Image-embodied Knowledge Representation Learning

Abstract: Entity images could provide significant visual information for knowledge representation learning. Most conventional methods learn knowledge representations merely from structured triples, ignoring rich visual information extracted from entity images. In this paper, we propose a novel Image-embodied Knowledge Representation Learning model (IKRL), where knowledge representations are learned with both triple facts and images. More specifically, we first construct representations for all images of an entity with a neural image encoder. These image representations are then integrated into an aggregated image-based representation via an attention-based method. We evaluate our IKRL models on knowledge graph completion and triple classification. Experimental results demonstrate that our models outperform all baselines on both tasks, which indicates the significance of visual information for knowledge representations and the capability of our models in learning knowledge representations with images.

• The paper introduces an innovative model that fuses image data with triple-based knowledge to enrich entity representations.
• It employs neural networks with attention mechanisms, utilizing AlexNet for image feature extraction and projection of features into entity space.
• Experimental results on WN9-IMG demonstrate that the IKRL (UNION) model outperforms traditional methods in graph completion and triple classification tasks.

Image-embodied Knowledge Representation Learning

The paper presents an innovative approach to knowledge representation by amalgamating visual information from images with triple-based data in knowledge graphs. The proposed model, Image-embodied Knowledge Representation Learning (IKRL), addresses a significant gap in conventional knowledge representation learning, which often neglects the wealth of visual information available from entity images.

Methodology

The methodology outlined in the paper leverages neural networks and attention-based mechanisms to construct image-based entity representations, which are integrated into existing knowledge representations. The model begins with an image encoder that consists of an image representation module and an image projection module. The image representation module utilizes AlexNet to extract significant features from images, while the image projection module maps those features onto an entity space using a shared projection matrix.

The aggregated image-based representation for entities is built using an instance-level attention-based multi-instance learning method. This method intelligently selects informative images among multiple instances of an entity by calculating attention scores based on both image embeddings and structured entity representations. The aggregated representations are then learned jointly with structure-based representations under the framework of translation-based models like TransE.

Experimental Results

The IKRL model was evaluated on two primary tasks: knowledge graph completion and triple classification, using a novel dataset, WN9-IMG, derived from WordNet and ImageNet. The results indicate that the IKRL models outperform baseline models such as TransE and TransR in both tasks. Particularly notable is the IKRL (UNION) model, which represents a combination of structure-based and image-based representations, achieving superior performance across the evaluation metrics of mean rank and Hits@10.

The paper further demonstrates the superiority of the attention-based combination strategy over average and maximum pooling alternatives. The attention mechanism proved adept at selecting informative image instances and ignoring potentially misleading ones.

Implications and Future Directions

This research provides compelling evidence for the inclusion of visual information in knowledge graph embeddings, suggesting that image data offers valuable contextual information that enhances entity representation learning. The findings have practical implications for various AI applications, including improved accuracy in semantic tasks like question answering and entity recognition.

For future work, the authors propose several directions: employing more sophisticated models for image feature extraction, extending IKRL’s integration with other translation-based methods beyond TransE, and considering complex scenarios where multiple entities and relations are encapsulated within a single image. This opens potential pathways for enriched multi-modal knowledge representation conducive to more nuanced AI reasoning and learning.

The paper significantly enriches the discourse on multi-modal learning and advocates for a paradigm shift in integrating visual cues into knowledge graph frameworks. Researchers interested in advancing AI applications more attuned to human-like understanding would find this approach particularly relevant and potentially transformative.