Project Background:
The domain-specific knowledge graphs and embeddings revolve around leveraging structured data representations to enhance machine learning tasks within specific domains. Knowledge graphs are graphical structures that organize information about entities and relationships, while embeddings are dense vector representations of entities in a latent semantic space. Constructing domain-specific knowledge graphs and learning embeddings to capture rich semantic relationships and contextual information inherent to the domain. These embeddings encode complex domain knowledge computationally efficiently, facilitating tasks such as entity classification, relation prediction, and recommendation systems. Domain-specific knowledge graphs enable the integration of structured and unstructured data sources, allowing machine learning models to leverage textual and relational information for improved performance. This seeks to develop novel techniques for constructing and learning from domain-specific knowledge graphs and embeddings to advance machine learning capabilities within targeted application domains like healthcare, finance, or biology.
Problem Statement
- Machine learning models often lack a deep understanding of domain-specific concepts and relationships, hindering their performance on complex tasks within specific domains.
-
Integrating heterogeneous data sources, such as structured databases and unstructured text documents, poses challenges due to data formats and semantics differences.
- Constructing and learning from large-scale domain-specific knowledge graphs and embeddings may be computationally expensive and resource-intensive, limiting their practical applicability.
- Changes in domain-specific knowledge and relationships over time may lead to concept drift and require continuous updating of knowledge graphs and embeddings to maintain relevance and accuracy.
- The complexity of domain-specific knowledge graphs and embeddings may hinder interpretability, making it difficult to understand and debug model decisions and predictions.
Aim and Objectives
- Enhance machine learning tasks within specific domains by leveraging domain-specific knowledge graphs and embeddings.
- Construct domain-specific knowledge graphs that capture rich semantic relationships and contextual information.
- Learn dense vector representations of entities in the knowledge graphs to encode domain knowledge efficiently.
- Integrate structured and unstructured data sources within the knowledge graph framework to leverage diverse information types.
- Develop scalable techniques for constructing and learning from large-scale domain-specific knowledge graphs and embeddings.
- Address semantic ambiguity and interpretability challenges to ensure the reliability and usability of the learned representations.
- Continuously update knowledge graphs and embeddings to adapt to domain-specific knowledge and relationship changes.
Contributions to Domain-Specific Knowledge Graphs and Embeddings
- Enhancing machine learning models to understand domain-specific concepts and relationships through structured representations.
- Encoding rich semantic connections and contextual information in dense vector representations facilitates more effective machine-learning tasks.
- Integrating structured and unstructured data sources within a unified framework enables models to leverage diverse information types.
- Developing scalable methods for constructing and learning from large-scale domain-specific knowledge graphs and embeddings, improving their practical applicability.
- Addressing interpretability challenges ensures that learned representations are understandable and reliable for decision-making.
- Continuously updating knowledge graphs and embeddings to adapt to domain-specific knowledge and relationship changes, maintaining their relevance and accuracy over time.
Deep Learning Algorithms for Domain-Specific Knowledge Graphs and Embeddings
- Graph Convolutional Networks (GCNs)
-
Graph Attention Networks (GATs)
-
GraphSAGE
-
DeepWalk
-
Node2Vec
-
Graph Autoencoders
-
Graph Neural Networks (GNNs)
-
Hierarchical Graph Pooling Networks
-
Knowledge Graph Convolutional Networks (KGCNs)
Datasets for Domain-Specific Knowledge Graphs and Embeddings
- WordNet
-
Freebase
-
DBpedia
-
Wikidata
-
ConceptNet
-
PubMed
-
Microsoft Academic Graph
-
Gene Ontology
-
DrugBank
-
Unified Medical Language System (UMLS)
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
