Paper Link: https://arxiv.org/abs/2312.15643

Github Link: https://github.com/HKUST-KnowComp/AbductiveKGR.

In the field of artificial intelligence, generating logical hypotheses from observations is crucial. The paper, Advancing Abductive Reasoning in Knowledge Graphs through Complex Logical Hypothesis Generation, presents innovative methods to enhance this process using knowledge graphs (KGs).

Key Concepts

Observations

Observations are pieces of data or evidence that need to be explained. In the context of knowledge graphs, they are a set of entities that occurred in the same context. For example, a social network user follows five people, and we want to know why given we have a knowledge graph about these people.

Example: In a medical KG, an observation might be a set of symptoms exhibited by a patient, such as fever, cough, and fatigue.

Hypothesis

A hypothesis is a proposed explanation that accounts for the observations. It is generated to as a logical expresssion, or in other words, a knowledge graph query. The generated hypothesis is not in natural language but in formal language. The advantage of using formal language is that it can be executed on a knowledge graph to verify whether the hypothesis is correct.

Example: For the symptoms of fever, cough, and fatigue, a possible hypothesis could be that the patient has influenza. This hypothesis explains the observed symptoms.

The Problem

Traditional methods often struggle to generate hypotheses when faced with new or unseen observations. This is mainly because the knowledge graph is contructed under open-world assumption, which there are missing links which is correct but unobserved from the KG. Because of the missing links, the graph search algorihms cannot not be directly applied to find the exact hypothesis.

The paper addresses this by focusing on complex logical hypothesis generation within KGs.

Proposed Solution: Reinforcement Learning from Knowledge Graph (RLF-KG)

The authors introduce Reinforcement Learning from Knowledge Graph (RLF-KG) to improve hypothesis generation:

Supervised Model Training

• Supervised Learning: Initially, a model is trained to produce hypotheses that match known reference hypotheses. However, we find this is not enough. The model tends to generate the queires that are similar to good hypothese in token-level, but token-level similarity does ensure it is a good hypothesis that explain the observation.

RLF-KG Approach

• Reinforcement Learning: The model uses feedback from the KG to refine its hypotheses, minimizing differences between what is observed and what is concluded by the generated logical hypothesis. We have to use the RL methods because the execution of the generated logical hypothesis on KG is not differentiable, thus we cannot use gradient-based methods directly optimize the generation model.

Key Findings

The experiments show that RLF-KG enhances hypothesis generation in several ways:

• Structural Accuracy: Hypotheses are more closely aligned with reference examples.
• Improved Explanations: The generated hypotheses provide better explanations for observations.
• State-of-the-Art Performance: The method achieves superior results on three widely used KGs.

Implications and Future Directions

This research opens new possibilities for integrating abductive reasoning with KGs, impacting areas like automated reasoning, AI diagnostics, and decision-making tools. Future research could explore:

• Scalability: Applying the method to larger datasets.
• Domain Expansion: Extending to different domains and KGs.
• Real-World Applications: Implementing these methods in practical scenarios.

In summary, this paper marks a significant advancement in AI by combining machine learning with structured knowledge to enhance logical reasoning. For more details, you can read the full paper here.