Abstract graphs, abstract paths, grounded paths for Freebase and NELL (ICPSR doi:10.11588/data/AVLFPZ)

View:

Part 1: Document Description
Part 2: Study Description
Part 5: Other Study-Related Materials
Entire Codebook

Document Description

Citation

Title:

Abstract graphs, abstract paths, grounded paths for Freebase and NELL

Identification Number:

doi:10.11588/data/AVLFPZ

Distributor:

heiDATA

Date of Distribution:

2019-07-15

Version:

1

Bibliographic Citation:

Nastase, Vivi; Kotnis, Bhushan, 2019, "Abstract graphs, abstract paths, grounded paths for Freebase and NELL", https://doi.org/10.11588/data/AVLFPZ, heiDATA, V1

Study Description

Citation

Title:

Abstract graphs, abstract paths, grounded paths for Freebase and NELL

Subtitle:

A compact representation of typed knowledge graphs and patterns in them

Identification Number:

doi:10.11588/data/AVLFPZ

Authoring Entity:

Nastase, Vivi (Department of Computational Linguistics, Heidelberg University, Germany)

Kotnis, Bhushan (Department of Computational Linguistics, Heidelberg University, Germany (2016-2018), NEC Laboratories Europe GmbH (since 2018))

Date of Production:

2019

Distributor:

heiDATA

Date of Distribution:

2019-07-15

Study Scope

Keywords:

Arts and Humanities, Computer and Information Science, knowledge graph, abstract graph, link prediction, targeted information extraction

Topic Classification:

knowledge discovery in knowledge graphs

Abstract:

<p>We describe a method for representing knowledge graphs that capture an intensional representation of the original extensional information. This representation is very compact, and it abstracts away from individual links, allowing us to find better path candidates, as shown by the results of link prediction using this information.</p> <p>The data in this archive consists of the abstract graphs built from several configurations of Freebase and NELL (used in the experiments described in Gardner et al. 2014), the abstract paths extracted from these graphs, the grounded paths and the negative sampling used in the experiments described in (Nastase and Kotnis, 2019). The motivation for this work was to find better patterns in knowledge graphs than those obtained using the PRA approach.</p> <p>This dataset contains:</p> <ul> <li>&nbsp;AbstractGraphs_Garder2014data.tar.gz: the abstract graphs</li> <li>AbstractPathsGroundedPaths.tar.gz: the abstract paths, grounded paths, train/test data and the corresponding negative samples (for each abstract graph)</li> </ul> <p>For a brief description of the data in each archive, please see README_AbstractGraphs.txt. </p>

Kind of Data:

text files, tab separated values

Methodology and Processing

Other Study-Related Materials

Label:

AbstractGraphs_Garder2014data.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

AbstractPathsGroundedPaths.tar.gz

Notes:

application/gzip

Other Study-Related Materials

Label:

README_AbstractGraphs.txt

Notes:

text/plain