Multi-modal search in knowledge graphs: how it works

Are you ready to discover powerful insights hidden across your diverse data formats? Organizations today are brimming with valuable information stored in text documents, images, videos, and audio files. However, this rich diversity often leads to fragmented data silos, preventing businesses from uncovering the crucial connections and insights they need for strategic decisions and innovation. The great news is—this limitation is now being transformed into a powerful advantage through multi-modal search in knowledge graphs. By seamlessly integrating various data types within interconnected knowledge structures, businesses can perform sophisticated, cross-format queries, revealing valuable relationships that were previously invisible.

In this article, you'll discover exactly how multi-modal search works, and learn about the technology behind this groundbreaking approach. Let's dive in and see how your organization can benefit from unifying your data in entirely new ways.

What are multi-modal knowledge graphs?

Multi-modal knowledge graphs (MMKGs) expand beyond traditional text-based knowledge graphs by bringing multiple data formats into a unified framework. The core components include:

• Entities: Objects, concepts, or individuals represented through multiple modalities (e.g., a "dog" represented in text descriptions, images, and audio recordings)
• Relationships: Links between entities that span across modalities (e.g., "belongs to" relationships)
• Attributes: Properties that include numerical values, textual descriptions, or visual features
• Multiple data modalities: Various formats like text, images, audio, and video integrated within the same graph structure

This structure creates a more comprehensive knowledge representation that mirrors how humans naturally process information through multiple senses.

The evolution from single-modal to multi-modal search in knowledge graphs

Traditional knowledge graphs primarily focused on text-based representations, but the development of specialized AI models, sophisticated neural networks, and embedding techniques has enabled the alignment of different modalities in shared semantic spaces. This evolution reflects the growing recognition that human knowledge is inherently multi-modal, and AI systems that process diverse data formats gain significant advantages in understanding and reasoning, as demonstrated by innovative projects in the field.

Why multi-modal search in knowledge graphs matters for modern AI

Multi-modal search has become essential for modern AI systems for several compelling reasons:

• Rich contextual understanding: By processing multiple data types together, AI systems gain a more nuanced understanding of concepts and their relationships, similar to how humans integrate sensory inputs.
• Enhanced reasoning capabilities: MMKGs enable cross-modal reasoning, allowing systems to draw connections between information in different formats that might otherwise remain disconnected, contributing to the development of scalable AI systems.
• Breaking down data silos: Organizations typically store different data types in separate systems, limiting the ability to leverage connections between them. MMKGs create bridges between these isolated information repositories.
• More natural user interactions: Users can search using the most convenient or relevant modality for their query, whether that's text, an image, or even audio.

As AI continues to advance, multi-modal search in knowledge graphs will become increasingly central to creating systems that can understand and interact with the world in ways that resemble human cognition.

Core components of multi-modal knowledge graphs for multi-modal search

Multi-modal knowledge graphs represent a significant evolution beyond traditional knowledge graphs by incorporating diverse data types into a unified structure. To understand how these systems work, let's examine their fundamental building blocks and how they function together.

Entities, relations, and attributes across modalities

Entities (E) in MMKGs represent objects, concepts, or individuals that exist in the real world. Unlike traditional knowledge graphs where entities might only have text-based representations, multi-modal entities can exist simultaneously across different data formats. For example:

• A "dog" entity might be represented by:
  • Text descriptions and taxonomic classification
  • Images showing different breeds and poses
  • Audio clips of barking sounds

These different representations of the same entity are interconnected, providing a richer semantic understanding.

Relationships define the connections between entities. In MMKGs, these connections can span across modalities, creating a web of meaningful interactions. Relationships might include "owns," "belongs to," or "is a part of," linking entities regardless of their modality type. For instance, a relationship might connect a person (represented by text and image) with their voice recording (audio modality).

Attributes provide additional characteristics of entities. In multi-modal contexts, attributes extend beyond simple text properties to include:

• Visual features (color, texture, spatial dimensions)
• Audio properties (frequency, duration, tone)
• Numerical measurements
• Textual descriptions

For example, a landmark entity might have attributes like geographic coordinates (numerical), architectural style (text), visual appearance (image), and ambient sounds (audio).

Triples and knowledge representation in multi-modal search

Triples form the foundation of knowledge representation in MMKGs, typically structured as RDF (subject, predicate, object). In multi-modal systems, these triples incorporate information from various modalities:

• (Dog, has_appearance, [image_data])
• (Dog, makes_sound, [audio_data])
• (Dog, belongs_to_category, Mammal)

This structure allows for complex knowledge representation that blends different modalities seamlessly. For example, the ImgFact Large-Scale MMKG demonstrates this approach, integrating 3.7 million images with textual triplet facts to enable advanced relationship classification and image-text retrieval tasks.

Integration approaches: Attribute-based vs. node-based

There are two primary approaches to constructing MMKGs, each with distinct advantages:

Attribute-based integration treats multi-modal data as attributes of entities. In this approach:

• Images, audio, or video become features of specific entities
• The graph structure remains primarily focused on entity-entity relationships
• Multi-modal data enriches entity descriptions but doesn't fundamentally alter the graph topology

For example, in a medical MMKG, patient entities might have attributes that include MRI scans (images) and doctor's notes (text).

Node-based integration elevates multi-modal data to become standalone nodes in the graph. This approach:

• Allows modalities like images or videos to form their own entities
• Enables relationships not only between traditional entities but also between modalities
• Creates a more complex but expressive graph structure

For instance, in a media MMKG, an image node might be linked to multiple concept nodes and other media nodes, forming a rich network of cross-modal connections, facilitating knowledge base creation.

This node-based approach is particularly powerful for applications requiring sophisticated reasoning across modalities, as demonstrated by systems like the MR-MKG framework and projects from the Knowledge Graph AI Challenge, which use graph attention networks and cross-modal alignment for advanced reasoning tasks.

By combining these core components—entities, relationships, attributes, triples, and modalities—multi-modal knowledge graphs create comprehensive knowledge representations that transcend the limitations of traditional, text-only approaches.

The architecture of multi-modal knowledge graph systems for multi-modal search

Multi-modal knowledge graph systems represent a sophisticated evolution of traditional knowledge graphs, designed to handle diverse data types including text, images, audio, and video. The architecture of these systems requires careful consideration to ensure efficient processing, alignment, and retrieval across modalities.

Data ingestion and processing across modalities

The foundation of any multi-modal knowledge graph system begins with data ingestion. This process must be tailored to handle the unique characteristics of each modality:

• Text processing typically involves tokenization, embedding generation using models like BERT, Word2Vec, or GloVe, and entity extraction to identify key concepts.
• Image processing relies on convolutional neural networks (CNNs) like ResNet or vision transformers to extract visual features and identify objects or scenes.
• Audio processing converts sound waves into representations like mel-frequency cepstral coefficients (MFCCs) or spectrograms that can be analyzed by neural networks.
• Video processing combines frame-based image analysis with temporal information, often requiring specialized architectures to capture motion and sequence relationships.

During ingestion, each modality is processed through specialized pipelines that convert raw data into structured representations that can be incorporated into the knowledge graph. This often involves entity recognition across modalities and relationship extraction between entities.

Creating unified semantic spaces

The core challenge in multi-modal knowledge graphs is creating a unified semantic space where different modalities can interact meaningfully. This involves:

• Joint embedding models like CLIP (Contrastive Language-Image Pre-training) that align text and image representations in a shared vector space
• Cross-modal alignment techniques that map representations from different modalities to each other through attention mechanisms or contrastive learning
• Multi-modal fusion strategies that combine information from multiple modalities, including early fusion (combining raw features), late fusion (combining decisions), or hybrid approaches

The creation of unified semantic spaces enables powerful cross-modal operations such as text-to-image search or finding relationships between entities represented in different modalities. For example, a user could search for "red sports cars" and retrieve relevant images, or upload an image to find related textual information.

Advanced systems employ transformer-based architectures with multi-headed attention mechanisms to dynamically align and fuse information across modalities, creating richer and more nuanced entity representations. Continuous AI iteration is crucial in refining these models.

Storage and indexing strategies

Efficient storage and retrieval are critical for multi-modal knowledge graph systems to perform at scale, and the use of serverless databases can provide scalability and flexibility:

• Graph databases like Dgraph store the relationships between entities while accommodating node properties that can include multi-modal embeddings, enabling vector similarity search on massive datasets.
• Hybrid storage solutions combine traditional graph structures with vector indexes to support both relationship traversal and similarity search.

To enable fast retrieval, multi-modal systems employ various indexing techniques:

• Approximate Nearest Neighbor (ANN) indexes like HNSW (Hierarchical Navigable Small World) or IVF (Inverted File) accelerate similarity searches across embedding spaces.
• Cross-modal hashing techniques create compact binary codes that preserve similarity relationships across modalities while reducing storage requirements.
• Two-stage retrieval pipelines that use efficient first-stage retrieval followed by more sophisticated re-ranking to balance speed and accuracy.

The architecture of multi-modal knowledge graph systems represents a complex interplay of specialized processing pipelines, embedding models, and storage technologies. When implemented effectively, these systems enable powerful cross-modal search and reasoning capabilities that surpass traditional single-modality approaches, opening new possibilities for applications across domains like healthcare, e-commerce, and content management.

Embedding techniques for multi-modal search

Embedding techniques form the foundation of multi-modal search by transforming various data types into dense vector representations that capture their semantic meaning. These vectors allow us to measure similarities between different pieces of content, regardless of their original format.

Current state-of-the-art systems employ specialized embedding models for each modality, with recent advances focusing on joint embedding spaces that allow direct comparison across different data types. Models like CLIP (Contrastive Language-Image Pre-training) have revolutionized cross-modal search by creating unified representations for both text and images, enabling powerful zero-shot capabilities and direct text-to-image or image-to-text retrieval.

The most effective multi-modal search systems typically employ:

• Transformer-based encoders that capture rich contextual information within each modality
• Contrastive learning approaches that align representations from different modalities in a shared semantic space
• Cross-attention mechanisms that help focus on relevant aspects of content across modalities

These embeddings, along with the use of semantic embeddings, enhance the ability of systems to understand and represent the meaning of data across different modalities. For cross-modal alignment, strategies like contrastive learning, joint embedding models like BLIP (Bootstrapping Language-Image Pre-training), and multimodal embedding APIs such as Google Cloud's Multimodal Embeddings API generate compatible embeddings for text, images, and video data, supported by advanced AI databases.

The field of multi-modal embeddings continues to advance rapidly. The ability to represent diverse data types in a unified vector space enables powerful applications from cross-modal retrieval to multimodal question answering and content recommendation.

Multi-modal query processing in knowledge graphs

Multi-modal query processing represents one of the most sophisticated aspects of modern knowledge graph systems. When you submit a query that combines multiple data types (like text and images) or requires retrieving information across different modalities, a complex series of operations takes place behind the scenes.

Query understanding across modalities

The first challenge in multi-modal search is understanding what the user is actually asking for, especially when the query itself contains multiple modalities:

1. Joint embedding spaces: The system must project different data modalities into a common representational space. This allows for direct comparison between, for example, text and images. Models like CLIP (Contrastive Language-Image Pre-training) create joint embedding spaces that enable zero-shot cross-modal retrieval.
2. Cross-modal attention: When you submit a query containing both text and an image, attention mechanisms help the system focus on relevant aspects of each modality. For instance, if you provide an image of a car with a text query about its engine, cross-modal attention helps align these elements.
3. Multi-modal fusion: Early, late, and hybrid fusion techniques combine features from multiple modalities to create unified representations. This allows the system to leverage complementary information across different data types before searching the knowledge graph.

Cross-modal matching and retrieval algorithms

Once the system understands your query, it needs to find relevant matches across the knowledge graph:

1. Two-stage retrieval: Most systems employ a two-stage approach:
   • First, they retrieve an initial set of candidates using efficient indexing methods like approximate nearest neighbor search.
   • Then, they re-rank these candidates using more sophisticated cross-modal matching techniques.
2. Cross-modal matching: Several techniques ensure accurate matching across modalities:
   • Similarity metrics in joint embedding spaces.
   • Cross-attention between query and candidate embeddings.
   • Late fusion of uni-modal matching scores.
3. Hybrid indexing: To efficiently handle queries across multiple modalities, systems use specialized indexing structures:
   • Multi-index hashing.
   • Cross-modal hashing.
   • Heterogeneous graph indexing.

For example, when you search with both text and an image, the system might first encode both into the joint embedding space, retrieve candidates based on combined similarity, and then perform a more detailed matching process.

Similarity computation and ranking

The final challenge is determining which results are most relevant and how to present them:

1. Learning to rank: Machine learning approaches optimize multi-modal result ranking:
   • LambdaMART for listwise ranking.
   • RankNet for pairwise ranking.
2. These incorporate multi-modal features and various relevance signals.
3. Diversity-aware ranking: When retrieving heterogeneous results, systems often use methods to ensure diversity:
   • xQuAD for explicit query aspect diversification.
   • PM-2 for proportionality-based diversification.
4. Modality-specific relevance: The system must account for different notions of relevance across modalities:
   • Text: Semantic similarity, query term matching.
   • Images: Visual similarity, object detection.
   • Video: Temporal relevance, scene matching.

A practical example might involve a healthcare knowledge graph where a clinician uploads an X-ray image with an accompanying text query. The system would encode both the image and text into a joint representation, retrieve candidate nodes from the knowledge graph, and rank them based on multi-modal relevance. The results might include relevant medical literature, similar case studies, and appropriate treatment protocols—all drawn from different modalities but presented in a coherent, ranked list.

These sophisticated techniques for query processing across different modalities are what make multi-modal knowledge graphs so powerful for complex information retrieval tasks requiring the integration of diverse data types.

Result integration and presentation

The effective integration and presentation of results from different modalities is essential for usable multi-modal search systems. Key approaches include:

• Unified result pages that blend different result types into a single ranked list
• Faceted navigation allowing users to filter or pivot across modalities
• Multi-modal snippets that combine various data types to summarize results effectively

For ranking diverse multi-modal results, systems typically employ learning-to-rank approaches, diversity-aware ranking methods, and modality-specific relevance considerations to ensure the most valuable content rises to the top, regardless of format.

Companies like Pinterest demonstrate the power of effective multi-modal result presentation through their visual search capability, which combines region-based convolutional networks, embedding fusion from various attributes, and sophisticated re-ranking based on engagement signals.

Unlock next-generation multi-modal search with Hypermode

Multi-modal knowledge graphs offer revolutionary capabilities, empowering organizations to integrate, query, and reason across diverse data types seamlessly. Yet, effectively aligning and retrieving data across text, images, audio, and video requires specialized infrastructure and sophisticated embedding capabilities. Traditional databases and isolated vector stores fall short in delivering the truly unified, scalable experience demanded by multi-modal applications.

Hypermode is purpose-built to address exactly these challenges. Its AI-native platform, powered by Dgraph, supports native multi-modal embeddings, advanced vector indexing, and cross-modal search capabilities. Hypermode seamlessly integrates multi-modal data into a single, coherent knowledge graph—allowing you to effortlessly perform complex, modality-spanning queries with exceptional accuracy and performance.

Are you ready to unlock the full potential of your data by breaking down modality barriers?
Start building your multi-modal knowledge graph with Hypermode today and experience firsthand the future of integrated, intelligent search.