Configuration of Industrial Automation Solutions Using Multi-relational Recommender Systems

Hildebrandt, Marcel; Sunder, Swathi Shyam; Mogoreanu, Serghei; Thon, Ingo; Tresp, Volker; Runkler, Thomas A.

doi:10.1007/978-3-030-10997-4_17

Cited by 13 publications

(3 citation statements)

References 17 publications

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“…Furthermore, we make use of the scientific Python stack for scientific computing (NumPy 9 , SciPy 10 , Scikit-Learn 11 , Pandas 12 ). Moreover, we apply following community standards: flake8 13 to ensure code quality, setuptools 14 to create distributions, pyroma 15 to ensure package metadata standards, sphinx 16 to build our documentation and Read the Docs 17 to host it. Finally, Travis-CI 18 is used as continuous integration server.…”

Section: Methodsmentioning

confidence: 99%

“…Impact on Industry. KGs are established in several major companies such as Google, Facebook, Bayer, Siemens, and KGEs are for instance used to build KGE based recommender systems [6,15]. Furthermore, the evolution of industry to Industry 4.0 paves a new way for KGEs to be applied in the observation of manufacturing processes: (knowledge) graphs are a convenient approach to model the data produced by sensors which can be used to model the status of production pipelines.…”

Section: Impactmentioning

confidence: 99%

See 1 more Smart Citation

The KEEN Universe

Ali

Jabeen

Hoyt

et al. 2019

Lecture Notes in Computer Science

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There is an emerging trend of embedding knowledge graphs (KGs) in continuous vector spaces in order to use those for machine learning tasks. Recently, many knowledge graph embedding (KGE) models have been proposed that learn low dimensional representations while trying to maintain the structural properties of the KGs such as the similarity of nodes depending on their edges to other nodes. KGEs can be used to address tasks within KGs such as the prediction of novel links and the disambiguation of entities. They can also be used for downstream tasks like question answering and fact-checking. Overall, these tasks are relevant for the semantic web community. Despite their popularity, the reproducibility of KGE experiments and the transferability of proposed KGE models to research fields outside the machine learning community can be a major challenge. Therefore, we present the KEEN Universe, an ecosystem for knowledge graph embeddings that we have developed with a strong focus on reproducibility and transferability. The KEEN Universe currently consists of the Python packages PyKEEN (Python KnowlEdge EmbeddiNgs), BioKEEN (Biological KnowlEdge EmbeddiNgs), and the KEEN Model Zoo for sharing trained KGE models with the community.

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Section: Methodsmentioning

confidence: 99%

Section: Impactmentioning

confidence: 99%

The KEEN Universe

Ali

Jabeen

Hoyt

et al. 2019

Lecture Notes in Computer Science

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“…Machine learning algorithms designed to address this problem try to infer missing triples or detect false facts based on observed connectivity patterns. Moreover, many tasks such as question answering or collaborative filtering can be formulated in terms of predicting new links in a KG (e.g., (Lukovnikov et al 2017), (Hildebrandt et al 2018)). Most machine learning approaches for reasoning on KGs embed both entities and predicates into low dimensional vector spaces.…”

Section: Introductionmentioning

confidence: 99%

Reasoning on Knowledge Graphs with Debate Dynamics

Hildebrandt

Serna

et al. 2020

AAAI

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We propose a novel method for automatic reasoning on knowledge graphs based on debate dynamics. The main idea is to frame the task of triple classification as a debate game between two reinforcement learning agents which extract arguments – paths in the knowledge graph – with the goal to promote the fact being true (thesis) or the fact being false (antithesis), respectively. Based on these arguments, a binary classifier, called the judge, decides whether the fact is true or false. The two agents can be considered as sparse, adversarial feature generators that present interpretable evidence for either the thesis or the antithesis. In contrast to other black-box methods, the arguments allow users to get an understanding of the decision of the judge. Since the focus of this work is to create an explainable method that maintains a competitive predictive accuracy, we benchmark our method on the triple classification and link prediction task. Thereby, we find that our method outperforms several baselines on the benchmark datasets FB15k-237, WN18RR, and Hetionet. We also conduct a survey and find that the extracted arguments are informative for users.

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