• Publications
  • Influence
RDF2Vec: RDF Graph Embeddings for Data Mining
TLDR
We present RDF2Vec, an approach that uses language modeling approaches for unsupervised feature extraction from sequences of words, and adapts them to RDF graphs. Expand
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Adoption of the Linked Data Best Practices in Different Topical Domains
TLDR
This paper revisited and updated the finding of the State of the LOD Cloud report [7] from 2011 based on a Linked Data crawl gathered in April 2014. Expand
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Knowledge graph refinement: A survey of approaches and evaluation methods
TLDR
We provide a survey of such knowledge graph refinement approaches, with a dual look at both the methods being proposed as well as the evaluation methodologies used. Expand
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Type Inference on Noisy RDF Data
TLDR
We propose the heuristic link-based type inference mechanism SDType, which can handle noisy and incorrect data. Expand
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A Multi-Indicator Approach for Geolocalization of Tweets
TLDR
We propose the first multi-indicator method for determining (1)The location where a tweet was created as well as (2) the location of the user's residence. Expand
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I See a Car Crash: Real-Time Detection of Small Scale Incidents in Microblogs
TLDR
This paper contributes an approach that leverages information provided in microblogs for detection of small scale incidents. Expand
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RDF2Vec: RDF graph embeddings and their applications
TLDR
We generate sequences by leveraging local information from graph sub-structures, harvested by Weisfeiler-Lehman Subtree RDF Graph Kernels and graph walks, and learn latent numerical representations of entities in RDF graphs. Expand
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Improving the Quality of Linked Data Using Statistical Distributions
TLDR
We present two algorithms that exploit statistical distributions of properties and types for enhancing the quality of incomplete and noisy Linked Data sets: SDType adds missing type statements, and SDValidate identifies faulty statements. Expand
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Semantic Web in data mining and knowledge discovery: A comprehensive survey
TLDR
Data Mining and Knowledge Discovery in Databases (KDD) is a research field concerned with deriving higher-level insights from data. Expand
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Global RDF Vector Space Embeddings
TLDR
Vector space embeddings have been shown to perform well when using RDF data in data mining and machine learning tasks, and show that the results are competitive with traditional local techniques like RDF2Vec. Expand
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