Change search
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf
Creating Knowledge Graphs using Distributional Semantic Models
KTH, School of Computer Science and Communication (CSC).
2016 (English)Independent thesis Advanced level (degree of Master (Two Years)), 20 credits / 30 HE creditsStudent thesis
Abstract [en]

This report researches a method for creating knowledge graphs, a specific way of structuring information, using distributional semantic models. Two different algorithms for selecting graph edges and two different algorithms for labelling edges are tried, and variations of those are evaluated. We perform experiments comparing our knowledge graphs with existing manually constructed knowledge graphs of high quality, with respect to graph structure and edge labels. We find that the algorithms usually produces graphs with a structure similar to that of manually constructed knowledge graphs, as long as the data set is sufficiently large and general, and that the similarity of edge labels to manually chosen edge labels vary widely depending on input.

Place, publisher, year, edition, pages
2016.
National Category
Language Technology (Computational Linguistics)
Identifiers
URN: urn:nbn:se:kth:diva-199702OAI: oai:DiVA.org:kth-199702DiVA: diva2:1065299
External cooperation
Gavagai
Subject / course
Computer Science
Educational program
Master of Science in Engineering - Computer Science and Technology
Supervisors
Examiners
Available from: 2017-02-02 Created: 2017-01-14 Last updated: 2018-01-13Bibliographically approved

Open Access in DiVA

fulltext(735 kB)72 downloads
File information
File name FULLTEXT01.pdfFile size 735 kBChecksum SHA-512
073e8be676b6528843992866c1b54e4800d50b211134d391a0455a282e396440d3e96799a0fde7814ec1044b8dd4082b9e8ca101872f1d7ef7f42b4692a60cb2
Type fulltextMimetype application/pdf

By organisation
School of Computer Science and Communication (CSC)
Language Technology (Computational Linguistics)

Search outside of DiVA

GoogleGoogle Scholar
Total: 72 downloads
The number of downloads is the sum of all downloads of full texts. It may include eg previous versions that are now no longer available

urn-nbn

Altmetric score

urn-nbn
Total: 113 hits
CiteExportLink to record
Permanent link

Direct link
Cite
Citation style
  • apa
  • ieee
  • modern-language-association-8th-edition
  • vancouver
  • Other style
More styles
Language
  • de-DE
  • en-GB
  • en-US
  • fi-FI
  • nn-NO
  • nn-NB
  • sv-SE
  • Other locale
More languages
Output format
  • html
  • text
  • asciidoc
  • rtf