Multimodal Analogical Reasoning over Knowledge Graphs (2210.00312v4)

Abstract: Analogical reasoning is fundamental to human cognition and holds an important place in various fields. However, previous studies mainly focus on single-modal analogical reasoning and ignore taking advantage of structure knowledge. Notably, the research in cognitive psychology has demonstrated that information from multimodal sources always brings more powerful cognitive transfer than single modality sources. To this end, we introduce the new task of multimodal analogical reasoning over knowledge graphs, which requires multimodal reasoning ability with the help of background knowledge. Specifically, we construct a Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph MarKG. We evaluate with multimodal knowledge graph embedding and pre-trained Transformer baselines, illustrating the potential challenges of the proposed task. We further propose a novel model-agnostic Multimodal analogical reasoning framework with Transformer (MarT) motivated by the structure mapping theory, which can obtain better performance. Code and datasets are available in https://github.com/zjunlp/MKG_Analogy.

• The paper introduces a multimodal analogical reasoning task formalized as a link prediction problem that emphasizes relational mapping over feature similarity.
• The authors construct the MARS dataset and MarKG multimodal knowledge graph, integrating text and image data to enable advanced reasoning benchmarks.
• The study demonstrates that the MarT framework enhances zero-shot analogical reasoning by optimizing Transformer-based models with a novel relaxation loss.

Multimodal Analogical Reasoning over Knowledge Graphs

The paper "Multimodal Analogical Reasoning over Knowledge Graphs" introduces an innovative framework for performing analogical reasoning across multiple modalities using knowledge graphs. The authors address the previously unexplored task of multimodal analogical reasoning, leveraging the cognitive advantages demonstrated in human learning from multimodal sources. The paper constructs the Multimodal Analogical Reasoning dataSet (MARS) and a multimodal knowledge graph called MarKG. These resources aim to foster advancements in reasoning algorithms by providing benchmarks and data for developing multimodal analogical reasoning capabilities in AI systems.

Core Contributions

1. Multimodal Analogical Task Formulation: The paper formalizes analogical reasoning as a link prediction task without explicitly providing relations. This approach diverges from multiple-choice paradigms and aligns more closely with structural mapping theories in cognitive psychology, which emphasize relational similarity rather than feature-level similarity.
2. Dataset Creation: MARS and MarKG are introduced to facilitate the proposed task, providing researchers with a substantial dataset drawn from existing knowledge graph structures such as Wikidata, combined with images and text data from sources including Laion-5B. This setup encourages reasoning over both visual and textual modalities.
3. Evaluation and Baseline Models: The researchers establish baseline performance with multimodal knowledge graph embedding (MKGE) methods and Transformer-based multimodal pre-trained (MPT) architectures. They observe that MKGE models can be optimized for analogical reasoning by incorporating the ANALOGY model, while MPT architectures benefit from a novel Multimodal analogical reasoning framework with Transformer (MarT).
4. Framework Innovation with MarT: The MarT framework enhances Transformer models for analogical reasoning by considering the adaptive interaction between analogy examples and questions. This is achieved through a relaxation loss that prioritizes relation over entity similarity, adhering to the Structure Mapping Theory.

Results and Implications

The experiments reveal that the MarT framework significantly improves reasoning capabilities over baseline models, particularly in conditions requiring adaptation to novel relations, akin to zero-shot learning tasks. The paper demonstrates that models pre-trained on MarKG exhibit superior reasoning performance on the MARS dataset, which validates the method's efficacy in linking and exploiting multimodal data sources for reasoning tasks.

This paper underscores the viability of extending AI analogical reasoning capability beyond single-modality constraints, which holds potential implications for developing AI systems that simulate human cognitive processes more closely. The implications are vast across fields requiring advanced reasoning, such as automated decision making, creative design, and semantic understanding.

In conclusion, this paper is a pivotal exploration into multimodal analogical reasoning, laying groundwork for future research into neural architectures that can process complex, multimodal information analogous to human cognition. Future studies in AI could explore expanding the scale and diversity of multimodal datasets and further refining transformer-based models to emulate more nuanced cognitive aspects of human-like reasoning and learning.