Graph Embeddings for Enrichment of Historical Data

J. Baas; A.J. Feelders; M.M. Dastani

Graph Embeddings for Enrichment of Historical Data

Sub Algorithmic Data Analysis

Research output: Contribution to conference › Poster › Academic

Abstract

In this work-in-progress paper we describe our method of combining expert knowledge and RDF graph embeddings to solve for specific downstream tasks such as entity resolution. We show that efficiency gains can be made by choosing the correct gradient descent algorithm and that expert input can lead to the desired results.

Original language	English
Number of pages	14
Publication status	Published - 16 Sept 2019
Event	Workshop on Graph Embedding and Data Mining(GEM) 2019 - Hubland campus of the University of Würzburg, Würzburg, Germany Duration: 16 Sept 2019 → 20 Sept 2019 https://gem-ecmlpkdd.github.io

Workshop

Workshop	Workshop on Graph Embedding and Data Mining(GEM) 2019
Abbreviated title	GEM
Country/Territory	Germany
City	Würzburg
Period	16/09/19 → 20/09/19
Internet address	https://gem-ecmlpkdd.github.io

Keywords

RDF
GloVe
Digital Humantities
Graph Embedding
entity resolution

Access to Document

ECML_PKDD_GEM_2019Final published version, 451 KBLicence: Unspecified
ECML_PKDD_GEM_2019_PosterFinal published version, 185 KB

Cite this

@conference{cbac971256534af685378d490d2eab38,

title = "Graph Embeddings for Enrichment of Historical Data",

abstract = "In this work-in-progress paper we describe our method of combining expert knowledge and RDF graph embeddings to solve for specific downstream tasks such as entity resolution. We show that efficiency gains can be made by choosing the correct gradient descent algorithm and that expert input can lead to the desired results.",

keywords = "RDF, GloVe, Digital Humantities, Graph Embedding, entity resolution",

author = "J. Baas and A.J. Feelders and M.M. Dastani",

year = "2019",

month = sep,

day = "16",

language = "English",

note = "Workshop on Graph Embedding and Data Mining(GEM) 2019, GEM ; Conference date: 16-09-2019 Through 20-09-2019",

url = "https://gem-ecmlpkdd.github.io",

}

TY - CONF

T1 - Graph Embeddings for Enrichment of Historical Data

AU - Baas, J.

AU - Feelders, A.J.

AU - Dastani, M.M.

PY - 2019/9/16

Y1 - 2019/9/16

N2 - In this work-in-progress paper we describe our method of combining expert knowledge and RDF graph embeddings to solve for specific downstream tasks such as entity resolution. We show that efficiency gains can be made by choosing the correct gradient descent algorithm and that expert input can lead to the desired results.

AB - In this work-in-progress paper we describe our method of combining expert knowledge and RDF graph embeddings to solve for specific downstream tasks such as entity resolution. We show that efficiency gains can be made by choosing the correct gradient descent algorithm and that expert input can lead to the desired results.

KW - RDF

KW - GloVe

KW - Digital Humantities

KW - Graph Embedding

KW - entity resolution

M3 - Poster

T2 - Workshop on Graph Embedding and Data Mining(GEM) 2019

Y2 - 16 September 2019 through 20 September 2019

ER -

Graph Embeddings for Enrichment of Historical Data

Abstract

Workshop

Keywords

Access to Document

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Cite this