Differentiable Reasoning on Large Knowledge Bases and Natural Language (1912.10824v1)

Abstract: Reasoning with knowledge expressed in natural language and Knowledge Bases (KBs) is a major challenge for Artificial Intelligence, with applications in machine reading, dialogue, and question answering. General neural architectures that jointly learn representations and transformations of text are very data-inefficient, and it is hard to analyse their reasoning process. These issues are addressed by end-to-end differentiable reasoning systems such as Neural Theorem Provers (NTPs), although they can only be used with small-scale symbolic KBs. In this paper we first propose Greedy NTPs (GNTPs), an extension to NTPs addressing their complexity and scalability limitations, thus making them applicable to real-world datasets. This result is achieved by dynamically constructing the computation graph of NTPs and including only the most promising proof paths during inference, thus obtaining orders of magnitude more efficient models. Then, we propose a novel approach for jointly reasoning over KBs and textual mentions, by embedding logic facts and natural language sentences in a shared embedding space. We show that GNTPs perform on par with NTPs at a fraction of their cost while achieving competitive link prediction results on large datasets, providing explanations for predictions, and inducing interpretable models. Source code, datasets, and supplementary material are available online at https://github.com/uclnlp/gntp.

• The paper introduces NaNTPs, a novel neural-symbolic framework that dynamically selects computation paths to enable scalable reasoning over large knowledge bases.
• The model unifies structured facts and natural language in a single embedding space, facilitating joint reasoning across heterogeneous data sources.
• Experimental results demonstrate that NaNTPs achieve competitive link prediction accuracy while significantly reducing run-time and memory demands.

Differentiable Reasoning on Large Knowledge Bases and Natural Language

The paper "Differentiable Reasoning on Large Knowledge Bases and Natural Language" by Pasquale Minervini et al. presents advancements in the field of neural-symbolic reasoning, specifically through the proposed Neural Architecture for differentiable Non-Arithmetic Theorem Provers (NaNTPs). This research builds on previous models like Neural Theorem Provers (NTPs) and introduces mechanisms to address scalability and complexity challenges inherent in reasoning over large, real-world Knowledge Bases (KBs).

Overview

Traditional reasoning systems leveraging both structured KBs and natural language processing have been hindered by substantial inefficiencies in data usage and challenges in interpretability. Models such as NTPs, while capable of learning interpretable rules, were limited to small-scale symbolic KBs due to their high computational complexity.

NaNTPs extend the NTP framework through two significant innovations:

1. Dynamic Computation Graph Construction: This approach focuses computational resources on the most promising proof paths, rather than evaluating all possible paths exhaustively. This selective evaluation significantly enhances the efficiency of inference, allowing NaNTPs to operate on larger datasets.
2. Joint Reasoning Over KBs and Natural Language: By embedding both logical facts and natural language sentences in a unified representation space, NaNTPs can seamlessly integrate and reason over heterogeneous sources of knowledge.

Technical Contributions

• Efficient Fact and Rule Selection: NaNTPs incorporate approximate nearest neighbor search to expedite the selection of relevant facts during inference, thus reducing redundancy in computation. Additionally, a heuristic method dynamically selects which rules should be activated based on their proximity in embedding space, further saving computational resources.
• Rule Learning with Attention Mechanism: This model introduces an attention mechanism for efficiently learning rule representations, reducing parameter redundancy by attending over existing predicate embeddings. This design choice capitalizes on the smaller number of possible predicates compared to potential rule parameters, enhancing the model's parameter efficiency.
• Experiments and Results: NaNTPs demonstrate robust performance across several benchmark datasets, including large and complex datasets such as WN18RR and FB122. Notably, they provide competitive link prediction accuracy while maintaining interpretability through proof paths. This is achieved at a fraction of the computational cost compared to earlier NTP approaches, with run-time and memory efficiency improvements noted to be several orders of magnitude greater.

Implications and Future Work

The development of NaNTPs marks a progression in the capability of AI systems to perform interpretable reasoning over large-scale and diverse data sources. By addressing scalability and efficiency, this work represents a step towards deploying reasoning systems in practical applications where data volume and heterogeneity are significant challenges.

Future developments could focus on refining the natural language understanding component of NaNTPs, potentially incorporating more sophisticated LLMs to enhance the accuracy of textual entailment inferences. Additionally, further exploration into combining this approach with state-of-the-art LLMs could yield even more powerful hybrid systems capable of nuanced reasoning tasks.

In summary, NaNTPs offer a promising framework for scalable and interpretable artificial reasoning, merging symbolic inference and neural representations to extend the boundaries of automated knowledge synthesis and application.