Machine Learning with Symbolic Methods and Knowledge Graphs

# including :

# 2nd International Workshop On

# New Trends in Representation Learning with Knowledge Graphs

and CSSA

## ECML PKDD Workshop 2021

**MLSMKG 2021: **

**MLSMKG 2021:**

This year KGRL is joint with CSSA and named as: Machine Learning with Symbolic Methods and Knowledge Graphs

**Proceedings: **

**Proceedings:**

This Years proceedings are published here.

**Overview**

**Overview**

Knowledge Graphs [1] are becoming the standard for storing, retrieving, and querying structured data. In academia and industry, they are increasingly used to provide background knowledge. Over the last years, several research contributions were made which show that machine learning, especially representation learning, can be successfully applied to knowledge graphs enabling inductive inference about facts with unknown truth values.

Several of these approaches [2, 3] encode the graph structure that can be used for tasks such as link prediction, node classification, entity resolution, recommendation, dialogue systems, and many more. Although proposed graph representations can capture the complex relational patterns over multiple hops, they are still insufficient to solve more complex tasks such as relational reasoning [4,5]. For this kind of tasks, we envision a need for representations with more expressive power, which could include representation in non-Euclidean space. This starts by capturing e.g., type constrained, transitive or hierarchical relations in an embedding [16], up to learning expressive knowledge representations languages like first-order logic rules.

Furthermore, most approaches for learning representations for knowledge graphs focus on transductive settings, i.e., all entities and relations need to be seen during training, not allowing predictions for unseen elements [18,19]. For evolving graphs, approaches are required that generalize to unseen entities and relations. One avenue of research to address inductiveness is to employ multimodal approaches that compensate for missing modalities [20], and recently meta-learning approaches have successfully been applied [18].

Lately, the generalization of deep neural network models to non-Euclidean domains such as graphs and manifolds is explored [6]. They study the fundamental aspects that influence the underlying geometry of structured data for building graph representations [7, 8]. Recent advances in graph representation learning led to novel approaches such as convolutional neural networks for graphs [17, 9, 10, 11], attention-based graph network [12] etc. Most graphs here are either undirected or directed with both discrete and continuous node and edge attributes representing types of spatial or spectral data.

In this workshop, we want to see novel representation learning methods, approaches that can be applied to inductive learning and to (logical) reasoning [13, 14, 15], and works that shed insights into the expressive power, interpretability, and generalization of graph representation learning methods.

Also, we want to bring together researchers from different disciplines but united by their adoption of earlier mentioned techniques from machine learning. We invite the submission of papers on topics including, but not limited to:

Knowledge graph representations for relational reasoning

Inductive link prediction

Graph neural networks for knowledge graphs

Query embeddings

Knowledge graph representation learning for conversational AI

Unsupervised learning of complex graphs over graph-structured data

Neural/Statistical Relational Learning

Integrating learning of expressive knowledge representation and flexible reasoning

Exploring non-Euclidean spaces for knowledge graph representations

Inference tasks for learned knowledge graph representations that require general-purpose reasoning

Entity alignment

Knowledge graph representations for industrial recommendation systems

Decision modeling in personalized medicine with knowledge graph representations (e.g., decision support at the point of care in tumor boards)

Visual scene graph modeling with the help of knowledge graphs

Knowledge graph representation to support natural language understanding

Knowledge Graphs for cognitive science

Representation learning on time-dependent knowledge graphs

Question answering and commonsense reasoning via knowledge graphs

Knowledge graph representation learning models based on adversarial methods

Quantum Computing as a basis for scalable Knowledge graph representation learning

**References**

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*arXiv preprint arXiv:2010.11465*.Halford, Graeme S., William H. Wilson, and Steven Phillips. "Relational knowledge: The foundation of higher cognition." Trends in cognitive sciences 14.11 (2010): 497-505.

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