Incremental Knowledge Graph Construction

Updated 23 August 2025

Incremental knowledge graph construction is a method for continuously updating graphs by integrating new data without full reprocessing.
It employs change detection, targeted updates, and schema evolution to maintain semantic accuracy and system efficiency.
Techniques such as sampling-based inference and incremental embedding updates enhance performance and support robust, scalable operations.

Incremental knowledge graph construction refers to the paradigm of building and maintaining knowledge graphs (KGs) such that new information—whether supplied as raw data, additional triples, or updated schemas—is integrated without reprocessing the entire KG from scratch. This approach is essential for real-world applications requiring timely updates, computational efficiency, and accurate semantic representations across domains ranging from scientific knowledge bases and industrial-scale entity graphs to interactive robotic systems.

1. Foundational Principles of Incremental Knowledge Graph Construction

Incremental KG construction contrasts with one-shot or batch pipelines by explicitly supporting updates, deletions, and schema evolution as data streams or periodic snapshots arrive. The central technical requirements are:

Change detection: Efficient identification of new, modified, or deleted records (typically via delta computation between source snapshots or streaming interfaces).
Targeted updates: Application of transformation, extraction, and inference steps only to the affected portions of the KG.
Schema and ontology evolution: The ability to incorporate new entity types, relations, and constraints as the domain evolves.
Entity and relation resolution: Continuous reconciliation of semantically equivalent or ambiguous concepts to prevent duplication and ensure consistency.
Quality assurance: Assurance that updates maintain or improve semantic coherence, minimize conflicting facts, and enable rollback or auditing.

Enterprise frameworks such as Saga (Ilyas et al., 2022, Ilyas et al., 2023) and DeepDive (Shin et al., 2015), as well as modular research pipelines (Hofer et al., 2023), exemplify practical implementations of these principles.

2. Incremental Inference and Embedding Algorithms

Many KG construction scenarios require not only populating nodes and edges but also supporting probabilistic reasoning, link prediction, and representation learning as new data arrives. State-of-the-art methods focus on scalable, underdetermined inference and continual embedding updates:

Probabilistic Incremental Inference

Sampling-based inference (Shin et al., 2015): DeepDive materializes the factor graph of the current model with samples ("possible worlds") and, upon updates, proposes new samples using an independent Metropolis–Hastings (MH) scheme. Proposals are accepted at a rate determined by the relative change in the factor distributions; this approach is highly efficient when changes are small.
Variational-based inference (Shin et al., 2015): A sparse, approximated factor graph is solved via an optimization (log-determinant maximization with sparsity/structure constraints). This is preferred when updates introduce large distributional changes or the factor graph is inherently sparse.
Rule-based optimizer (Shin et al., 2015): Selection between inference techniques is automated based on the size/nature of updates, structural change, and sample acceptance rates.

Incremental Knowledge Graph Embedding

Architectural and continual learning (Daruna et al., 2021): Novel entities and relations are appended to embedding tables; previously learned representations are typically frozen (Progressive Neural Networks, PNN) or updated with regularization penalties to prevent catastrophic forgetting (L2 or Synaptic Intelligence regularization).
Incremental distillation and hierarchical learning (Liu et al., 7 May 2024): The IncDE approach orders new triples by graph distance and structural centrality; distillation loss ensures new embeddings remain close to their prior representations, weighted by node/edge importance in the evolving structure.
Low-rank adapter mechanisms (Liu et al., 8 Jul 2024): FastKGE incrementally updates only low-rank adapters for newly introduced nodes/relations, with adaptive rank allocation per layer according to entity/edge importance, reducing training cost by up to 68% in large-scale settings while preserving or improving link prediction performance.

A summary of core incremental inference strategies is provided in the table below:

Approach	Update Scope	Key Technique	Optimal When...
Sampling	Local deltas	MH sampling on delta, reuse samples	Update is small, high acceptance rate
Variational	Partial graph	Sparse approx. via log-det optimization	High distributional or structural shift
Distillation	Embedding	Regularize new embed to prior, layer-wise	Catastrophic forgetting is a risk
Low-rank adapter	Embedding	Separate and adapt low-rank new entity blocks	Rapid adaptation with efficiency needed

3. Pipeline Architectures and Production-Grade Incremental Construction

Industrial-scale systems like Saga (Ilyas et al., 2022, Ilyas et al., 2023) and production pipelines discussed in (Hofer et al., 2023) partition the construction process into parallelizable, loosely coupled modules that can be independently updated. The principal modules include:

Ingestion: Pluggable adapters transform diverse source data formats, compute additions/deletions/updates (formally, $Added = S(t_n) \setminus S(t_0)$ , $Deleted = S(t_0) \setminus S(t_n)$ , $Updated = S(t_0) \cap S(t_n) : e_{t_n} \neq e_{t_0}$ ).
Linkage and De-duplication: Entity resolution via parallel correlation clustering, advanced blocking, and ML-based matching ensures high recall while minimizing redundant graph growth.
Fusion and Quality Control: Confidence estimation, provenance tracking, and source reliability scoring are used for fact fusion; incremental view maintenance supports downstream specialized graph views.
Orchestration and Scalability: Distributed, durable logs and modular pipelines support continuous availability and rapid data freshness, with execution times per update step reduced from hours to sub-second latencies in production.

This pipeline architecture supports the incremental integration of streaming or batch data at scales exceeding billions of facts, as demonstrated in Saga's 33× growth in knowledge base size while maintaining accuracy and freshness SLAs (Ilyas et al., 2022).

4. Methods for Incremental Entity Type and Schema Alignment

Semantic heterogeneity—arising from varied property labels, structural schema drift, or different granularities—must be addressed during incremental integration and extension of KGs.

Entity type recognition (Shi, 3 May 2024): Alignment is achieved by combining classic string-based similarity with property-based metrics:
- Horizontal Similarity ( $HS_{KG}(E,p) = w_E(p) \cdot e^{\lambda(1-|K_v|)}$ ) reflects property specificity to a type.
- Vertical Similarity and Informational Similarity account for property sharing across type hierarchies and information gain, respectively.
Machine learning classification: Feature vectors of lexical and property-based metrics are fed to random forest or XGBoost classifiers for schema- and instance-level alignment.
Formal Concept Analysis (FCA): FCA lattices enable a flattening of schemas to handle inheritance and polymorphic property usage.
Assessment metrics: Post-extension quality is rigorously evaluated by metrics such as “Focus,” which quantifies categorization informativeness via cue validities, in addition to standard measures (CMM, DEM, TF-IDF, BM25).

The LiveSchema platform (Shi, 3 May 2024) embodies these methods, providing real-time, modular incremental KG acquisition, management, and quality assessment across various domains.

5. Applications, Performance, and Empirical Findings

Incremental construction directly addresses the demands of systems requiring high data freshness, fast iteration, and adaptability. Key empirical findings include:

DeepDive (Shin et al., 2015): Up to 112× speedup in inference updates and 22× reduction in end-to-end execution time compared to full rerunning; negligible (<1% for high-confidence facts) accuracy loss in practice.
Probabilistic factorization approaches (Kim et al., 2016): Improved mean reciprocal rank and $F_1$ scores on benchmark datasets, validating the importance of compositional and path-structure reasoning for effective incremental knowledge acquisition.
Saga (Ilyas et al., 2023): Large-scale entity linking, fact extraction, and semantic annotation pipelines enable KG construction and real-time serving across both open-domain and personal (on-device) settings.
Entity type recognition frameworks (Shi, 3 May 2024): Achieve precision above 0.82 and competitive $F$ -measures for schema/instance alignment versus state-of-the-art benchmarks.

Applications span open-domain KGs (e.g., integrating web-scale facts), robotics (continual semantic adaptation in HRI), dynamic recommender systems, semantic search, biomedical curation, and more.

6. Methodological Challenges and Open Research Directions

Despite advances, several challenges persist:

Error propagation and pipeline interdependency (Hofer et al., 2023): Early-stage extraction or mapping errors can impede downstream resolution and fusion; robust provenance and feedback mechanisms are necessary.
Ontology and schema evolution: Adding new types/relations may require retroactive re-alignment of previous facts; automated schema enrichment must be balanced with human oversight for critical domains.
Real-time and streaming data: Ensuring consistency, low-latency integration, and quality assurance under streaming updates remains an open technical problem.
Scalability and openness: While some platforms remain closed-source, ongoing efforts to develop unified, modular, and open-is-spec incremental KG toolkits are identified as a research priority.
Unified benchmarks: Quantitative frameworks to measure update latency, degradation, and quality across iterative versions are needed.

Open areas for research include adaptive clustering/blocking, streaming-quality management, enhanced metadata/provenance models, and hybrid architectures blending batch and streaming updates (Hofer et al., 2023).

7. Integration with LLMs and Zero-shot Approaches

Recent advances leverage LLMs for incremental and ontology-grounded KG construction (Kommineni et al., 13 Mar 2024, Feng et al., 30 Dec 2024). These approaches include:

Ontology-driven methods: LLMs generate competency questions and extract relations, matched via vector similarity to Wikidata schemas. Additional properties not in Wikidata are automatically integrated, allowing for KG schema expansion.
Human-in-the-loop and judge LLMs: Evaluation and validation/prompting by LLMs, combined with periodic expert review, support scalable, interpretable KG construction and incremental refinement.
Zero-shot and few-shot extraction: LLMs, equipped with templated prompts and minimal supervision, extract entities and relations incrementally from text, further reducing development latency (e.g., iText2KG (Lairgi et al., 5 Sep 2024), SAC-KG (Chen et al., 22 Sep 2024)).

These developments indicate a trend toward highly modular, ontology-grounded, machine-assisted incremental KG pipelines capable of integrating both structured and unstructured sources with minimal manual intervention.

In summary, incremental knowledge graph construction constitutes a rigorous, multi-faceted research area combining efficient data processing, probabilistic inference, continual learning, schema alignment, and modular pipeline design. Advances are driven by requirements for speed, scalability, and semantic quality, with ongoing innovation around integration with machine learning, automated reasoning, and LLMs across both research and industrial practice.