70 citations · 273 across the 44 of their papers we have counts for
6 papers · 1 filter
ExeKGLib: Knowledge Graphs-Empowered Machine Learning Analytics
Antonis Klironomos, Baifan Zhou, Zhipeng Tan +4
Many machine learning (ML) libraries are accessible online for ML practitioners. Typical ML pipelines are complex and consist of a series of steps, each of them invoking several ML…
Walk this Way! Entity Walks and Property Walks for RDF2vec
Jan Portisch, Heiko Paulheim
RDF2vec is a knowledge graph embedding mechanism which first extracts sequences from knowledge graphs by performing random walks, then feeds those into the word embedding algorithm…
Putting RDF2vec in Order
Jan Portisch, Heiko Paulheim
The RDF2vec method for creating node embeddings on knowledge graphs is based on word2vec, which, in turn, is agnostic towards the position of context words. In this paper, we argue…
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…
Walk Extraction Strategies for Node Embeddings with RDF2Vec in Knowledge Graphs
Gilles Vandewiele, Bram Steenwinckel, Pieter Bonte +5
As KGs are symbolic constructs, specialized techniques have to be applied in order to make them compatible with data mining techniques. RDF2Vec is an unsupervised technique that ca…
On Cognitive Preferences and the Plausibility of Rule-based Models
Johannes Fürnkranz, Tomáš Kliegr, Heiko Paulheim
It is conventional wisdom in machine learning and data mining that logical models such as rule sets are more interpretable than other models, and that among such rule-based models,…