Knowledge Graph Reasoning is a fascinating area within AI that leverages graph structures to extract and infer new knowledge. This blog post aims to guide you through prominent papers in this domain, breaking down the contents into easy-to-understand categories and providing troubleshooting information for common issues. Let’s embark on this journey!

Contents Overview

• 1. Survey Papers
• 2. Multi-hop Reasoning
• 3. Reasoning with Logic Rule
• 4. Query-based Reasoning
• 5. Benchmark and Evaluation
• Acknowledgements

1. Survey Papers

Surveys provide comprehensive insights into the body of work done in Knowledge Graph Reasoning.

• A Review: Knowledge Reasoning over Knowledge Graph. Xiaojun Chen, Shengbin Jia, Yang Xiang. Expert Systems with Applications.
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2. Multi-Hop Reasoning

This section focuses on the art of multi-hop reasoning, where we hop through various nodes in a graph to gain insights.

2.1 Entity Prediction

Predict the missing tail entity based on different paths connecting three entities.

Think of multi-hop reasoning like a detective solving a mystery. The detective (our model) gathers clues from various places (nodes in a graph) to uncover the culprit (tail entity).

• Go for a Walk and Arrive at the Answer. Rajarshi Das et al. ICLR 2018.
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2.2 Relation Prediction

Given head and tail entities, predict the missing relation between them.

In this setting, imagine that each relationship is like a missing piece of a puzzle. Our task is to fit the correct piece into the puzzle so that the overall picture (knowledge) becomes complete.

• Random walk inference and learning in a large scale knowledge base. Ni Lao et al. EMNLP 2011.
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2.3 Inductive Reasoning

This section discusses predicting relationships by leveraging subgraph reasoning.

• Inductive Relation Prediction by Subgraph Reasoning. Komal K. Teru et al. ICML 2020.
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3. Reasoning with Logic Rule

This section pertains to using logical rules to enhance knowledge graph understanding.

3.1 Rule Mining Learning

• Fast rule mining in ontological knowledge bases with AMIE+. Luis Galárraga et al. VLDB Journal 2015.
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3.2 Rule-based Reasoning

• Differentiable Learning of Logical Rules for Knowledge Base Reasoning. Fan Yang et al. NeurIPS 2017.
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4. Query-based Reasoning

This section focuses on reasoning based on specific queries expressed in logic or natural language.

4.1 Path-based Query

• Traversing Knowledge Graphs in Vector Space. Kelvin Guu et al. EMNLP 2015.
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5. Benchmark and Evaluation

Baking knowledge graphs requires continuous evaluation and benchmarking. This section explores fairness, accuracy, and transparency of multi-hop reasoning processes.

• Is Multi-Hop Reasoning Really Explainable? Xin Lv et al. Arxiv 2021.
  Read Here Code

Acknowledgements

Heartfelt thanks to all contributors, especially Xin Lv and Jiaxin Shi, for their relentless efforts in maintaining these resources.

Troubleshooting Tips

If you encounter any issues while navigating or working with these papers, consider the following troubleshooting ideas:

• Make sure you have a stable internet connection when trying to access online resources.
• Check if citation formats are correctly followed for your reference.
• For any broken links, try accessing through a different search engine to locate the papers.
• Lastly, for more insights, updates, or to collaborate on AI development projects, stay connected with fxis.ai.

At fxis.ai, we believe that such advancements are crucial for the future of AI, as they enable more comprehensive and effective solutions. Our team is continually exploring new methodologies to push the envelope in artificial intelligence, ensuring that our clients benefit from the latest technological innovations.