Useful Datasets for Graph KB

what would be some useful data sets that would be good to use for graph knowledge bases to load into a platform like I already built: Search for articles, authors, or concepts... Search Reset Markdown Export Selected Article Author Concept Ingest New Article Article Title Article Content Article URL Ingest Article Failed to load graph data. Please try again later.

Since your platform involves searching for articles, authors, and concepts within a graph-based knowledge base, useful datasets should be structured to enhance entity relationships and semantic search. Here are some datasets that could be valuable: ### **General Knowledge & Academic Research** 1. **ArXiv Metadata** – Academic papers with author affiliations, citations, and topics. - [https://www.kaggle.com/Cornell-University/arxiv](https://www.kaggle.com/Cornell-University/arxiv) 2. **MAG (Microsoft Academic Graph) or OpenAlex** – Author, paper, citation, and topic relationships. - [https://www.openalex.org/](https://www.openalex.org/) 3. **Semantic Scholar Open Research Corpus** – Large-scale academic dataset with NLP-enhanced metadata. - [https://allenai.org/data/s2orc](https://allenai.org/data/s2orc) 4. **CORD-19** – COVID-19 and medical research dataset (useful for biosciences). - [https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge](https://www.kaggle.com/allen-institute-for-ai/CORD-19-research-challenge) ### **News & Journalism** 5. **Common Crawl News Dataset** – A massive dataset of online news articles. - [https://commoncrawl.org/](https://commoncrawl.org/) 6. **GDELT (Global Database of Events, Language, and Tone)** – News event tracking globally with sentiment analysis. - [https://www.gdeltproject.org/](https://www.gdeltproject.org/) 7. **MediaCloud** – Open-source news dataset covering global publications. - [https://mediacloud.org/](https://mediacloud.org/) ### **Encyclopedic & Entity-Based Datasets** 8. **DBpedia** – Extracted knowledge base from Wikipedia. - [https://www.dbpedia.org/](https://www.dbpedia.org/) 9. **Wikidata** – Structured data about entities, linked with Wikipedia and other sources. - [https://www.wikidata.org/](https://www.wikidata.org/) 10. **ConceptNet** – A semantic network connecting words and phrases with meanings. - [https://conceptnet.io/](https://conceptnet.io/) ### **Web & Social Media Knowledge Graphs** 11. **Reddit Pushshift Dataset** – Reddit comment and post data for tracking discussions. - [https://files.pushshift.io/reddit/](https://files.pushshift.io/reddit/) 12. **Twitter Academic API** – Relationship graph of tweets, hashtags, and users. - [https://developer.twitter.com/en/docs/twitter-api](https://developer.twitter.com/en/docs/twitter-api) ### **Law & Government Knowledge Bases** 13. **CourtListener (RECAP)** – Free law database with court cases, citations, and legal entities. - [https://www.courtlistener.com/](https://www.courtlistener.com/) 14. **Legis-Graph** – Congressional bills and voting records in graph format. - [https://github.com/JeffreyBLewis/congressional-record](https://github.com/JeffreyBLewis/congressional-record) ### **Business & Financial Knowledge Graphs** 15. **Crunchbase Open Data Map** – Business, startup, and funding relationships. - [https://data.crunchbase.com/](https://data.crunchbase.com/) 16. **Refinitiv PermID** – Corporate entity graph for financial research. - [https://permid.org/](https://permid.org/) 17. **OpenCorporates** – Database of company relationships globally. - [https://opencorporates.com/](https://opencorporates.com/) Since your platform already has a search and ingestion function, you might benefit from **preprocessing these datasets into entity-relationship graphs** with tools like Neo4j, NetworkX, or ChromaDB. Would you like help designing an ingestion pipeline to structure these datasets into your knowledge base?

how would I use this: https://data.commoncrawl.org/projects/hyperlinkgraph/cc-main-2024-25-nov-dec-jan/index.html and the program I just made the ai research assistant?

The Common Crawl's host- and domain-level web graphs, such as the one from November 2024 to January 2025, provide extensive data on interconnections between websites. These graphs represent nodes (hosts or domains) and edges (hyperlinks), offering a macroscopic view of the web's structure. citeturn0search0 To integrate this data into your AI research assistant platform, consider the following steps: 1. **Data Acquisition**: - **Access the Dataset**: Download the desired web graph from Common Crawl's official releases. citeturn0search2 - **Understand the Format**: Familiarize yourself with the graph's structure, typically comprising nodes (hostnames/domains) and edges (hyperlinks). 2. **Data Processing**: - **Parsing the Graph**: Utilize tools or scripts to parse the graph data, extracting relevant nodes and edges. - **Filtering**: Focus on specific domains, topics, or regions pertinent to your research assistant's scope. - **Enrichment**: Augment the graph with metadata, such as page titles, content summaries, or publication dates, by cross-referencing with other datasets or performing targeted crawls. 3. **Integration into Your Platform**: - **Database Selection**: Choose a graph database (e.g., Neo4j) or a search-optimized database (e.g., Elasticsearch) to store and query the processed data. - **Data Ingestion**: Develop pipelines to import the processed graph data into your chosen database, ensuring efficient indexing for rapid search and retrieval. - **Feature Development**: Enhance your platform's capabilities by leveraging the integrated data to provide functionalities like: - **Content Discovery**: Recommend related articles or authors based on hyperlink connections. - **Trend Analysis**: Identify emerging topics or influential domains within the dataset. - **Semantic Search**: Improve search accuracy by understanding the context and relationships between entities. 4. **Continuous Updates**: - Regularly update your dataset by incorporating newer web graphs to maintain the relevance and accuracy of your platform's insights. By systematically processing and integrating the Common Crawl web graph data, your AI research assistant can offer enriched content discovery and more nuanced insights into the interconnected web landscape.