Hit papers significantly outperform the citation benchmark for their cohort. A paper qualifies
if it has ≥500 total citations, achieves ≥1.5× the top-1% citation threshold for papers in the
same subfield and year (this is the minimum needed to enter the top 1%, not the average
within it), or reaches the top citation threshold in at least one of its specific research
topics.
Learning Sequence Encoders for Temporal Knowledge Graph Completion
2018289 citationsAlberto García-Durán, Sebastijan Dumančić et al.profile →
Peers — A (Enhanced Table)
Peers by citation overlap · career bar shows stage (early→late)
cites ·
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Countries citing papers authored by Mathias Niepert
Since
Specialization
Citations
This map shows the geographic impact of Mathias Niepert's research. It shows the number of citations coming from papers published by authors working in each country. You can also color the map by specialization and compare the number of citations received by Mathias Niepert with the expected number of citations based on a country's size and research output (numbers larger than one mean the country cites Mathias Niepert more than expected).
This network shows the impact of papers produced by Mathias Niepert. Nodes represent research fields, and links connect fields that are likely to share authors. Colored nodes show fields that tend to cite the papers produced by Mathias Niepert. The network helps show where Mathias Niepert may publish in the future.
Co-authorship network of co-authors of Mathias Niepert
This figure shows the co-authorship network connecting the top 25 collaborators of Mathias Niepert.
A scholar is included among the top collaborators of Mathias Niepert based on the total number of
citations received by their joint publications. Widths of edges
represent the number of papers authors have co-authored together.
Node borders
signify the number of papers an author published with Mathias Niepert. Mathias Niepert is excluded from
the visualization to improve readability, since they are connected to all nodes in the network.
Niepert, Mathias, et al.. (2019). Answering Visual-Relational Queries in Web-Extracted Knowledge Graphs.8 indexed citations
6.
Wang, Cheng & Mathias Niepert. (2019). State-Regularized Recurrent Neural Networks. International Conference on Machine Learning. 6596–6606.5 indexed citations
7.
Dumančić, Sebastijan, Alberto García-Durán, & Mathias Niepert. (2018). On embeddings as an alternative paradigm for relational learning.. arXiv (Cornell University).1 indexed citations
Niepert, Mathias, et al.. (2017). Learning Graph Representations with Embedding Propagation. Neural Information Processing Systems. 30. 5119–5130.33 indexed citations
10.
Niepert, Mathias, et al.. (2017). Learning Graph Embeddings with Embedding Propagation. Neural Information Processing Systems. 5123–5134.1 indexed citations
11.
Niepert, Mathias & Pedro Domingos. (2015). Learning and inference in tractable probabilistic knowledge bases. Uncertainty in Artificial Intelligence. 632–641.2 indexed citations
12.
Noessner, Jan, Heiner Stuckenschmidt, Christian Meilicke, & Mathias Niepert. (2014). Completeness and optimality in ontology alignment debugging. MADOC (University of Mannheim). 25–36.6 indexed citations
13.
Niepert, Mathias & Pedro Domingos. (2014). Tractable probabilistic knowledge bases: Wikipedia and beyond. National Conference on Artificial Intelligence. 69–75.3 indexed citations
Niepert, Mathias, Christian Meilicke, & Heiner Stuckenschmidt. (2012). Towards Distributed MCMC Inference in Probabilistic Knowledge Bases. MADOC (University of Mannheim). 1–6.2 indexed citations
16.
Helaoui, Rim, Daniele Riboni, Mathias Niepert, Cláudio Bettini, & Heiner Stuckenschmidt. (2012). Towards Activity Recognition Using Probabilistic Description Logics. MADOC (University of Mannheim). 26–31.10 indexed citations
17.
Niepert, Mathias, et al.. (2011). Fine-Grained Sentiment Analysis with Structural Features. MADOC (University of Mannheim). 336–344.60 indexed citations
18.
Noessner, Jan & Mathias Niepert. (2010). CODI: combinatorial optimization for data integration - results for OAEI 2010. MADOC (University of Mannheim). 142–149.16 indexed citations
Niepert, Mathias, Cameron Buckner, & Colin Allen. (2008). Answer Set Programming on Expert Feedback to Populate and Extend Dynamic Ontologies. The Florida AI Research Society. 500–505.7 indexed citations
Rankless uses publication and citation data sourced from OpenAlex, an open and comprehensive
bibliographic database. While OpenAlex provides broad and valuable coverage of the global
research landscape, it—like all bibliographic datasets—has inherent limitations. These include
incomplete records, variations in author disambiguation, differences in journal indexing, and
delays in data updates. As a result, some metrics and network relationships displayed in
Rankless may not fully capture the entirety of a scholar's output or impact.