Knowledge Graphs: Normalization, Deduplication, and Entity Resolution

A knowledge graph is only useful if it accurately represents the world. This may seem straightforward, but many graph projects struggle here. For example, the same customer might appear under different names, or a company might appear in several systems with slightly different addresses.

A scientific concept might appear in text in several different ways, and the same relationship can be described in many similar forms. If these variations are added to a graph without careful handling, the result is a cluttered structure with duplicate nodes, broken identities, and relationships that seem more complex than they are. Both research and practical guides agree that building, refining, and managing identity are essential for graph quality, not just minor cleanup tasks.

This is why normalization, deduplication, and entity resolution should be considered core parts of building a graph. To achieve this, a typical data-cleaning process involves aligning schemas, narrowing down potential matches, identifying which records refer to the same entity, and then merging or standardizing them. In summary, normalization makes records easier to compare, deduplication removes duplicates, and entity resolution determines which records that look different actually describe the same thing.

What Normalization Means In Graph Building

In graph projects, normalization is the behind-the-scenes work that enables everything else. It involves aligning fields from different sources, ensuring values follow the same structure, standardizing formats, and reducing accidental differences so matching systems can compare similar items. For example, ‘Robert Smith’ in one system and ‘Bob Smith’ in another, or ‘123 Main Street’ and ‘123 Main St,’ should not become separate nodes just because their names look different.

The entity resolution literature consistently places cleaning and standardization before matching, and recent work on knowledge graph construction treats data integration and canonicalization as required steps in transforming heterogeneous input into a usable graph.

Normalization alone does not solve the identity problem. However, it plays a key role by removing noise, allowing the matching process to focus on real similarities. As a result, differences in address format, capitalization, punctuation, abbreviations, or field layout should not create fake entities. Ultimately, the more carefully normalization is done, the more reliable and explainable the matching results will be for data stewards and experts.

Deduplication Is The First Identity Problem, Not The Last

Deduplication is the simplest form of entity resolution. When matching occurs within a single data source, it is usually called deduplication or duplicate detection. While this sounds simple, it is not always easy in practice. Records in the same database can still differ because of typos, missing information, inconsistent abbreviations, or partial updates. If these duplicates are not resolved in a graph, a single real-world entity may appear as multiple nodes, reducing the effectiveness of search, analytics, and reasoning.

This is why deduplication should not be seen as just deleting obvious duplicates. Removing exact matches is easy, but building a graph often requires more advanced comparisons to catch the harder cases. This work is important because duplicate records can cause problems in later data processing, mining, and analysis. What seems like a small data issue can turn into a bigger structural problem in the graph.

Entity Resolution Is Where The Graph Learns Who Is Who

Entity resolution goes beyond deduplication and has a bigger impact. It involves extracting uncertain references from one or more data sources, identifying the unique real entities, and mapping each reference to the correct entity. This means solving two problems: linking records that look different but are the same, and separating records that look similar but are actually different. For example, a minor spelling change can mask a real match, while almost identical names can mask a false one.

This is why good entity resolution does more than just say whether two records match. In graph construction, entity resolution typically leads to a later step in which the system decides how to represent the resolved entity. First, the system identifies which records belong together; then it decides which details to keep or merge in the final graph. Research shows that matching and merging are separate steps, since finding a match is distinct from deciding how to combine the information.

The Practical Pipeline: Alignment, Blocking, Matching, Fusion

Most successful graph-building pipelines follow a common pattern. They start by aligning schemas or attributes so fields from different sources can be understood together. Next, they use blocking or indexing to narrow down which records to compare, avoiding the need to check every possible pair.

This shortcut is important because comparing every possible pair of records quickly becomes unmanageable as the number of records grows. The literature states that, with N records, there are N(N−1)/2 possible pairs. Only after reducing the number of candidates does the system compare records and decide which ones should be grouped or linked.

After matching, canonicalization, or fusion, those decisions become the graph’s structure. This usually means creating a single resolved node or a cluster with combined properties, instead of leaving multiple versions of the same entity in the network. Recent research also shows that integration happens at several levels: not only must entities be matched, but ontologies, properties, and concepts must also be aligned to keep the graph consistent as new data comes in.

Why Graphs Make Entity Resolution Harder — And More Valuable

Graph construGraph construction introduces a challenge that flat matching systems often overlook: identity can depend on relationships. In relational or collective entity resolution, resolving one entity can affect how others are resolved, since the surrounding network provides context. This makes graph-based identity work especially valuable. Once duplicate nodes are handled, users see better analytics, clearer visualizations, and stronger machine learning results.

The graph also becomes easier to query and trust, especially when it is not static. Incremental knowledge graph construction cannot afford to rebuild identity from scratch every time a new source appears. Research on incremental multi-source ER for KG completion argues for assigning new entities to existing clusters in ways that reduce dependence on ingestion order, and for lightweight cluster repair to correct earlier mistakes.

Broader KG construction research reaches the same conclusion from a systems perspective: full recomputation becomes wasteful and unscalable as the number of sources and the graph size increase.

Canonicalization Is Not An Afterthought

Many teams believe the difficult work is done after matching records. In reality, canonicalization is one of the most important quality steps in a finished graph. Research shows that both entity names and relationship labels can be redundant or unclear, so they should be normalized to stable forms. It is not enough to merge two names for the same organization if the graph still has several similar relation labels for the same idea. Keeping the graph clean depends on both node identity and consistent relationships.

Recent research using large language models for graph building supports this approach. Instead of using a single prompt, newer methods extract facts, define or find the right schema, and then apply canonicalization afterward.

Studies on staged knowledge graph construction show that direct prompting can lead to inaccurate entities and unclear or wrong relationships, while step-by-step pipelines improve quality and handle bigger schemas better. In short, good graph construction now means extracting, normalizing, aligning, and then resolving, rather than just asking once and accepting the answer.

Choosing The Right Approach

There is no single entity resolution method that works best for every graph project. The field includes rule-based, probabilistic, pairwise, cluster-based, relational, and machine learning approaches. The best choice depends on the data, the risk of errors, the amount of labeled training data available, and the importance of explainability.

For example, a fraud graph, a biomedical knowledge graph, and a product catalog graph might all require different matching strategies, even though their overall processes are similar.

In practice, hybrid systems are often the best choice. Clear, high-confidence cases can be handled with simple rules. More uncertain cases can be scored with statistical or machine learning models. A small but important group of cases may still need human review.

Research on crowdsourced and manual review for entity resolution shows why human input is still valuable. Machine methods speed up the process, but people help resolve ambiguous cases and improve accuracy. In graphs, this is important because entity resolution results affect canonicalization and later analytics, so mistakes can spread throughout the system.

Best Practices For Building A Cleaner, More Trustworthy Knowledge Graph

Normalize before you compare.

Make schema alignment and standardization a top engineering priority. If you compare values that are not aligned or are messy, you force the matching model to handle both formatting and identity issues at once, which usually leads to poorer results.

Keep deduplication, resolution, and fusion separate.

Removing clear duplicates, deciding if two records refer to the same entity, and choosing how to represent the final entity are three related but separate steps. Keeping them apart makes the process easier to adjust and helps with error analysis.

Design for incremental updates

A dynamic graph needs an identity layer that can adapt. Reuse previous mappings, compare new data to existing clusters, and allow for repairs instead of rebuilding the entire graph every time the data changes.

Measure both match quality and scalability.

Entity resolution is not only about getting matches right. Research also focuses on complexity and scalability. Common metrics include precision, recall, and F-measure, but when the graph is large, runtime and operational costs are important too.

Leave room for human judgment.

No matter how good the model is, some cases will still be unclear. Human review is helpful when mistakes are costly, and it can also improve training and evaluation over time.

Conclusion

The most reliable knowledge graphs are not just the largest or those with the most data sources. They are the ones who manage identity well. Normalization makes data comparable, and deduplication removes unnecessary duplicates.

Entity resolution determines what is truly the same. Canonicalization turns those decisions into a stable graph structure. When these steps are built together, the graph becomes a reliable and useful model of the domain, rather than just a collection of connected records.

FAQ

Is deduplication the same as entity resolution?

Not exactly. Deduplication usually handles duplicates within a single source, while entity resolution covers both single-source and multi-source matching, as well as mapping references to the correct real-world entity.

Can modern LLMs replace normalization and canonicalization?

Current research shows that LLMs can help, but they do not replace the need for structured normalization and canonicalization. Recent papers on knowledge graph construction show that step-by-step pipelines work better than single prompts, especially with large schemas or messy relation labels.

Why is incremental entity resolution so important in knowledge graphs?

Because production graphs change over time, it is costly and often unnecessary to rebuild the whole graph for every new data source. Incremental entity resolution and cluster repair allow teams to update the graph while keeping it scalable and consistent.

References

1. L. Zhong et al., A Comprehensive Survey on Automatic Knowledge Graph Construction (ACM Computing Surveys, 2023).

2. A. Hogan et al., Knowledge Graphs (Springer book, 2021).

3. P. Christen, Data Matching: Concepts and Techniques for Record Linkage, Entity Resolution, and Duplicate Detection (book, 2012).

4. P. Christen, A Survey of Indexing Techniques for Scalable Record Linkage and Deduplication (IEEE TKDE, 2012).

5. O. Binette et al., (Almost) all of entity resolution (Science Advances / PMC).

6. A. Saeedi et al., Incremental Multi-source Entity Resolution for Knowledge Graph Completion (2020).

7. M. Hofer et al., Construction of Knowledge Graphs: State and Challenges (2024).

8. B. Zhang and H. Soh, Extract, Define, Canonicalize: An LLM-based Framework for Knowledge Graph Construction (EMNLP 2024 / arXiv).

9. L. Ding et al., Automated Construction of Theme-specific Knowledge Graphs (2024 / arXiv).

10. C. Jiang et al., COMBO: A Complete Benchmark for Open KG Canonicalization (2023 / arXiv).

11. P. Nathan, Entity Resolved Knowledge Graphs: A Tutorial (Neo4j, 2024).

12. Combining entity resolution and knowledge graphs (Linkurious, 2024).