Graphs to Graph Neural Networks: From Fundamentals to Applications — Part 2b: Knowledge Graphs
Isaac: In this post, I will continue learning about knowledge graphs. You can find the first part of this here.
11- What is the difference between a semantic network and a knowledge graph?
A semantic network and a knowledge graph are both methods of representing knowledge in a structured way. They are related concepts, but there are some key differences between the two:
- Structure:
Semantic network: A semantic network represents concepts and their relationships as a graph, with nodes representing concepts or entities and edges representing relationships or connections between those concepts. The relationships can be hierarchical, associative, or other types, and are typically labeled with semantic roles, such as “is-a” or “part-of.”
Knowledge graph: A knowledge graph is a more advanced form of a semantic network, which also represents knowledge as a graph with nodes and edges. However, knowledge graphs are typically more expressive and allow for the representation of more complex relationships, including properties and attributes of the entities. Knowledge graphs often employ a schema or an ontology to define the structure of the graph and the types of relationships that can exist.
2. Expressiveness:
Semantic network: Semantic networks are usually less expressive than knowledge graphs, as they primarily focus on representing relationships between concepts. They may not be able to capture more complex relationships or properties of entities.
Knowledge graph: Knowledge graphs are more expressive, allowing for representation of not only relationships between entities but also properties and attributes of the entities themselves. This makes knowledge graphs better suited for representing more detailed and nuanced information.
3. Usage:
Semantic network: Semantic networks are commonly used in cognitive psychology, linguistics, and artificial intelligence as a way to represent and reason about knowledge. They can be used for various tasks, such as natural language understanding, reasoning, and knowledge-based systems.
Knowledge graph: Knowledge graphs are widely used in many domains, including the semantic web, information retrieval, and data integration. They are employed by large-scale systems, like Google’s Knowledge Graph and DBpedia, to enhance search results, provide relevant information to users, and enable reasoning and inference over structured data.
In summary, while both semantic networks and knowledge graphs represent knowledge in a structured way, knowledge graphs are typically more expressive and allow for more complex relationships to be captured. Semantic networks are simpler and primarily focus on representing relationships between concepts, while knowledge graphs also include properties and attributes of the entities being represented.
Isaac: This answer on Quora is also good to read.
12- How are knowledge graphs used in the context of the Semantic Web?
Knowledge graphs play a vital role in the context of the Semantic Web, which is an extension of the traditional web that aims to make the information on the web more meaningful and machine-readable. The Semantic Web enables computers to understand and process the information available on the web more efficiently, making it easier to integrate, discover, and reuse data across different sources.
In the context of the Semantic Web, knowledge graphs are used for the following purposes:
1. Data Representation: Knowledge graphs allow data to be represented in a structured and machine-readable format. The Semantic Web relies on standardized languages, such as RDF (Resource Description Framework) and OWL (Web Ontology Language), to represent and define knowledge graphs. This standardized representation facilitates data integration and sharing across different sources on the web.
2. Linking and Integration: Knowledge graphs enable linking and integration of data from different sources, as they provide a common vocabulary to describe entities and their relationships. This capability allows for the creation of a more interconnected and unified web of knowledge, where information from different domains can be combined and queried seamlessly.
3. Ontologies and Inference: In the Semantic Web, ontologies are used to define the structure of knowledge graphs and the types of relationships that can exist between entities. Ontologies enable computers to reason about the data and infer new information based on the relationships and rules defined in the ontology. Knowledge graphs provide the foundation for reasoning and inference in the Semantic Web.
4. Information Retrieval and Querying: Knowledge graphs support advanced querying and information retrieval techniques, such as SPARQL (an RDF query language), which enable users and applications to extract relevant and accurate information from the Semantic Web. This improves search capabilities and helps users find the information they need more efficiently.
5. Data Reuse and Interoperability: By representing data in a standardized and machine-readable format, knowledge graphs facilitate data reuse and interoperability. This enables developers and researchers to build applications that can access and leverage data from multiple sources on the Semantic Web, promoting collaboration and innovation.
In summary, knowledge graphs play a central role in the Semantic Web by providing a structured, machine-readable format for representing data and enabling the integration, discovery, and reuse of information across different sources. They also support advanced querying and reasoning capabilities, which are essential for realizing the vision of a more intelligent and interconnected web of knowledge.
13- How do companies like Google and Microsoft use knowledge graphs?
Companies like Google and Microsoft use knowledge graphs extensively to enhance their products and services, leveraging the structured and interconnected nature of knowledge graphs to provide more relevant and personalized information to users. Some of the ways these companies use knowledge graphs include:
1. Search and Information Retrieval: Knowledge graphs help improve search results by understanding the relationships between entities and the context in which they appear. For example, Google’s Knowledge Graph helps provide direct answers to users’ queries, display rich snippets, and offer related information, such as images, facts, and figures about the searched entity. This leads to more accurate, relevant, and context-aware search results.
2. Personalization and Recommendations: Knowledge graphs can be used to provide personalized content and recommendations based on users’ interests, preferences, and browsing history. By understanding the relationships between entities and the user’s preferences, companies can offer more tailored suggestions, such as movie or music recommendations, news articles, or shopping suggestions.
3. Natural Language Understanding: Knowledge graphs aid in natural language understanding by providing structured information about entities, their properties, and relationships. This allows companies to build AI systems that can better understand user queries and generate more relevant responses, as seen in voice assistants like Google Assistant or Microsoft’s Cortana.
4. Data Integration and Knowledge Management: Companies use knowledge graphs to integrate and manage vast amounts of structured and unstructured data from various sources, such as websites, databases, and user-generated content. Knowledge graphs help connect different data sources, enabling companies to provide a more comprehensive view of the information and derive insights from the data.
5. AI and Machine Learning: Knowledge graphs provide a valuable source of information for AI and machine learning algorithms, allowing them to learn and reason about the relationships between entities and their properties. This enables companies to develop advanced AI applications that can perform tasks such as question-answering, fact-checking, or automated decision-making.
Some specific examples of how Google and Microsoft use knowledge graphs include:
- Google’s Knowledge Graph: Launched in 2012, Google’s Knowledge Graph is a large-scale knowledge base that contains billions of entities and their relationships. It is used to provide direct answers to users’ queries, display rich snippets, suggest related topics, and improve the overall search experience.
- Microsoft Academic Graph (MAG): Microsoft has developed an extensive knowledge graph called Microsoft Academic Graph, which contains information about millions of academic publications, authors, institutions, and other research-related entities. MAG is used to power the Microsoft Academic search engine, which provides users with access to research papers, authors, and institutions.
In summary, companies like Google and Microsoft use knowledge graphs to enhance their products and services, improving search results, enabling personalization, supporting natural language understanding, and aiding in data integration and knowledge management. Knowledge graphs also provide valuable information for AI and machine learning applications, helping to develop more advanced and intelligent systems.
14- What are some open-source knowledge graph projects and tools?
There are several open-source knowledge graph projects and tools available for building, managing, and querying knowledge graphs. Some of the most popular ones include:
1. Apache Jena: A Java-based framework for building and managing RDF-based knowledge graphs. It provides a robust set of tools for parsing, querying, and manipulating RDF data, as well as support for the SPARQL query language and OWL reasoning.
2. RDF4J: Formerly known as Sesame, RDF4J is a Java-based framework for building and processing RDF data. It includes APIs for parsing, querying, and storing RDF data, and supports SPARQL and RDFS/OWL reasoning.
3. Virtuoso: An open-source high-performance RDF triple store and knowledge graph management system developed by OpenLink Software. Virtuoso supports RDF, RDFS, OWL, and SPARQL, as well as other data formats like XML, JSON, and relational databases.
4. Neo4j: A popular open-source graph database management system that can be used to build knowledge graphs. Neo4j is based on the property graph model and provides an expressive query language called Cypher, which can be used to query and manipulate the graph data.
5. ArangoDB: A multi-model open-source database that supports graph data model along with document and key-value data models. ArangoDB can be used to build knowledge graphs and supports its own query language called AQL (ArangoDB Query Language) for querying and manipulating the graph data.
6. Dgraph: A fast, distributed, and horizontally scalable graph database that can be used to build knowledge graphs. Dgraph is written in Go and supports GraphQL as its query language.
7. GraphDB: An RDF triple store developed by Ontotext, GraphDB is designed for building, managing, and querying knowledge graphs. It supports RDF, RDFS, OWL, and SPARQL and offers advanced reasoning and inferencing capabilities.
8. DBpedia: A community-driven project that extracts structured data from Wikipedia and makes it available as a large-scale knowledge graph. DBpedia can be used as a source of information for building custom knowledge graphs or for enriching existing ones.
9. Wikidata: A free, collaborative, and multilingual knowledge base that serves as the structured data source for Wikimedia projects like Wikipedia. Wikidata provides a vast amount of structured data that can be used to build and enhance knowledge graphs.
10. OpenRefine: A powerful open-source tool for data cleaning, transformation, and reconciliation, OpenRefine can be used to preprocess and prepare data for building knowledge graphs. It also supports connecting and aligning data with external knowledge graphs like Wikidata.
These open-source projects and tools provide a solid foundation for building, managing, and querying knowledge graphs. By leveraging these resources, developers and researchers can create and enhance their own knowledge graphs, facilitating better data integration, analysis, and decision-making.
15- What are some popular knowledge graph datasets and benchmarks?
Several popular knowledge graph datasets and benchmarks are available for research and evaluation purposes. These datasets and benchmarks help researchers and developers assess the performance of knowledge graph construction, completion, and reasoning algorithms. Some of the most popular ones include:
1. DBpedia: DBpedia is a large-scale, community-driven knowledge graph that extracts structured data from Wikipedia. It covers a wide range of domains and provides data in RDF format, making it suitable for various knowledge graph tasks.
2. Freebase: Freebase was a large, collaborative knowledge graph containing information about millions of entities across diverse domains. Although the project was discontinued in 2016, its data dumps are still widely used in knowledge graph research and experiments.
3. YAGO: YAGO (Yet Another Great Ontology) is a knowledge graph that combines information from Wikipedia, WordNet, and GeoNames. It has a strong focus on accuracy and provides detailed provenance information, making it suitable for various knowledge graph tasks.
4. Wikidata: Wikidata is a free, collaborative, and multilingual knowledge base that serves as the structured data source for Wikimedia projects like Wikipedia. Its extensive and diverse data makes it an ideal resource for knowledge graph research and experiments.
5. NELL: The Never-Ending Language Learning (NELL) dataset is an ongoing project at Carnegie Mellon University. It consists of a knowledge graph that is automatically constructed and extended by a machine learning system. The dataset covers a wide range of categories and relations, making it suitable for various knowledge graph tasks.
6. WordNet: WordNet is a lexical database of English words organized into synsets (sets of synonyms) and linked by various semantic relations. While not a traditional knowledge graph, WordNet is often used in knowledge graph research for tasks such as entity linking and relation prediction.
7. OpenKE: OpenKE is an open-source toolkit for knowledge graph embedding, which includes various popular knowledge graph datasets like FB15k, FB15k-237, WN18, and WN18RR. These datasets are subsets of Freebase and WordNet and are widely used for evaluating knowledge graph embedding algorithms.
8. Kinship and UMLS: Kinship and UMLS (Unified Medical Language System) are two popular benchmark datasets for evaluating knowledge graph reasoning algorithms. Kinship is a small dataset containing genealogical information, while UMLS is a larger dataset derived from a biomedical ontology.
9. LUBM: The Lehigh University Benchmark (LUBM) is a synthetic benchmark dataset designed for evaluating the performance of Semantic Web repositories. It consists of a university domain ontology and a set of randomly generated RDF data conforming to that ontology.
These popular knowledge graph datasets and benchmarks cover various domains and tasks, providing valuable resources for evaluating and comparing the performance of different knowledge graph construction, completion, and reasoning algorithms.
16- What is deep learning on knowledge graphs, and how is it different from traditional methods?
Deep learning on knowledge graphs refers to the application of deep learning techniques, such as neural networks, to learn patterns and representations from knowledge graph data. These methods are designed to address various tasks related to knowledge graphs, including entity and relation prediction, link prediction, knowledge graph embedding, and knowledge graph completion.
Traditional methods for knowledge graph tasks typically rely on handcrafted features, logic-based reasoning, or shallow machine learning techniques. In contrast, deep learning methods learn complex and expressive representations from the data automatically, without the need for manual feature engineering. This difference makes deep learning approaches more scalable and adaptable to a wide range of knowledge graph tasks and datasets.
Here are some key differences between deep learning on knowledge graphs and traditional methods:
1. Representation Learning: Deep learning methods are designed to automatically learn meaningful representations of entities and relations in a knowledge graph. These representations, often referred to as embeddings, can capture complex patterns and relationships in the data. Traditional methods, on the other hand, usually rely on manually crafted features or simple vector-based representations.
2. Model Complexity: Deep learning models, such as neural networks, can learn highly complex and non-linear relationships between entities and relations in a knowledge graph. This enables them to capture more nuanced patterns and generalize better to unseen data. Traditional methods, like logic-based reasoning or shallow machine learning techniques, may struggle to model such complex relationships.
3. Scalability: Deep learning methods are highly scalable, making them suitable for large-scale knowledge graphs with millions or billions of entities and relations. Traditional methods often face challenges when dealing with such large-scale data, as they may require extensive manual feature engineering or may not scale well computationally.
4. End-to-End Learning: Deep learning methods can be trained end-to-end, learning both the representations and the task-specific model parameters simultaneously. This allows for better optimization and adaptation to the specific task at hand. Traditional methods, in contrast, typically involve separate stages of feature engineering and model training.
Some popular deep learning techniques applied to knowledge graphs include:
- Knowledge Graph Embedding: Methods such as TransE, DistMult, and ComplEx learn low-dimensional vector representations of entities and relations that capture the structure and semantics of the knowledge graph.
- Graph Neural Networks (GNNs): GNNs are a class of deep learning models specifically designed for graph-structured data. They can be used to learn embeddings and perform various tasks on knowledge graphs, such as node classification, link prediction, and graph completion.
- Convolutional Neural Networks (CNNs) and Recurrent Neural Networks (RNNs): These deep learning models have also been adapted for knowledge graph tasks, such as relation extraction from text and sequence-based knowledge graph completion.
In summary, deep learning on knowledge graphs involves the application of deep learning techniques to learn complex representations and relationships from knowledge graph data. These methods are distinct from traditional methods, as they automatically learn meaningful representations, model complex relationships, and scale well to large-scale datasets.
Isaac: Here is one image from Wikipedia that shows the concept:
17- How are graph neural networks used in deep learning on knowledge graphs?
Graph Neural Networks (GNNs) are a class of deep learning models specifically designed for graph-structured data, such as knowledge graphs. They are used to learn meaningful representations of entities (nodes) and relationships (edges) in the graph, which can then be used for various tasks related to knowledge graphs. GNNs are particularly suited for deep learning on knowledge graphs because they can effectively capture both local and global graph structure and propagate information across the graph.
Some common ways GNNs are used in deep learning on knowledge graphs include:
1. Node Representation Learning: GNNs can be used to learn node embeddings, which are low-dimensional vector representations of entities in a knowledge graph. These embeddings can capture the graph structure and entity attributes, making them useful for various downstream tasks such as entity classification, clustering, or similarity search.
2. Link Prediction: GNNs can be used for predicting missing or potential links between entities in a knowledge graph. By learning embeddings for nodes and edges, GNNs can measure the likelihood of a relationship between two entities based on their embeddings and the graph structure. This helps in knowledge graph completion and identifying potential new relationships.
3. Node Classification: GNNs can be used to classify nodes in a knowledge graph based on their attributes and the graph structure. For example, GNNs can be employed to classify entities into different categories, such as people, organizations, or events, based on the relationships they have with other entities in the graph.
4. Graph Classification: GNNs can also be used to classify entire graphs or subgraphs based on their structure and properties. For example, GNNs can be employed to identify similar subgraphs within a knowledge graph or to classify different knowledge graphs based on their overall structure and content.
5. Relation Prediction: GNNs can be used to predict the type of relationship between entities in a knowledge graph. By learning to represent both entities and relations, GNNs can predict the most likely relation type given a pair of entities and the graph context.
6. Knowledge Graph Reasoning: GNNs can be used for reasoning tasks on knowledge graphs, such as inferring missing facts or answering complex queries. By capturing the graph structure and propagating information across the graph, GNNs can help identify patterns and make inferences based on the existing knowledge in the graph.
Some popular GNN architectures used for deep learning on knowledge graphs include:
- Graph Convolutional Networks (GCNs): A foundational GNN architecture that uses convolution-like operations to aggregate information from a node’s neighbors in the graph.
- Graph Attention Networks (GATs): A GNN architecture that uses attention mechanisms to weigh the contributions of neighboring nodes when aggregating information, allowing the model to focus on relevant neighbors for each node.
- GraphSAGE (Graph Sample and Aggregate): A GNN architecture that uses a sampling strategy to scale to large graphs by aggregating information from a fixed-size neighborhood of each node.
- Relational Graph Convolutional Networks (R-GCNs): An extension of GCNs that incorporates edge (relation) information in the aggregation step, making it particularly suitable for knowledge graphs with labeled edges.
In summary, Graph Neural Networks are used in deep learning on knowledge graphs to learn meaningful representations of entities and relationships, and to perform various tasks such as link prediction, node classification, graph classification, relation prediction, and reasoning. GNNs are effective in capturing both local and global graph structure and propagating information across the graph, making them well-suited for knowledge graph applications.
18- What is the role of embeddings in deep learning on knowledge graphs?
Embeddings play a crucial role in deep learning on knowledge graphs. Embeddings are low-dimensional, continuous vector representations of entities (nodes) and relationships (edges) in a knowledge graph. They are designed to capture the graph’s structure, semantics, and complex patterns in a dense and compact form. The main purpose of embeddings is to transform the discrete and sparse graph data into a continuous and dense vector space, which can be more easily processed and analyzed by machine learning algorithms, particularly deep learning models.
Here are some key roles of embeddings in deep learning on knowledge graphs:
1. Representation Learning: Embeddings provide a way to learn meaningful representations of entities and relationships in a knowledge graph. These representations can capture complex patterns and relationships in the data, which can be used for various downstream tasks such as entity classification, clustering, or similarity search.
2. Model Input: Embeddings serve as input features for deep learning models, such as Graph Neural Networks (GNNs), Convolutional Neural Networks (CNNs), and Recurrent Neural Networks (RNNs). These models take the embeddings as input and learn to perform various tasks on knowledge graphs, such as link prediction, node classification, or graph classification.
3. Similarity and Distance Measures: Embeddings can be used to measure the similarity or distance between entities and relationships in a knowledge graph. By comparing the embeddings of two entities or relationships, one can determine their similarity or relatedness, which can be useful for tasks such as entity linking, recommendation, or query expansion.
4. Information Compression: Embeddings help compress the information contained in a knowledge graph into a lower-dimensional space, making it more manageable for computation and storage. This is particularly important when dealing with large-scale knowledge graphs with millions or billions of entities and relationships.
5. Transfer Learning: Embeddings learned from one knowledge graph or task can be used as a starting point for learning on a different knowledge graph or task. This transfer learning approach can help leverage pre-existing knowledge and reduce the amount of data and training time required for a new task.
Various embedding techniques have been proposed for deep learning on knowledge graphs, including:
- Knowledge Graph Embedding: Methods such as TransE, DistMult, and ComplEx learn embeddings for entities and relationships by modeling their interactions and aiming to preserve the graph structure in the embedding space.
- Graph Neural Networks (GNNs): GNNs, like Graph Convolutional Networks (GCNs) and Graph Attention Networks (GATs), learn embeddings for nodes (entities) by aggregating and propagating information from their neighbors in the graph.
- Pre-trained Language Models: Techniques like BERT and its variants can be fine-tuned to learn embeddings for entities in a knowledge graph based on their textual descriptions or context.
In summary, embeddings play a critical role in deep learning on knowledge graphs as they provide a way to learn meaningful representations, serve as input features for deep learning models, enable similarity and distance measures, facilitate information compression, and support transfer learning.
19- What are some common deep learning architectures used for knowledge graph tasks?
Several deep learning architectures have been developed and adapted for various knowledge graph tasks, such as representation learning, link prediction, relation prediction, and node classification. Some of the most common deep learning architectures used for knowledge graph tasks include:
1. Knowledge Graph Embedding Models: These models learn embeddings for entities and relations by modeling their interactions in the knowledge graph. Some popular knowledge graph embedding models include:
- TransE: Translates entity embeddings in the relation-specific direction to model relationships.
— DistMult: Models relationships as element-wise multiplication of entity and relation embeddings.
— ComplEx: Extends DistMult by incorporating complex-valued embeddings to capture asymmetric relations.
— RotatE: Models relations as rotations in the complex vector space of entity embeddings.
2. Graph Neural Networks (GNNs): GNNs are designed to learn embeddings for nodes (entities) and edges (relations) by aggregating and propagating information from their neighbors in the graph. Some popular GNN architectures for knowledge graph tasks are:
- Graph Convolutional Networks (GCNs): Applies convolution-like operations to aggregate information from neighboring nodes.
— Graph Attention Networks (GATs): Uses attention mechanisms to weigh the contributions of neighboring nodes when aggregating information.
— Relational Graph Convolutional Networks (R-GCNs): Extends GCNs by incorporating edge (relation) information in the aggregation step.
— GraphSAGE (Graph Sample and Aggregate): Scales to large graphs by aggregating information from a fixed-size neighborhood of each node using a sampling strategy.
3. Convolutional Neural Networks (CNNs): CNNs, originally designed for image processing, have been adapted to work with graph-structured data and textual information for tasks like relation extraction and entity recognition. For example:
- Text-based Relation Extraction: CNNs can be used to learn embeddings for entities and relations from their textual descriptions or context, which can then be used for tasks like relation prediction or entity linking.
4. Recurrent Neural Networks (RNNs) and their variants (LSTMs, GRUs): RNNs, designed for sequence data, can also be adapted for knowledge graph tasks, particularly when working with textual information or sequential patterns in the graph. For example:
- Sequence-based Knowledge Graph Completion: RNNs can be used to model sequential patterns in knowledge graphs and predict missing links or relations based on the observed sequences of entities and relations.
These deep learning architectures can be used individually or combined in various ways to address different knowledge graph tasks. By learning complex and expressive representations of entities and relations, these models can capture the rich structure and semantics of knowledge graphs, leading to improved performance and generalization across a wide range of tasks.
20- How do deep learning methods address the incompleteness and uncertainty of knowledge graphs?
Deep learning methods address the incompleteness and uncertainty of knowledge graphs by learning expressive and robust representations of entities and relations, as well as by modeling complex patterns and relationships in the graph. These methods can help predict missing or potential links, infer new facts, and improve the overall quality of the knowledge graph. Here are some ways deep learning methods address these challenges:
1. Link Prediction: Deep learning models, such as knowledge graph embedding models and graph neural networks (GNNs), can be used to predict missing or potential links between entities in a knowledge graph. By learning embeddings for entities and relations, these models can measure the likelihood of a relationship between two entities based on their embeddings and the graph structure. This helps to complete the knowledge graph and identify potential new relationships.
2. Relation Prediction: Deep learning methods can also be used to predict the type of relationship between entities in a knowledge graph, addressing uncertainty in the relations. By learning to represent both entities and relations, these models can predict the most likely relation type given a pair of entities and the graph context.
3. Knowledge Graph Reasoning: Deep learning models, particularly GNNs, can be used for reasoning tasks on knowledge graphs, such as inferring missing facts or answering complex queries. By capturing the graph structure and propagating information across the graph, these models can help identify patterns, make inferences, and address uncertainty based on the existing knowledge in the graph.
4. Robust Embeddings: Deep learning methods can learn robust and expressive embeddings for entities and relations that capture complex patterns and relationships in the graph. These embeddings can better handle uncertainty and incompleteness in the knowledge graph, as they can generalize better to unseen data and provide meaningful representations even when some information is missing.
5. Incorporating Auxiliary Information: Deep learning models can incorporate auxiliary information, such as textual descriptions or other external data sources, to enhance the representation learning process and address incompleteness in the knowledge graph. For example, pre-trained language models like BERT can be fine-tuned to learn embeddings for entities based on their textual descriptions, which can then be used for various knowledge graph tasks.
6. Transfer Learning: Deep learning models can leverage pre-existing knowledge learned from one knowledge graph or task and apply it to a different knowledge graph or task. This transfer learning approach can help improve the model’s performance on incomplete or uncertain data by leveraging prior knowledge.
In summary, deep learning methods address the incompleteness and uncertainty of knowledge graphs by learning expressive and robust representations of entities and relations, modeling complex patterns and relationships, predicting missing links and relation types, reasoning over the graph, incorporating auxiliary information, and leveraging transfer learning. These approaches help to improve the overall quality and completeness of the knowledge graph and enable better generalization and performance on a wide range of tasks.
Isaac: That’s enough for this post. We’ll continue in the next post.
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