Question
Why hasn’t the practice of building and sharing graph data networks, such as labelled property graphs or RDF triple-based graphs, become widespread? Specifically, why don’t we see large-scale collaborative graph networks where users share or interlink their graph data?
Answer
There are several interrelated reasons why collaborative graph data networks have not seen widespread adoption, despite the potential of models like labeled property graphs and RDF:
Data Ownership, Privacy, and Trust Issues
- Data sensitivity: Graphs often represent entities and relationships that are inherently personal, proprietary, or confidential (e.g., social networks, business knowledge graphs).
- Lack of trust: Users or organizations are hesitant to expose their data to a shared network due to risks of misuse, surveillance, or loss of competitive advantage.
Lack of Standardized Interoperability
- Multiple formats: RDF, labeled property graphs, and other graph models often use incompatible representations.
- Ontology mismatches: Even within RDF, different datasets may use different vocabularies, making it hard to merge or connect data meaningfully.
- No dominant platform: Unlike Wikipedia for text or GitHub for code, there’s no universally accepted graph-sharing platform.
High Complexity of Integration
- Entity resolution: Aligning entities across graphs (e.g., “IBM” in one dataset vs. “International Business Machines” in another) is complex and error-prone.
- Schema alignment: Different graphs might model the same domain in fundamentally different ways, making integration non-trivial.
- Link maintenance: Keeping links across datasets up-to-date is costly and hard to automate.
Lack of Incentives and Network Effects
- No immediate reward: Unlike social media or open-source software, contributing graph data yields few tangible benefits to individuals or organizations.
- Limited user base: Most graph data applications are niche, academic, or enterprise-focused, so there isn’t a broad community driving adoption.
Tooling and Usability Challenges
- Steep learning curve: Tools like SPARQL (for RDF) and Cypher (for property graphs) are non-trivial to learn.
- Poor end-user interfaces: Collaborative tools for graph construction and exploration are still relatively immature compared to spreadsheets or wikis.
Historical and Institutional Fragmentation
- Legacy data silos: Much valuable data is locked in legacy systems that are not easily converted to graph form.
- Domain-specific efforts: Where graph sharing happens (e.g., Wikidata, Bio2RDF), it’s typically confined to specific communities and hasn’t spread widely.
Summary
Collaborative graph networks face a “perfect storm” of technical, social, and economic barriers. While the potential is high, broad adoption would require:
- Strong incentives for data sharing,
- Better standards and interoperability,
- Easier-to-use tools,
- And robust privacy-preserving mechanisms.
Efforts like Wikidata, OpenCitations, and Bio2RDF show that collaborative graphs are possible—just not yet generalized across domains.