Learning concept hierarchies from textual resources for ontologies construction

Ríos-Alvarado, Ana B.; López-Arévalo, Iván; Sosa-Sosa, Víctor J.

doi:10.1016/j.eswa.2013.05.005

Cited by 47 publications

(33 citation statements)

References 10 publications

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“…Dataset ini juga telah digunakan sebagai benchmark pada penelitian [10]. Motivasi pemilihan dataset tersebut sebagai benchmark karena penggunaan dataset secara umum digunakan pada penelitian ekstraksi informasi sehingga hasil dari metode yang digunakan dapat secara langsung dibandingkan dengan hasil dari metode lain.…”

Section: Evaluasi Pengujianunclassified

“…Tabel 1 Perbandingan beberapa sistem ekstraksi informasi Evaluasi Kinerja (%) Penelitian [11] Perbandingan dengan penelitian [10] memperlihatkan bahwa nilai precision pada penelitian ini lebih tinggi dari pada nilai recall, sebaliknya pada penelitian [10] nilai recall-nya lebih tinggi daripada nilai precision. Hasil kinerja secara keseluruhan dapat dilihat pada nilai Fmeasure.…”

Section: Gambar 7 Hasil Populasi Ontologiunclassified

“…Hasil kinerja secara keseluruhan dapat dilihat pada nilai Fmeasure. Hasil perbandingan menunjukkan bahwa sistem OBIE memiliki kecenderungan yang meningkat bila dibandingkan dengan penelitian [11], namun cenderung menurun bila dibandingkan dengan penelitian [10]. Kecepatan rata-rata sistem OBIE menunjukkan waktu pemrosesan yang tidak jauh berbeda bila dibandingkan dengan sistem ANNIE.…”

Section: Gambar 7 Hasil Populasi Ontologiunclassified

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

Susanti¹,

Mustofa²

2015

Indonesian J. Comput. Cybern. Syst.

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Abstrak 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.

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Section: Evaluasi Pengujianunclassified

Section: Gambar 7 Hasil Populasi Ontologiunclassified

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

Susanti¹,

Mustofa²

2015

Indonesian J. Comput. Cybern. Syst.

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“…For reaching this goal, the approach is divided into two main phases: 1) extract hierarchical relations (taxonomy structure) and 2) extract axioms from text. For the first task, it has been considered the approach proposed in [9] where then main feature is the extraction of vocabulary from text, which is organized in a taxonomic structure. In particular, taxonomic relations are based on the identification of hypernymy/hyponymy relations 2 and.…”

Section: Introductionmentioning

confidence: 99%

“…These tasks are able to get the vocabulary of a domain and the relationships between concepts. Even though a large number of methods for ontology learning from text have been proposed, such methods are only concerned with the extraction of some elements such as the hierarchical structure [7][8][9], non-taxonomic relations [10,11], or axioms [12][13][14] in an independent manner.…”

Section: Introductionmentioning

confidence: 99%

An Approach for Learning Expressive Ontologies in Medical Domain

et al. 2015

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The access to medical information (journals, blogs, web-pages, dictionaries, and texts) has been increased due to availability of many digital media. In particular, finding an appropriate structure that represents the information contained in texts is not a trivial task. One of the structures for modeling the knowledge are ontologies. An ontology refers to a conceptualization of a specific domain of knowledge. Ontologies are especially useful because they support the exchange and sharing of information as well as reasoning tasks. The usage of ontologies in medicine is mainly focussed in the representation and organization of medical terminologies. Ontology learning techniques have emerged as a set of techniques to get ontologies from unstructured information. This paper describes a new ontology learning approach that consists of a method for the acquisition of concepts and its corresponding taxonomic relations, where also axioms disjointWith and equivalentClass are learned from text without human intervention. The source of knowledge involves files about medical domain. Our approach is divided into two stages, the first part corresponds to discover hierarchical relations and the second part to the axiom extraction. Our automatic ontology learning approach shows better results compared against previous work, giving rise to more expressive ontologies.

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