automatically generated from the structural information of encyclopedias and a few general heuristic rules. Finally, we evaluate SSCO in two aspects, scale and precision; manual evaluation results show that the ontology has excellent precision, and high coverage is concluded by comparing SSCO with other famous ontologies and knowledge bases; the experiment results also indicate that the self-supervised models obviously enrich SSCO.

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