Are a Thousand Words Better Than a Single Picture? Beyond Images -- A Framework for Multi-Modal Knowledge Graph Dataset Enrichment

Abstract

Multi-Modal Knowledge Graphs (MMKGs) benefit from visual information, yet large-scale image collection is hard to curate and often excludes ambiguous but relevant visuals (e.g., logos, symbols, abstract scenes). We present Beyond Images, an automatic data-centric enrichment pipeline with optional human auditing. This pipeline operates in three stages: (1) large-scale retrieval of additional entity-related images, (2) conversion of all visual inputs into textual descriptions to ensure that ambiguous images contribute usable semantics rather than noise, and (3) fusion of multi-source descriptions using a large language model (LLM) to generate concise, entity-aligned summaries. These summaries replace or augment the text modality in standard MMKG models without changing their architectures or loss functions. Across three public MMKG datasets and multiple baseline models, we observe consistent gains (up to 7% Hits@1 overall). Furthermore, on a challenging subset of entities with visually ambiguous logos and symbols, converting images into text yields large improvements (201.35% MRR and 333.33% Hits@1). Additionally, we release a lightweight Text-Image Consistency Check Interface for optional targeted audits, improving description quality and dataset reliability. Our results show that scaling image coverage and converting ambiguous visuals into text is a practical path to stronger MMKG completion. Code, datasets, and supplementary materials are available at https://github.com/pengyu-zhang/Beyond-Images.

Figures (3)

• Figure 1: Original MMKG Dataset: the entity "Amsterdam" with its photo and a textual description. Ambiguous yet Relevant Images: additional visuals such as the three red Saint Andrew's crosses and an abstract cityscape, whose semantics may be unclear when incorporated directly as visual features. Textual Descriptions of the Additional Ambiguous Images: our framework converts these images into concise, entity-aligned text, expanding semantic coverage while mitigating noise from ambiguous visual embeddings.
• Figure 2: Overview of our Beyond Images framework. Given an MMKG entity, (1) the Modality Extension Module retrieves additional web images from search engines; (2) the Semantic Generation Module produces per-image textual descriptions for both original and newly retrieved images; (3) the LLM-based Semantic Fusion Module consolidates valid descriptions into a single, rich summary paragraph via an explicit prompt. The summary becomes an enhanced textual view of the entity and is stored in the new enriched dataset for downstream MMKG completion.
• Figure 3: Hits@1 comparison on the MKG-W dataset across four models under three modality settings: I+T (image embeddings + textual descriptions), T+G (textual descriptions + image-generated descriptions), and I+T+G (all). Legend: G(o) denotes descriptions from original images, G(n) from newly retrieved images, G(o+n) their concatenation, and LLM Fusion an LLM summary over all descriptions. Bars report Hits@1 (higher is better).