Semantic Graph Completion Overview

• Semantic graph completion is the process of inferring missing facts in knowledge graphs by utilizing rich semantic signals from texts, neighborhood configurations, and logical rules.
• It employs advanced methodologies such as transformer-based encodings, semantic-aware message passing, and joint language-structure embeddings to improve prediction accuracy.
• Empirical benchmarks demonstrate significant gains in metrics like Hits@1 and MRR, highlighting its effectiveness in addressing data sparsity and unseen entities.

Semantic graph completion is the process of inferring missing facts in a graph where nodes and edges are equipped not only with structure but also rich semantic signals, such as textual context, neighborhood configurations, or logical knowledge. Unlike classical graph completion that considers only graph topology, semantic graph completion leverages contextual, textual, and semantic cues within and beyond the graph structure to enable more accurate and generalizable prediction of missing entities and relationships.

1. Foundations of Semantic Graph Completion

Semantic graph completion extends traditional knowledge graph completion (KGC), in which the goal is to predict missing triples $(h, r, t)$ in a graph $G = (E, R, T)$, by explicitly incorporating semantic information into the modeling and inference process. Semantic information is sourced variously from neighborhood subgraphs, textual descriptions, type hierarchies, or pre-trained LLMs.

Traditional embedding-based models such as TransE or ComplEx operate on structured triples and learn representations solely from structural patterns. While effective on densely connected graphs, these methods struggle with unseen entities, data sparsity, or when the semantics of nodes and relations are not fully encoded in the graph structure (Gul et al., 15 Dec 2024).

Semantic graph completion seeks to alleviate these shortcomings by:

• Incorporating local and global graph context (e.g., neighbor sets, incident relations).
• Utilizing external semantics such as natural language descriptions and textual entailment.
• Aggregating multi-hop reasoning paths and composing semantic signals along them.
• Attending to only the most relevant information to suppress noise and over-smoothing effects.

2. Key Methodologies and Model Architectures

Semantic graph completion encompasses a wide range of modeling paradigms, from contextualized LLMs to semantic-aware message passing. Representative approaches include:

A. Contextualized Transformer-Based Models

CAB-KGC (Gul et al., 15 Dec 2024) improves on BERT-based KGC by injecting graph-structural context into the model input. For each query $(h, r, ?)$, the input sequence to BERT concatenates:

• The query head $h$
• The union of $h$’s incident relations and neighbors ($H_c$)
• The query relation $r$
• The set of all entity pairs connected by $r$ ($R_c$)

The input is tokenized and processed through BERT, and the model uses the [CLS] output for classification over all candidates. The cross-entropy objective is computed over the entire entity vocabulary (no negative sampling required):

$$P(t|h, r) = \mathrm{softmax}\!\bigl(W\,\mathrm{BERT}([h; H_c;\, r; R_c])\bigr)_t$$

Empirical ablations show that both head context ($H_c$) and relation context ($R_c$) contribute complementary gains, with full context yielding the best Hits@1 and MRR.

B. Semantic-Aware Relational Message Passing

SARMP (Li et al., 29 Jun 2025) advances GNN-based KGC by introducing a semantic-aware top-K neighbor selection mechanism. For node $v$ at layer $l$, only the K most semantically relevant edges (scored in a learned latent space) are selected for aggregation. A specialized multi-head attention module fuses these edges with the node’s feature. This mechanism prevents information dilution and over-smoothing, improving focus on salient context. The final node states are used jointly with learned relation embeddings to compute the plausibility of each triplet.

C. Joint Language-Structure Embedding

LaSS (Shen et al., 2022) demonstrates that fusing LLM semantics with structural constraints yields robust KG completion. Each triple is linearized to text and embedded with a pre-trained LM. A translational loss enforces structured patterns ($\vec{h} + \vec{r} \approx \vec{t}$), promoting the LM’s representation to organize around the graph’s latent geometry. Negative sampling is used to approximate the computationally heavy full softmax during training.

D. Neuro-symbolic and Logical Frameworks

KeGNN (Werner et al., 2023) overlays fuzzy-logic based knowledge enhancement layers atop standard GNNs. Domain knowledge, such as class-homophily or logical rules, is encoded as differentiable constraints that are integrated into the update equations, allowing the end-to-end model to learn both structural and logical dependencies within semantic graphs.

E. Path-based Reasoning with Semantic Statements

Lan et al. (Lan et al., 2021) address medical KG sparsity by encoding each multi-hop path as a natural language sentence, which is then fed into BERT. This approach enables dense, shared semantic representations of rarely observed paths, and attention-based aggregation over these embeddings enables high-precision relation prediction even in settings with severe path or entity sparsity.

3. Semantic Context Engineering and Input Design

The construction and selection of context is a critical component:

• Neighbor Context Extraction: Collecting immediate neighborhoods (incident relations and entities), global relation occurrences, or sampled multi-hop paths.
• Semantic Scoring and Pruning: Top-K selection mechanisms (Li et al., 29 Jun 2025, Li et al., 29 Jun 2025) filter only the most semantically relevant neighbors, suppressing noisy signals and preventing the collapse that arises when too many paths are considered indiscriminately.
• Attention Aggregation: Attention modules fuse node features with semantically filtered neighbors, adaptively weighting structural vs. semantic information.
• Input Linearization: For LLMs, sequences are constructed concatenating main, neighbor, and relational tokens and encoded with learned embedding tables (tokens, positions, segments).
• Hybridization with Structural Features: Models may concatenate static entity/relation embeddings with semantic vectors to combine strengths (Lan et al., 2021, Werner et al., 2023).

4. Training Objectives, Losses, and Optimization

The prevailing objectives are tailored to both the structure and semantics:

• Full-Vocabulary Cross-Entropy: Treat each prediction as multiclass classification over the entity set (as in CAB-KGC (Gul et al., 15 Dec 2024)), obviating the need for negative sampling.
• Margin-Based Negative Sampling: Sample corrupted candidate triples and impose a margin loss (SARMP (Li et al., 29 Jun 2025), LaSS (Shen et al., 2022)).
• Semantic Contrastive Loss: For architectures using semantic networks (AESI-KGC (Ji et al., 2023)), contrastive objectives maximize separation between positive and negative attention scores.
• Logical/Fuzzy Regularization: Neuro-symbolic models enforce satisfaction of logical constraints via fuzzy t-conorms or differentiable “clause enhancers” (KeGNN (Werner et al., 2023)).
• Combined Parameterization: Most approaches combine these losses with L2 regularization or weight decay on parameters.

5. Empirical Benchmarks and Comparative Results

Semantic graph completion models are benchmarked extensively:

Model	Dataset	MRR	Hits@1	Hits@3
CAB-KGC	FB15k-237	0.350	0.322	0.399
(prev SOTA)	FB15k-237	0.358	0.266	0.394
CAB-KGC	WN18RR	0.685	0.637	0.687
(prev SOTA)	WN18RR	0.674	0.596	0.671

Cab-KGC shows a +5.3% absolute improvement in Hits@1 on FB15k-237 and +4.88% on WN18RR over the previous best. SARMP (Li et al., 29 Jun 2025) achieves 0.492/0.440 MRR/Hits@1 on FB15k-237 and 0.535/0.493 on WN18RR, matching or exceeding recent baselines. Computationally, CAB-KGC trains to convergence in 2–3 hours on a single RTX 3090 and achieves inference speeds of ~500 queries/sec.

Ablation studies across architectures demonstrate the necessity of full context injection. For example, removing both $H_c$ and $R_c$ from CAB-KGC incurs an ~4% drop in Hits@1; partial recovery is observed with only head or relation contexts. SARMP’s ablations confirm the necessity of both Top-K filtering (–0.019 MRR if omitted) and metric-based semantic similarity scoring.

6. Limitations, Trade-offs, and Deployment Considerations

• Computational Cost: Transformer-based models and context-aware encodings are significantly more resource-intensive than lightweight embeddings, but eliminating negative sampling and entity descriptions (as in CAB-KGC) partially offsets this.
• Context Windowing: Both memory and speed scale with neighborhood/context window size; pruning is essential to tractability.
• Negative Sampling: Full softmax approaches obviate negative sampling but require large classification heads and more memory; sample-based negatives reduce compute but risk undersampling hard negatives.
• Generalization to Unseen Entities: Contextual semantic models (e.g., those incorporating BERT or fine-tuned transformers) are empirically more robust to unseen or infrequent entities compared to pure structure-based models.
• Scalability: On very large graphs, context extraction and input sequence construction may become the bottleneck; practical solutions use sparse dynamic batching and neighbor sampling.

7. Significance and Research Directions

Semantic graph completion is reshaping knowledge graph reasoning by bridging the symbolic/graph-based and distributional/semantic paradigms. Advances in contextual encoding, discriminative context aggregation, and hybrid symbolic-neural architectures continue to set new benchmarks in KGC. Notably, context-aware transformers that dynamically encode neighborhood and relation-level signals directly into the model’s input sequence currently deliver the best trade-off between semantic fidelity and computational scalability.

Emerging research directions include:

• Adaptive, query-aware context selection beyond static top-K thresholds.
• Joint modeling of multi-hop paths, type constraints, and textual entailments.
• Neuro-symbolic and logic-enhanced regularization for improved interpretability.
• Efficient context construction and inference for ultra-large graphs.
• Investigation of strong semantic priors, especially for settings with high out-of-vocabulary or unseen entities.

Semantic graph completion thus marks a convergence of structural, textual, and contextual paradigms within machine reasoning and continues to enable more reliable, generalizable, and interpretable knowledge graph inference.