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Max Berrendorf

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4 papers

2 author rows

IJCAI Conference 2022 Conference Paper

Improving Inductive Link Prediction Using Hyper-Relational Facts (Extended Abstract)

Mehdi Ali
Max Berrendorf
Mikhail Galkin
Veronika Thost
Tengfei Ma
Volker Tresp
Jens Lehmann

For many years, link prediction on knowledge. graphs has been a purely transductive task, not allowing for reasoning on unseen entities. Recently, increasing efforts are put into exploring semi- and fully inductive scenarios, enabling inference over unseen and emerging entities. Still, all these approaches only consider triple-based KGs, whereas their richer counterparts, hyper-relational KGs (e. g. , Wikidata), have not yet been properly studied. In this work, we classify different inductive settings and study the benefits of employing hyper-relational KGs on a wide range of semi- and fully inductive link prediction tasks powered by recent advancements in graph neural networks. Our experiments on a novel set of benchmarks show that qualifiers over typed edges can lead to performance improvements of 6% of absolute gains (for the Hits@10 metric) compared to triple-only baselines. Our code is available at https: //github. com/mali-git/hyper_relational_ilp.

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ICLR Conference 2022 Conference Paper

Query Embedding on Hyper-Relational Knowledge Graphs

Dimitrios Alivanistos
Max Berrendorf
Michael Cochez
Mikhail Galkin 0001

Multi-hop logical reasoning is an established problem in the field of representation learning on knowledge graphs (KGs). It subsumes both one-hop link prediction as well as other more complex types of logical queries. Existing algorithms operate only on classical, triple-based graphs, whereas modern KGs often employ a hyper-relational modeling paradigm. In this paradigm, typed edges may have several key-value pairs known as qualifiers that provide fine-grained context for facts. In queries, this context modifies the meaning of relations, and usually reduces the answer set. Hyper-relational queries are often observed in real-world KG applications, and existing approaches for approximate query answering cannot make use of qualifier pairs. In this work, we bridge this gap and extend the multi-hop reasoning problem to hyper-relational KGs allowing to tackle this new type of complex queries. Building upon recent advancements in Graph Neural Networks and query embedding techniques, we study how to embed and answer hyper-relational conjunctive queries. Besides that, we propose a method to answer such queries and demonstrate in our experiments that qualifiers improve query answering on a diverse set of query patterns.

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AAAI Conference 2021 Conference Paper

Argument Mining Driven Analysis of Peer-Reviews

Michael Fromm
Evgeniy Faerman
Max Berrendorf
Siddharth Bhargava
Ruoxia Qi
Yao Zhang
Lukas Dennert
Sophia Selle

Peer reviewing is a central process in modern research and essential for ensuring high quality and reliability of published work. At the same time, it is a time-consuming process and increasing interest in emerging fields often results in a high review workload, especially for senior researchers in this area. How to cope with this problem is an open question and it is vividly discussed across all major conferences. In this work, we propose an Argument Mining based approach for the assistance of editors, meta-reviewers, and reviewers. We demonstrate that the decision process in the field of scientific publications is driven by arguments and automatic argument identification is helpful in various use-cases. One of our findings is that arguments used in the peer-review process differ from arguments in other domains making the transfer of pretrained models difficult. Therefore, we provide the community with a new peer-review dataset from different computer science conferences with annotated arguments. In our extensive empirical evaluation, we show that Argument Mining can be used to efficiently extract the most relevant parts from reviews, which are paramount for the publication decision. The process remains interpretable since the extracted arguments can be highlighted in a review without detaching them from their context.

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JMLR Journal 2021 Journal Article

PyKEEN 1.0: A Python Library for Training and Evaluating Knowledge Graph Embeddings

Mehdi Ali
Max Berrendorf
Charles Tapley Hoyt
Laurent Vermue
Sahand Sharifzadeh
Volker Tresp
Jens Lehmann

Recently, knowledge graph embeddings (KGEs) have received significant attention, and several software libraries have been developed for training and evaluation. While each of them addresses specific needs, we report on a community effort to a re-design and re-implementation of PyKEEN, one of the early KGE libraries. PyKEEN 1.0 enables users to compose knowledge graph embedding models based on a wide range of interaction models, training approaches, loss functions, and permits the explicit modeling of inverse relations. It allows users to measure each component's influence individually on the model's performance. Besides, an automatic memory optimization has been realized in order to optimally exploit the provided hardware. Through the integration of Optuna, extensive hyper-parameter optimization (HPO) functionalities are provided. [abs] [ pdf ][ bib ] &copy JMLR 2021. ( edit, beta )

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