Project Background:
Multimodal knowledge graphs (MKG) involve integrating diverse data types, such as text, images, videos, and audio, into a unified knowledge representation framework. The background of such projects involves addressing the limitations of traditional knowledge graphs that primarily rely on textual data and expanding their scope to incorporate information from various modalities. By amalgamating these diverse data types, the project aims to create a richer, more comprehensive knowledge graph that encapsulates a broader spectrum of information. This integration enables a more nuanced and interconnected understanding of entities, concepts, and their relationships, offering a more holistic view of the data. The ultimate objective is to develop methodologies that effectively manage the complexities inherent in diverse data formats, facilitating the creation of context-aware, intelligent systems capable of handling multimodal information for applications such as recommendation systems, search engines, and artificial intelligence. The projects core foundation is converging different data types into a cohesive knowledge graph, promising a more robust and sophisticated understanding of information across various domains.
Problem Statement
- It involves creating a unified representation of information from diverse modalities, such as text, images, audio, and video, within a cohesive knowledge graph framework.
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The challenge lies in integrating and linking these varied data types to establish meaningful relationships and associations across the modalities.
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It addresses data heterogeneity, interoperability, and the sheer volume and complexity of multimodal data.
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Additionally, handling the differing structures and semantics of various modalities and ensuring a consistent and comprehensive representation within the knowledge graph presents a substantial challenge.
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Furthermore, identifying efficient methods to manage and extract meaningful insights from multimodal data, ensuring data quality, and enabling effective retrieval and analysis are central concerns.
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The primary goal is to develop robust techniques and frameworks to create interconnected, context-aware multimodal knowledge graphs that offer a holistic understanding of information across diverse sources.
Aim and Objectives
- To develop interconnected knowledge graphs that seamlessly integrate diverse data modalities like text, images, audio, and video to understand information comprehensively.
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Integrate diverse data types into a unified knowledge graph framework.
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Establish meaningful relationships and associations between different modalities within the knowledge graph.
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Enhance the context-aware representation of information by leveraging multimodal data.
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Develop methods to handle the scale and complexity of diverse data while ensuring computational efficiency.
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Explore and enable applications in recommendation systems, search engines, and AI by leveraging the power of multimodal knowledge graphs.
Contributions to Multimodal Knowledge Graphs
1. Offer a unified representation that encapsulates information from various modalities, providing a more holistic and nuanced understanding of entities, concepts, and their relationships.
2. Enrich the context in which information is represented, allowing for a deeper and more interconnected understanding of data.
3. Enable more sophisticated and context-aware applications like recommendation systems, search engines, and artificial intelligence by incorporating diverse data types for more accurate and informed decision-making.
4. Facilitate the integration of knowledge across domains, enabling cross-disciplinary insights and applications by linking information from various sources.
5. Provide a foundation for leveraging diverse data sources and types, fostering advancements in analysis, interpretation, and utilization across multiple domains.
Deep Learning Algorithms for Multimodal Knowledge Graphs
- Graph Convolutional Networks (GCNs)
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Multimodal Embeddings
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Graph Attention Networks (GAT)
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Variational Graph Autoencoders
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Multimodal Neural Networks
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Graph Recurrent Networks
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Multimodal Transformer Networks
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Graph Neural Networks (GNNs)
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Deep Multimodal Representation Learning
Datasets for Multimodal Knowledge Graphs
- ConceptNet
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WordNet
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YFCC100M
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Visual Genome
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COCO
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Open Images Dataset
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VG Gender Dataset
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NEIL
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LAMBADA
Performance Metrics
- Accuracy
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Recall
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Precision
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F1 Score
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Hit Ratio
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Mean Average Precision (mAP)
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Normalized Discounted Cumulative Gain (NDCG)
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Mean Reciprocal Rank (MRR)
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Area Under the Receiver Operating Characteristic Curve (AUC-ROC)
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Root Mean Squared Error (RMSE)
Software Tools and Technologies
Operating System: Ubuntu 18.04 LTS 64bit / Windows 10
Development Tools: Anaconda3, Spyder 5.0, Jupyter Notebook
Language Version: Python 3.9
Python Libraries:
1. Python ML Libraries:
- Scikit-Learn
- Numpy
- Pandas
- Matplotlib
- Seaborn
- Docker
- MLflow
2. Deep Learning Frameworks:
- Keras
- TensorFlow
- PyTorch
