Datasets:

Jerrylife
/

WikiDBGraph

	---
	license: cc-by-4.0
	task_categories:
	- tabular-classification
	- tabular-regression
	language:
	- en
	tags:
	- chemistry
	- medical
	- finance
	size_categories:
	- 10B<n<100B
	---

	# WikiDBGraph Dataset

	This document provides an overview of the datasets associated with the research paper on WikiDBGraph, a large-scale graph of interconnected relational databases.

	Description:
	WikiDBGraph is a novel, large-scale graph where each node represents a relational database, and edges signify identified correlations or similarities between these databases. It is constructed from 100,000 real-world-like databases derived from Wikidata. The graph is enriched with a comprehensive set of node (database) and edge (inter-database relationship) properties, categorized into structural, semantic, and statistical features.

	Source:
	WikiDBGraph is derived from the WikiDBs corpus (see below). The inter-database relationships (edges) are primarily identified using a machine learning model trained to predict database similarity based on their schema embeddings, significantly expanding upon the explicitly known links in the source data.

	Key Characteristics:
	* Nodes: 100,000 relational databases.
	* Edges: Millions of identified inter-database relationships (the exact number depends on the similarity threshold $\tau$ used for graph construction).
	* Node Properties: Include database-level structural details (e.g., number of tables, columns, foreign keys), semantic information (e.g., pre-computed embeddings, topic categories from Wikidata, community IDs), and statistical measures (e.g., database size, total rows, column cardinalities, column entropies).
	* Edge Properties: Include structural similarity (e.g., Jaccard index on table/column names, Graph Edit Distance based metrics), semantic relatedness (e.g., cosine similarity of embeddings, prediction confidence), and statistical relationships (e.g., KL divergence of shared column distributions).

	Usage in this Research:
	WikiDBGraph is the primary contribution of this paper. It is used to:
	* Demonstrate a methodology for identifying and representing inter-database relationships at scale.
	* Provide a rich resource for studying the landscape of interconnected databases.
	* Serve as a foundation for experiments in collaborative learning, showcasing its utility in feature-overlap and instance-overlap scenarios.

	Availability:
	* Dataset (WikiDBGraph): The WikiDBGraph dataset, including the graph structure (edge lists for various thresholds $\tau$) and node/edge property files, will be made publicly available. (Please specify the URL here, e.g., Zenodo, GitHub, or your project website).
	* Code: The code used for constructing WikiDBGraph from WikiDBs, generating embeddings, and running the experiments will be made publicly available. (Please specify the URL here, e.g., GitHub repository).

	License:
	* WikiDBGraph Dataset: Creative Commons Attribution 4.0 International (CC BY 4.0).
	* Associated Code: Apache License 2.0.

	How to Use WikiDBGraph:
	The dataset is provided as a collection of files, categorized as follows:

	A. Graph Structure Files:
	These files define the connections (edges) between databases (nodes) for different similarity thresholds ($\tau$).
	* `filtered_edges_threshold_0.93.csv`, `filtered_edges_threshold_0.94.csv`, `filtered_edges_threshold_0.96.csv`:
	* Meaning: CSV files representing edge lists. Each row typically contains a pair of database IDs that are connected at the specified similarity threshold.
	* How to Load: Use standard CSV parsing libraries (e.g., `pandas.read_csv()` in Python). These edge lists can then be used to construct graph objects in libraries like NetworkX (`nx.from_pandas_edgelist()`) or igraph.
	* `filtered_edges_0.94_with_confidence.csv`:
	* Meaning: An edge list in CSV format for $\tau=0.94$, which also includes the confidence score (similarity score) for each edge.
	* How to Load: Similar to other CSV edge lists, using `pandas`. The confidence score can be used as an edge weight.
	* `graph_raw_0.93.dgl`, `graph_raw_0.94.dgl`, `graph_raw_0.96.dgl`:
	* Meaning: These are graph objects serialized in the Deep Graph Library (DGL) format for different thresholds. They likely contain the basic graph structure (nodes and edges).
	* How to Load `.dgl` files: DGL provides functions to save and load graph objects. You would typically use:
	```python
	import dgl
	# Example for loading a graph
	graphs, _ = dgl.load_graphs('graph_raw_0.94.dgl')
	g = graphs[0] # Typically, load_graphs returns a list of graphs
	```
	* `graph_with_properties_0.94.dgl`:
	* Meaning: A DGL graph object for $\tau=0.94$ that includes node and/or edge properties directly embedded within the graph structure. This is convenient for direct use in DGL-based graph neural network models.
	* How to Load: Same as other `.dgl` files using `dgl.load_graphs()`. Node features can be accessed via `g.ndata` and edge features via `g.edata`.

	B. Node Property Files:
	These CSV files provide various features and metadata for each database node.
	* `database_embeddings.pt`:
	* Meaning: A PyTorch (`.pt`) file containing the pre-computed embedding vectors for each database. These are crucial semantic features.
	* How to Load: Use `torch.load('database_embeddings.pt')` in Python with PyTorch installed.
	* `node_structural_properties.csv`:
	* Meaning: Contains structural characteristics of each database (e.g., number of tables, columns, foreign key counts).
	* `column_cardinality.csv`:
	* Meaning: Statistics on the number of unique values (cardinality) for columns within each database.
	* `column_entropy.csv`:
	* Meaning: Entropy values calculated for columns within each database, indicating data diversity.
	* `data_volume.csv`:
	* Meaning: Information regarding the size or volume of data in each database (e.g., total rows, file size).
	* `cluster_assignments_dim2_sz100_msNone.csv`:
	* Meaning: Cluster labels assigned to each database after dimensionality reduction (e.g., t-SNE) and clustering (e.g., HDBSCAN).
	* `community_assignment.csv`:
	* Meaning: Community labels assigned to each database based on graph community detection algorithms (e.g., Louvain).
	* `tsne_embeddings_dim2.csv`:
	* Meaning: 2-dimensional projection of database embeddings using t-SNE, typically used for visualization.
	* How to Load CSV Node Properties: Use `pandas.read_csv()` in Python. The resulting DataFrames can be merged or used to assign attributes to nodes in a graph object.

	C. Edge Property Files:
	These CSV files provide features for the relationships (edges) between databases.
	* `edge_embed_sim.csv`:
	* Meaning: Contains the embedding-based similarity scores (EmbedSim) for connected database pairs. This might be redundant if confidence is in `filtered_edges_..._with_confidence.csv` but could be a global list of all pairwise similarities above a certain initial cutoff.
	* `edge_structural_properties_GED_0.94.csv`:
	* Meaning: Contains structural similarity metrics (potentially Graph Edit Distance or related measures) for edges in the graph constructed with $\tau=0.94$.
	* How to Load CSV Edge Properties: Use `pandas.read_csv()`. These can be used to assign attributes to edges in a graph object.

	D. Other Analysis Files:
	* `distdiv_results.csv`:
	* Meaning: Likely contains results from a specific distance or diversity analysis performed on the databases or their embeddings. The exact nature would be detailed in the paper or accompanying documentation.
	* How to Load: As a CSV file using `pandas`.

	Detailed instructions on the specific schemas of these CSV files, precise content of `.pt` and `.dgl` files, and example usage scripts will be provided in the code repository and full dataset documentation upon release.

	---
	license: cc-by-4.0
	task_categories:
	- tabular-classification
	- tabular-regression
	language:
	- en
	tags:
	- chemistry
	- medical
	- finance
	size_categories:
	- 10B<n<100B
	---

	# WikiDBGraph Dataset

	This document provides an overview of the datasets associated with the research paper on WikiDBGraph, a large-scale graph of interconnected relational databases.

	Description:
	WikiDBGraph is a novel, large-scale graph where each node represents a relational database, and edges signify identified correlations or similarities between these databases. It is constructed from 100,000 real-world-like databases derived from Wikidata. The graph is enriched with a comprehensive set of node (database) and edge (inter-database relationship) properties, categorized into structural, semantic, and statistical features.

	Source:
	WikiDBGraph is derived from the WikiDBs corpus (see below). The inter-database relationships (edges) are primarily identified using a machine learning model trained to predict database similarity based on their schema embeddings, significantly expanding upon the explicitly known links in the source data.

	Key Characteristics:
	* Nodes: 100,000 relational databases.
	* Edges: Millions of identified inter-database relationships (the exact number depends on the similarity threshold $\tau$ used for graph construction).
	* Node Properties: Include database-level structural details (e.g., number of tables, columns, foreign keys), semantic information (e.g., pre-computed embeddings, topic categories from Wikidata, community IDs), and statistical measures (e.g., database size, total rows, column cardinalities, column entropies).
	* Edge Properties: Include structural similarity (e.g., Jaccard index on table/column names, Graph Edit Distance based metrics), semantic relatedness (e.g., cosine similarity of embeddings, prediction confidence), and statistical relationships (e.g., KL divergence of shared column distributions).

	Usage in this Research:
	WikiDBGraph is the primary contribution of this paper. It is used to:
	* Demonstrate a methodology for identifying and representing inter-database relationships at scale.
	* Provide a rich resource for studying the landscape of interconnected databases.
	* Serve as a foundation for experiments in collaborative learning, showcasing its utility in feature-overlap and instance-overlap scenarios.

	Availability:
	* Dataset (WikiDBGraph): The WikiDBGraph dataset, including the graph structure (edge lists for various thresholds $\tau$) and node/edge property files, will be made publicly available. (Please specify the URL here, e.g., Zenodo, GitHub, or your project website).
	* Code: The code used for constructing WikiDBGraph from WikiDBs, generating embeddings, and running the experiments will be made publicly available. (Please specify the URL here, e.g., GitHub repository).

	License:
	* WikiDBGraph Dataset: Creative Commons Attribution 4.0 International (CC BY 4.0).
	* Associated Code: Apache License 2.0.

	How to Use WikiDBGraph:
	The dataset is provided as a collection of files, categorized as follows:

	A. Graph Structure Files:
	These files define the connections (edges) between databases (nodes) for different similarity thresholds ($\tau$).
	* `filtered_edges_threshold_0.93.csv`, `filtered_edges_threshold_0.94.csv`, `filtered_edges_threshold_0.96.csv`:
	* Meaning: CSV files representing edge lists. Each row typically contains a pair of database IDs that are connected at the specified similarity threshold.
	* How to Load: Use standard CSV parsing libraries (e.g., `pandas.read_csv()` in Python). These edge lists can then be used to construct graph objects in libraries like NetworkX (`nx.from_pandas_edgelist()`) or igraph.
	* `filtered_edges_0.94_with_confidence.csv`:
	* Meaning: An edge list in CSV format for $\tau=0.94$, which also includes the confidence score (similarity score) for each edge.
	* How to Load: Similar to other CSV edge lists, using `pandas`. The confidence score can be used as an edge weight.
	* `graph_raw_0.93.dgl`, `graph_raw_0.94.dgl`, `graph_raw_0.96.dgl`:
	* Meaning: These are graph objects serialized in the Deep Graph Library (DGL) format for different thresholds. They likely contain the basic graph structure (nodes and edges).
	* How to Load `.dgl` files: DGL provides functions to save and load graph objects. You would typically use:
	```python
	import dgl
	# Example for loading a graph
	graphs, _ = dgl.load_graphs('graph_raw_0.94.dgl')
	g = graphs[0] # Typically, load_graphs returns a list of graphs
	```
	* `graph_with_properties_0.94.dgl`:
	* Meaning: A DGL graph object for $\tau=0.94$ that includes node and/or edge properties directly embedded within the graph structure. This is convenient for direct use in DGL-based graph neural network models.
	* How to Load: Same as other `.dgl` files using `dgl.load_graphs()`. Node features can be accessed via `g.ndata` and edge features via `g.edata`.

	B. Node Property Files:
	These CSV files provide various features and metadata for each database node.
	* `database_embeddings.pt`:
	* Meaning: A PyTorch (`.pt`) file containing the pre-computed embedding vectors for each database. These are crucial semantic features.
	* How to Load: Use `torch.load('database_embeddings.pt')` in Python with PyTorch installed.
	* `node_structural_properties.csv`:
	* Meaning: Contains structural characteristics of each database (e.g., number of tables, columns, foreign key counts).
	* `column_cardinality.csv`:
	* Meaning: Statistics on the number of unique values (cardinality) for columns within each database.
	* `column_entropy.csv`:
	* Meaning: Entropy values calculated for columns within each database, indicating data diversity.
	* `data_volume.csv`:
	* Meaning: Information regarding the size or volume of data in each database (e.g., total rows, file size).
	* `cluster_assignments_dim2_sz100_msNone.csv`:
	* Meaning: Cluster labels assigned to each database after dimensionality reduction (e.g., t-SNE) and clustering (e.g., HDBSCAN).
	* `community_assignment.csv`:
	* Meaning: Community labels assigned to each database based on graph community detection algorithms (e.g., Louvain).
	* `tsne_embeddings_dim2.csv`:
	* Meaning: 2-dimensional projection of database embeddings using t-SNE, typically used for visualization.
	* How to Load CSV Node Properties: Use `pandas.read_csv()` in Python. The resulting DataFrames can be merged or used to assign attributes to nodes in a graph object.

	C. Edge Property Files:
	These CSV files provide features for the relationships (edges) between databases.
	* `edge_embed_sim.csv`:
	* Meaning: Contains the embedding-based similarity scores (EmbedSim) for connected database pairs. This might be redundant if confidence is in `filtered_edges_..._with_confidence.csv` but could be a global list of all pairwise similarities above a certain initial cutoff.
	* `edge_structural_properties_GED_0.94.csv`:
	* Meaning: Contains structural similarity metrics (potentially Graph Edit Distance or related measures) for edges in the graph constructed with $\tau=0.94$.
	* How to Load CSV Edge Properties: Use `pandas.read_csv()`. These can be used to assign attributes to edges in a graph object.

	D. Other Analysis Files:
	* `distdiv_results.csv`:
	* Meaning: Likely contains results from a specific distance or diversity analysis performed on the databases or their embeddings. The exact nature would be detailed in the paper or accompanying documentation.
	* How to Load: As a CSV file using `pandas`.

	Detailed instructions on the specific schemas of these CSV files, precise content of `.pt` and `.dgl` files, and example usage scripts will be provided in the code repository and full dataset documentation upon release.