Ekstraksi Informasi Halaman Web Menggunakan Pendekatan Bootstrapping pada Ontology-Based Information Extraction

AbstrakEkstraksi  informasi  merupakan suatu bidang ilmu untuk pengolahan bahasa alami, dengan cara mengubah teks tidak terstruktur menjadi informasi dalam bentuk terstruktur. Berbagai jenis informasi di Internet ditransmisikan secara tidak terstruktur melalui website, menyebabkan munculnya kebutuhan akan suatu teknologi untuk menganalisa teks dan menemukan pengetahuan yang relevan dalam bentuk informasi terstruktur. Contoh informasi tidak terstruktur adalah informasi utama yang ada pada konten halaman web. Bermacam pendekatan untuk ekstraksi informasi telah dikembangkan oleh berbagai peneliti, baik menggunakan metode manual atau otomatis, namun masih perlu ditingkatkan kinerjanya terkait akurasi dan kecepatan ekstraksi. Pada penelitian ini diusulkan suatu penerapan pendekatan ekstraksi informasi dengan mengkombinasikan pendekatan bootstrapping dengan Ontology-based Information Extraction (OBIE). Pendekatan bootstrapping dengan menggunakan sedikit contoh data berlabel, digunakan untuk memimalkan keterlibatan manusia dalam proses ekstraksi informasi, sedangkan penggunakan panduan ontologi untuk mengekstraksi classes (kelas), properties dan instance digunakan untuk menyediakan konten semantik untuk web semantik. Pengkombinasian kedua pendekatan tersebut diharapkan dapat meningkatan kecepatan proses ekstraksi dan akurasi hasil ekstraksi. Studi kasus untuk penerapan sistem ekstraksi informasi menggunakan dataset “LonelyPlanet”. Kata kunci—Ekstraksi informasi, ontologi, bootstrapping, Ontology-Based Information Extraction, OBIE, kinerja Abstract Information extraction is a field study of natural language processing by converting unstructured text into structured information. Several types of information on the Internet is transmitted through unstructured information via websites, led to emergence of the need a technology to analyze text and found relevant knowledge into structured information. For example of unstructured information is existing main information on the content of web pages. Various approaches  for information extraction have been developed by many researchers, either using manual or automatic method, but still need to be improved performance related accuracy and speed of extraction. This research proposed an approach of information extraction that combines bootstrapping approach with Ontology-Based Information Extraction (OBIE). Bootstrapping approach using small seed of labelled data, is used to minimize human intervention on information extraction process, while the use of guide ontology for extracting classes, properties and instances, using for provide semantic content for semantic web. Combining both approaches expected to increase speed of extraction process and accuracy of extraction results. Case study to apply information extraction system using “LonelyPlanet” datasets. Keywords— Information extraction, ontology, bootstrapping, Ontology-Based Information Extraction, OBIE, performanc

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An important problem for many kinds of knowledge systems is their initial set-up. It is difficult to choose the right information to include in such systems, and the right information is also a prerequisite for maximizing the uptake and relevance. To tackle this problem, most developers adopt heavyweight solutions and rely on a faithful continuous interaction with users to create and improve content. In this paper, we explore the use of an automatic, lightweight ontology-based solution to the bootstrapping problem, in which domain-describing ontologies are analysed to uncover significant yet implicit relationships between instances. We illustrate the approach by using such an analysis to provide content automatically for the initial set-up of an organizational memory

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Using different sources of information to support automated extracting of relations between biomedical concepts contributes to the development of our understanding of biological systems. The primary comprehensive source of these relations is biomedical literature. Several relation extraction approaches have been proposed to identify relations between concepts in biomedical literature, namely, using neural networks algorithms. The use of multichannel architectures composed of multiple data representations, as in deep neural networks, is leading to state-of-the-art results. The right combination of data representations can eventually lead us to even higher evaluation scores in relation extraction tasks. Thus, biomedical ontologies play a fundamental role by providing semantic and ancestry information about an entity. The incorporation of biomedical ontologies has already been proved to enhance previous state-of-the-art results.Comment: Artificial Neural Networks book (Springer) - Chapter 1

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