Knowledge Graph Construction from Text
Also known as: knowledge graph, KG construction, Bilgi Grafiği Oluşturma (Knowledge Graph)
Knowledge graph construction is a text-mining pipeline that turns unstructured text into a structured graph of entities and the relations between them. Drawing on the synthesis of Hogan et al. (2021) and the relational-machine-learning review of Nickel et al. (2016), it represents knowledge as nodes (entities such as people, places, organisations) connected by labelled edges (relations), and serves semantic search, recommendation systems, and reasoning.
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When to use it
Use knowledge graph construction when you have text data and want to expose the entities and relations it contains as a structured, queryable graph for semantic search, recommendation, or reasoning. It assumes that named-entity-recognition and relation-extraction results are available, and an entity-linking infrastructure is recommended to merge duplicate mentions. A corpus of at least around 30 documents is the practical minimum for a useful graph.
Strengths & limitations
- Turns unstructured text into an explicit, queryable structure of entities and relations.
- Supports semantic search, recommendation systems, and reasoning over connected facts.
- Surfaces relationships that span multiple sentences or documents, not just single statements.
- Quality depends entirely on the upstream named-entity-recognition and relation-extraction results being available and accurate.
- Without entity linking, duplicate and ambiguous entities fragment the graph.
- Building and maintaining the pipeline is moderately demanding and not a one-step procedure.
Frequently asked
What is a knowledge graph?
It is a structured representation of knowledge as a graph: entities such as people, places, and organisations are nodes, and the relations between them are labelled edges. It captures facts as connected subject-relation-object structures rather than as free text.
What inputs does the construction pipeline need?
It assumes named-entity-recognition and relation-extraction results are already available, since those supply the nodes and edges. An entity-linking infrastructure is recommended so that different mentions of the same thing map to one node.
Why is entity linking important?
Entity linking disambiguates mentions and merges duplicates so that every reference to the same real-world entity collapses onto a single node. Without it, the graph fragments into duplicate and ambiguous nodes that weaken search, recommendation, and reasoning.
What is a knowledge graph used for?
Once built, the graph supports semantic search, recommendation systems, and reasoning — letting a system follow links between entities to answer questions and surface relationships that no single sentence states directly.
Sources
- Hogan, A. et al. (2021). Knowledge Graphs. ACM Computing Surveys, 54(4), 1-37. DOI: 10.1145/3447772 ↗
- Nickel, M. et al. (2016). A Review of Relational Machine Learning for Knowledge Graphs. Proceedings of the IEEE, 104(1), 11-33. DOI: 10.1109/JPROC.2015.2483592 ↗
How to cite this page
ScholarGate. (2026, June 1). Knowledge Graph Construction from Text. ScholarGate. https://scholargate.app/en/text-mining/knowledge-graph-nlp
Which method?
Set this method beside its closest kin and read them side by side — the library lays the books on the table; the choice is yours.
- Entity LinkingText mining↔ compare
- Named Entity RecognitionText mining↔ compare
- Relation ExtractionText mining↔ compare