activity
20202022
most citedRDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings

4 citations · 5 across the 3 of their papers we have counts for

collaborators

5 papers

cs.AI20221 cited

Ontology Matching Through Absolute Orientation of Embedding Spaces

Jan Portisch, Guilherme Costa, Karolin Stefani +3

Ontology matching is a core task when creating interoperable and linked open datasets. In this paper, we explore a novel structure-based mapping approach which is based on knowledg…

cs.DB2021

Background Knowledge in Schema Matching: Strategy vs. Data

Jan Portisch, Michael Hladik, Heiko Paulheim

The use of external background knowledge can be beneficial for the task of matching schemas or ontologies automatically. In this paper, we exploit six general-purpose knowledge gra…

cs.LG2021

FinMatcher at FinSim-2: Hypernym Detection in the Financial Services Domain using Knowledge Graphs

Jan Portisch, Michael Hladik, Heiko Paulheim

This paper presents the FinMatcher system and its results for the FinSim 2021 shared task which is co-located with the Workshop on Financial Technology on the Web (FinWeb) in conju…

cs.AI20204 cited

RDF2Vec Light -- A Lightweight Approach for Knowledge Graph Embeddings

Jan Portisch, Michael Hladik, Heiko Paulheim

Knowledge graph embedding approaches represent nodes and edges of graphs as mathematical vectors. Current approaches focus on embedding complete knowledge graphs, i.e. all nodes an…

cs.CL2020

KGvec2go -- Knowledge Graph Embeddings as a Service

Jan Portisch, Michael Hladik, Heiko Paulheim

In this paper, we present KGvec2go, a Web API for accessing and consuming graph embeddings in a light-weight fashion in downstream applications. Currently, we serve pre-trained emb…