English
 
Help Privacy Policy Disclaimer
  Advanced SearchBrowse

Item

ITEM ACTIONSEXPORT
 
 
DownloadE-Mail
  Knowledge Questions from Knowledge Graphs

Seyler, D., Yahya, M., & Berberich, K. (2016). Knowledge Questions from Knowledge Graphs. Retrieved from http://arxiv.org/abs/1610.09935.

Item is

Files

show Files
hide Files
:
arXiv:1610.09935.pdf (Preprint), 593KB
Name:
arXiv:1610.09935.pdf
Description:
File downloaded from arXiv at 2016-12-12 08:50
OA-Status:
Visibility:
Public
MIME-Type / Checksum:
application/pdf / [MD5]
Technical Metadata:
Copyright Date:
-
Copyright Info:
-

Locators

show

Creators

show
hide
 Creators:
Seyler, Dominic1, Author           
Yahya, Mohamed2, Author           
Berberich, Klaus2, Author           
Affiliations:
1External Organizations, ou_persistent22              
2Databases and Information Systems, MPI for Informatics, Max Planck Society, ou_24018              

Content

show
hide
Free keywords: Computer Science, Computation and Language, cs.CL
 Abstract: We address the novel problem of automatically generating quiz-style knowledge questions from a knowledge graph such as DBpedia. Questions of this kind have ample applications, for instance, to educate users about or to evaluate their knowledge in a specific domain. To solve the problem, we propose an end-to-end approach. The approach first selects a named entity from the knowledge graph as an answer. It then generates a structured triple-pattern query, which yields the answer as its sole result. If a multiple-choice question is desired, the approach selects alternative answer options. Finally, our approach uses a template-based method to verbalize the structured query and yield a natural language question. A key challenge is estimating how difficult the generated question is to human users. To do this, we make use of historical data from the Jeopardy! quiz show and a semantically annotated Web-scale document collection, engineer suitable features, and train a logistic regression classifier to predict question difficulty. Experiments demonstrate the viability of our overall approach.

Details

show
hide
Language(s): eng - English
 Dates: 2016-10-312016-11-012016
 Publication Status: Published online
 Pages: 9 p.
 Publishing info: -
 Table of Contents: -
 Rev. Type: -
 Identifiers: arXiv: 1610.09935
URI: http://arxiv.org/abs/1610.09935
BibTex Citekey: Seyler1610.09935
 Degree: -

Event

show

Legal Case

show

Project information

show

Source

show