Controlled vocabularies reduce spelling variation inside one dataset. Integration creates a harder problem: two organizations may divide the same domain differently. “South,” “Northeast,” or “machine learning” can have different boundaries even when the labels match.
A taxonomy is more than a list
A taxonomy contains concepts and hierarchical relationships such as is_a or part_of. Alignment relates concepts across two taxonomies.
Useful relation types include:
| Symbolic idea | Meaning |
|---|---|
| equals | Both concepts cover the same instances |
| includes | A is broader than B |
| is included in | A is narrower than B |
| overlaps | Some, but not all, instances are shared |
| disjoint | No instance can belong to both |
An alignment should state one of these meanings rather than storing a generic “match.”
In plain terms: two folders can have the same name while containing different documents; two different folder names can contain the same documents.
Labels generate candidates, not conclusions
Lexical similarity, synonyms, embeddings, and shared instances can propose candidate mappings. The final relation depends on scope and definition.
For example, one US regional taxonomy may have five regions and another four. Mapping by label alone can miss that one system’s South equals the union of another system’s Southeast and Southwest—or may still not match exactly because state membership differs.
Constraints reveal impossible alignments
Within a taxonomy, sibling concepts may be declared disjoint and children may cover their parent. Cross-taxonomy articulations combine with those constraints. A reasoner can then detect inconsistency or infer relations that were not entered directly.
If evidence is incomplete, several globally consistent alignments may remain. Treat them as possible worlds rather than forcing an arbitrary choice. Additional expert input can eliminate alternatives.
A practical alignment workflow
- Define the scope, version, and intended use of both taxonomies.
- Normalize labels but preserve original identifiers.
- Generate candidate articulations from labels, definitions, and shared instances.
- Ask an expert to choose semantic relations, including uncertainty.
- Run logical consistency checks.
- Inspect inferred relations and remaining possible worlds.
- Publish mappings with provenance, confidence, and version dates.
Mappings are versioned data products. When either taxonomy changes, revalidate the alignment rather than carrying old relations forward silently.
Where alignment differs from entity resolution
Entity resolution asks whether two records denote the same real-world entity. Taxonomy alignment asks how two concept sets relate. Boston in two city tables may be the same entity; Northeast in two regional systems may overlap without being equal.
Review card
- A taxonomy includes concepts plus hierarchical semantics.
- Cross-taxonomy relations should distinguish equality, inclusion, overlap, and disjointness.
- Label similarity proposes candidates but does not prove meaning.
- Logical constraints can infer relations and expose contradictions.
- Ambiguity may require several possible worlds.
- Version alignments and retain expert decisions as provenance.