The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. ex. Some numerals are expressed as "XNUMX".
Copyrights notice
The original paper is in English. Non-English content has been machine-translated and may contain typographical errors or mistranslations. Copyrights notice
Kami membentangkan kaedah baharu untuk mencari pengetahuan daripada data berstruktur yang diwakili oleh graf dalam rangka kerja Pengaturcaraan Logik Induktif. Graf, atau rangkaian, digunakan secara meluas untuk mewakili hubungan antara pelbagai data dan menyatakan hipotesis yang kecil dan mudah difahami. Sistem analisis yang memanipulasi graf secara langsung berguna untuk penemuan pengetahuan. Kaedah kami menggunakan Sistem Graf Formal (FGS) sebagai bahasa perwakilan pengetahuan untuk data berstruktur graf. FGS ialah sejenis sistem pengaturcaraan logik yang secara langsung berurusan dengan graf seperti istilah tertib pertama. Dan kaedah kami menggunakan algoritma inferens induktif yang boleh disangkal sebagai algoritma pembelajaran. Algoritma inferens induktif yang boleh disangkal ialah sejenis algoritma inferens induktif khas dengan ruang hipotesis yang boleh disangkal dan sesuai untuk penemuan pengetahuan. Kami memberikan ruang hipotesis yang cukup besar, set program FGS yang mengurangkan lemah. Dan kami menunjukkan bahawa ruang hipotesis ini boleh disangkal daripada data lengkap. Kami telah mereka bentuk dan melaksanakan prototaip sistem penemuan pengetahuan KD-FGS, yang berdasarkan kaedah kami dan memperoleh pengetahuan secara langsung daripada data berstruktur graf. Akhir sekali kami membincangkan kebolehgunaan kaedah kami untuk data berstruktur graf dengan keputusan percubaan pada beberapa tanggapan teori graf.
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Salinan
Tetsuhiro MIYAHARA, Tomoyuki UCHIDA, Takayoshi SHOUDAI, Tetsuji KUBOYAMA, Kenichi TAKAHASHI, Hiroaki UEDA, "Discovering Knowledge from Graph Structured Data by Using Refutably Inductive Inference of Formal Graph Systems" in IEICE TRANSACTIONS on Information,
vol. E84-D, no. 1, pp. 48-56, January 2001, doi: .
Abstract: We present a new method for discovering knowledge from structured data which are represented by graphs in the framework of Inductive Logic Programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. The analyzing system directly manipulating graphs is useful for knowledge discovery. Our method uses Formal Graph System (FGS) as a knowledge representation language for graph structured data. FGS is a kind of logic programming system which directly deals with graphs just like first order terms. And our method employs a refutably inductive inference algorithm as a learning algorithm. A refutably inductive inference algorithm is a special type of inductive inference algorithm with refutability of hypothesis spaces, and is suitable for knowledge discovery. We give a sufficiently large hypothesis space, the set of weakly reducing FGS programs. And we show that this hypothesis space is refutably inferable from complete data. We have designed and implemented a prototype of a knowledge discovery system KD-FGS, which is based on our method and acquires knowledge directly from graph structured data. Finally we discuss the applicability of our method for graph structured data with experimental results on some graph theoretical notions.
URL: https://global.ieice.org/en_transactions/information/10.1587/e84-d_1_48/_p
Salinan
@ARTICLE{e84-d_1_48,
author={Tetsuhiro MIYAHARA, Tomoyuki UCHIDA, Takayoshi SHOUDAI, Tetsuji KUBOYAMA, Kenichi TAKAHASHI, Hiroaki UEDA, },
journal={IEICE TRANSACTIONS on Information},
title={Discovering Knowledge from Graph Structured Data by Using Refutably Inductive Inference of Formal Graph Systems},
year={2001},
volume={E84-D},
number={1},
pages={48-56},
abstract={We present a new method for discovering knowledge from structured data which are represented by graphs in the framework of Inductive Logic Programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. The analyzing system directly manipulating graphs is useful for knowledge discovery. Our method uses Formal Graph System (FGS) as a knowledge representation language for graph structured data. FGS is a kind of logic programming system which directly deals with graphs just like first order terms. And our method employs a refutably inductive inference algorithm as a learning algorithm. A refutably inductive inference algorithm is a special type of inductive inference algorithm with refutability of hypothesis spaces, and is suitable for knowledge discovery. We give a sufficiently large hypothesis space, the set of weakly reducing FGS programs. And we show that this hypothesis space is refutably inferable from complete data. We have designed and implemented a prototype of a knowledge discovery system KD-FGS, which is based on our method and acquires knowledge directly from graph structured data. Finally we discuss the applicability of our method for graph structured data with experimental results on some graph theoretical notions.},
keywords={},
doi={},
ISSN={},
month={January},}
Salinan
TY - JOUR
TI - Discovering Knowledge from Graph Structured Data by Using Refutably Inductive Inference of Formal Graph Systems
T2 - IEICE TRANSACTIONS on Information
SP - 48
EP - 56
AU - Tetsuhiro MIYAHARA
AU - Tomoyuki UCHIDA
AU - Takayoshi SHOUDAI
AU - Tetsuji KUBOYAMA
AU - Kenichi TAKAHASHI
AU - Hiroaki UEDA
PY - 2001
DO -
JO - IEICE TRANSACTIONS on Information
SN -
VL - E84-D
IS - 1
JA - IEICE TRANSACTIONS on Information
Y1 - January 2001
AB - We present a new method for discovering knowledge from structured data which are represented by graphs in the framework of Inductive Logic Programming. A graph, or network, is widely used for representing relations between various data and expressing a small and easily understandable hypothesis. The analyzing system directly manipulating graphs is useful for knowledge discovery. Our method uses Formal Graph System (FGS) as a knowledge representation language for graph structured data. FGS is a kind of logic programming system which directly deals with graphs just like first order terms. And our method employs a refutably inductive inference algorithm as a learning algorithm. A refutably inductive inference algorithm is a special type of inductive inference algorithm with refutability of hypothesis spaces, and is suitable for knowledge discovery. We give a sufficiently large hypothesis space, the set of weakly reducing FGS programs. And we show that this hypothesis space is refutably inferable from complete data. We have designed and implemented a prototype of a knowledge discovery system KD-FGS, which is based on our method and acquires knowledge directly from graph structured data. Finally we discuss the applicability of our method for graph structured data with experimental results on some graph theoretical notions.
ER -