Definition
Legal dependency mapping is the process of extracting and modeling relationships between legal sources: citations, cross-references, amendments, implementing measures, and hierarchical links. The result is often a graph that helps systems understand how texts connect and which sources control or override others.
Why it matters
- Better retrieval: a query about one article often requires linked definitions or implementing rules.
- Impact analysis: when a source changes, dependencies show what might be affected.
- Explainability: lets users see why a source is relevant (it is cited by / cites / implements).
- Conflict handling: helps detect contradictory or superseded sources.
How it works
Dependency mapping typically combines citation extraction with normalization:
Parse documents -> extract references -> normalize identifiers -> build graph -> use in retrieval and UICommon relationship types:
- “cites” / “is cited by”
- “amends” / “is amended by”
- “implements” / “is implemented by”
- “defines” / “is defined in”
Practical example
A user queries a tax article. The system also retrieves the definition article it depends on, the implementing decree, and the latest amendment. The answer includes citations and a short “dependency path” to justify why those sources were included.
Common questions
Q: Is this the same as a knowledge graph?
A: It can be part of a knowledge graph, but dependency mapping is narrower: it focuses on document-to-document relationships and legal references that affect applicability.
Q: Do you need perfect extraction?
A: No. Even partial graphs add value, as long as references are normalized and the system is clear about confidence.
Related terms
- Knowledge Graph - broader entity and relationship modeling
- Semantic Expansion - dependencies can guide safe expansion
- Authority Ranking Model - authority signals often use citation structure
- Source Conflict Resolution - conflicts can be detected via overrides/amendments
- Multi-Jurisdictional Indexing - dependency rules differ by jurisdiction
References
Manning, Raghavan & Schütze (2008), Introduction to Information Retrieval.