FCA-Based Ontology Learning from Unstructured Textual Data

被引：4

作者：

Jabbari, Simin ^{[1
,2
]}

Stoffel, Kilian ^{[1
]}

机构：

[1] Univ Neuchatel, Neuchatel, Switzerland

[2] F Hoffmann La Roche Ltd, Diagnost Data Sci Lab, Basel, Switzerland

来源：

MINING INTELLIGENCE AND KNOWLEDGE EXPLORATION, MIKE 2018 | 2018年 / 11308卷

关键词：

Ontology engineering; Semantic knowledge extraction; Formal concept analysis; Natural language processing; Concept lattice; ALGORITHM;

D O I：

10.1007/978-3-030-05918-7_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

Ontologies have been frequently used for representing domain knowledge. They have lots of applications in semantic knowledge extraction. However, learning ontologies especially from unstructured data is a difficult yet an interesting challenge. In this paper, we introduce a pipeline for learning ontology from a text corpus in a semi-automated fashion using Natural Language Processing (NLP) and Formal Concept Analysis (FCA). We apply our proposed method on a small given corpus that consists of some news documents in IT and pharmaceutical domain. We then discuss the potential applications of the proposed model and ideas on how to improve it even further.

引用

页码：1 / 10

页数：10

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