Open Knowledge Extraction Challenge

被引：25

作者：

Nuzzolese, Andrea Giovanni ^{[1
]}

Gentile, Anna Lisa ^{[2
]}

Presutti, Valentina ^{[1
]}

Gangemi, Aldo ^{[1
]}

Garigliotti, Dario ^{[3
]}

Navigli, Roberto ^{[3
]}

机构：

[1] CNR, ISTC, Semant Technol Lab, Rome, Italy

[2] Univ Sheffield, Dept Comp Sci, Sheffield S10 2TN, S Yorkshire, England

[3] Univ Roma La Sapienza, Dept Comp Sci, I-00185 Rome, Italy

来源：

SEMANTIC WEB EVALUATION CHALLENGES | 2015年 / 548卷

基金：

英国工程与自然科学研究理事会;

关键词：

D O I：

10.1007/978-3-319-25518-7_1

中图分类号：

TP18 [人工智能理论];

学科分类号：

081104 ; 0812 ; 0835 ; 1405 ;

摘要：

The Open Knowledge Extraction (OKE) challenge is aimed at promoting research in the automatic extraction of structured content from textual data and its representation and publication as Linked Data. We designed two extraction tasks: (1) Entity Recognition, Linking and Typing and (2) Class Induction and entity typing. The challenge saw the participations of four systems: CETUS-FOX and FRED participating to both tasks, Adel participating to Task 1 and OAK@Sheffield participating to Task 2. In this paper we describe the OKE challenge, the tasks, the datasets used for training and evaluating the systems, the evaluation method, and obtained results.

引用

页码：3 / 15

页数：13

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