top4grep is a command-line tool for searching academic papers from four top security conferences (NDSS, IEEE S&P, USENIX, CCS). It builds a local SQLite database by querying DBLP via SPARQL and optionally scraping abstracts from publisher websites. The tool supports keyword-based querying with stemming for fuzzy matching and sorts results by year and conference precedence. The architecture separates concerns into distinct modules: database operations (__main__.py, db.py, build_db.py), abstract extraction (abstract.py), and utilities (utils.py). The code demonstrates good practices like SQLAlchemy ORM usage, context managers for resource handling, and the Template Method pattern for conference-specific abstract extraction.
Sparnatural is a client-side, web-component-based visual SPARQL query builder. It allows non-technical users to construct complex SPARQL SELECT queries by selecting classes, properties, and values through an interactive criteria tree, with support for AND/OR, OPTIONAL, NOT EXISTS, aggregated variables, and geospatial filters. The system is configurable via SHACL or OWL shape definitions, which define available entities, properties, widgets, and datasources. It integrates with YASGUI for query editing and result display, and provides multiple built-in widget types (list, autocomplete, tree, map, date range, number, boolean, search). The core architecture is a custom HTML element (`<spar-natural>`) with a component-based UI, event-driven state management (ActionStore), and a SPARQL generation pipeline that converts an internal JSON query format to SPARQL algebra using sparqljs. The codebase demonstrates solid engineering with clean interfaces for specification providers and data providers, but carries technical debt from a legacy OWL configuration path, two query JSON formats (v1 and v13), heavy jQuery/DOM manipulation, and limited test coverage outside the SPARQL translator. It is actively maintained and well-documented, but its tight coupling to SPARQL endpoints and large dependency bundle may limit reuse in non-RDF contexts.
This repository contains the Virtuoso Open-Source multi-model database server, primarily written in C, with extensive client libraries in Java, C#, and PHP. In production, it functions as a relational database, RDF triple store, SPARQL query endpoint, XML processor, and application server (VSPX). The architecture is a monolithic server process with plugin extensions for hosting languages (CLR, Java, PHP) and connectivity via ODBC, JDBC, OLE DB, and ADO.NET. Key entry points include the Virtuoso server binary (built from libsrc/Wi/ and libsrc/Dk/), ODBC driver (binsrc/virtodbc/), JDBC Type 4 driver (libsrc/JDBCDriverType4/), and .NET client (binsrc/VirtuosoClient.Net/). Data flows include SQL parsing and execution via the Wi module, RDF/SPARQL query processing, and WebDAV/HTTP file management through the built-in web server. External dependencies include PCRE, zlib, HTML Tidy (libsrc/Tidy/), and curl. The repository also contains a suite of web applications (ODS) for blogging, bookmarks, calendar, wiki, etc., built on a common SQL-based framework, and a comprehensive test suite covering performance benchmarks (BSBM, LUBM, TPC-D/H). Despite its breadth, the codebase shows signs of age: multiple duplicate Jena driver versions, a legacy Ajax toolkit (OAT), and hardcoded paths in test scripts. It remains actively maintained with regular commits and issue triage.
This repository is a tutorial and research exploration of Graph RAG (Retrieval-Augmented Generation) combining vector databases (Weaviate) and knowledge graphs (RDF/SPARQL). It contains Jupyter notebooks that demonstrate building knowledge graphs from PubMed articles, World Bank documents, and Netflix movies, comparing vector search and structured query performance for retrieval-augmented generation. A Streamlit app provides an interactive demo for medical article search with MeSH term refinement and LLM summarization. The code is primarily educational, with reusable query modules for Weaviate, RDF, and Databricks, but lacks dependency pinning and has hardcoded credentials, limiting reproducibility. The repository is hybrid, with both application (Streamlit app) and data research (notebooks, datasets) elements, but the dominant character is data research as the core value lies in the experimental notebooks and data pipelines.