Circuit Semantic-Aware Knowledge Graphs

• The paper presents a framework that integrates multimodal extraction, ontological annotation, and rule-based inference to construct richly annotated circuit knowledge graphs.
• It employs multi-stage normalization and deep learning techniques, achieving high entity-linking accuracy and significant improvements in automated IC design.
• Semantic enrichment enables applications such as retrieval-augmented reasoning, automated circuit design, and precise validation of analog/mixed-signal systems.

Circuit Semantic-Aware Knowledge Graph Construction is a field that leverages formal ontologies, rule-based reasoning, and advanced information extraction to generate richly annotated knowledge graphs (KGs) from circuit diagrams, specifications, netlists, and related multimodal artefacts. The primary aim is to systematically encode both the physical structure and high-level semantics—such as functional roles or operational intent—of electrical, analog/mixed-signal (AMS), or integrated circuit (IC) designs. Semantic-aware KGs support downstream applications in retrieval-augmented reasoning, automated design, validation, and circuit intelligence. Construction frameworks are characterized by domain-specific ontologies, multi-stage graph normalization and enrichment pipelines, learned or rule-based annotation schemes, and formal criteria for evaluating semantic completeness and reasoning utility (Bayer et al., 2022, Shi et al., 7 Nov 2024, Xing et al., 5 Dec 2025).

1. Ontologies and Circuit Graph Schemes

A semantic-aware circuit KG is anchored in a formal ontology specifying entities, relations, attributes, and functional classes relevant to the domain. In (Bayer et al., 2022), the ontology is expressed in RDF and leverages Wikidata Q-IDs to unify physical (e.g., Diode, Resistor) and functional (e.g., FlybackDiode, PullUpResistor) concepts. Key classes include:

• Component (physical device; e.g., Diode, Resistor, Inductor)
• Port (individual terminals; e.g., Anode, Cathode)
• Junction and Crossover (diagram-specific topological elements)
• Function (context-sensitive roles; e.g., FlybackDiode)
• Net, PowerNet, SignalNet (circuit connections and signal semantics)

Properties specify connectivity (w:connects), physical decomposition (w:has_part), typology (rdf:type), and labeling semantics (w:name). AMSnet-KG (Shi et al., 7 Nov 2024) extends this with classes for Testbench, PerformanceMetric, and explicit parameter, annotation, and sizing constraint attributes. For IC specifications, the ChipKG schema (Xing et al., 5 Dec 2025) distinguishes Modules, Signals, RegisterFields, Parameters, and hierarchical/behavioral relations (e.g., "drives," "triggers," "hasParam," "instantiates").

2. Extraction and Raw Graph Construction Pipelines

The construction of a semantic-aware KG proceeds from multimodal sources—CAD schematics, netlists, image-understanding pipelines, and textual specifications:

• Schematic/Image Processing: Symbol centroids, line segments, and junctions are detected, usually with graph-based parsers or object detection (YOLO-V8 in (Shi et al., 7 Nov 2024)).
• Component and Port Mapping: Each detected symbol is assigned an RDF resource of type (e.g., w:Diode) and ports sub-typed as w:PORT, labeled with connectivity and function (e.g., "anode"/"cathode") (Bayer et al., 2022).
• Net and Connection Detection: Flood-filling from pins, followed by clustering of wires, resolves net blocks; intersection detection resolves cases of junction vs. crossing (Shi et al., 7 Nov 2024).
• Normalization: Raw graphs are normalized via forward-chaining rules—making connections undirected, collapsing junctions, and abstracting port-level connects to component-level connects (Bayer et al., 2022).

Structured knowledge from textual specifications is extracted via tokenization, sentence classification, and IR (intermediate representation) extraction, yielding JSON-IR records and candidate triples (Xing et al., 5 Dec 2025).

3. Semantic Enrichment, Functional Annotation, and Rule-Based Inference

Semantic enrichment assigns high-level functional roles to components and annotates architectural structure using rule-based inference or learned models:

• Forward-Chaining Rules: Expert-defined rules operate on normalized graphs to infer functional types. For example, a diode in parallel to an inductor with matching anode/cathode connection is annotated as w:FlybackDiode (Bayer et al., 2022).
• Formal Rule Specification: Rules are encoded in Apache Jena-style or first-order logic (LaTeX), e.g.,

$$\forall d, t_A, t_C, L.\, \left[ \begin{aligned} &\mathrm{Diode}(d) \wedge \mathrm{connects}(d,t_A)\wedge \mathrm{name}(t_A,\text{"anode"}) \ &\wedge \mathrm{connects}(d,t_C)\wedge \mathrm{name}(t_C,\text{"cathode"})\ &\wedge \mathrm{connects}(t_A, L)\wedge \mathrm{connects}(t_C, L)\wedge \mathrm{Inductor}(L) \end{aligned} \right] \rightarrow \mathrm{FlybackDiode}(d).$$

• Semantic Anchoring and Attribute Enrichment: Declarative/procedural sentences map respectively to entity/behavioral nodes and triggers; electrical constraints (timing, voltage) are attached as attributes (Xing et al., 5 Dec 2025).
• Annotation of Performance and Topological Properties: AMSnet-KG annotates nodes and relations with measured or specified performance metrics, testbench results, symmetry constraints, and architectural tags * “Telescopic cascode”, “current mirror”, etc. (Shi et al., 7 Nov 2024).

4. Graph Storage, Query, and Reasoning

Constructed KGs are stored in graph databases or triple-stores and exposed to query, reasoning, or downstream automations:

• Triple-Store Deployment: Jena TDB is used for RDF persistence and rule-based reasoning (Bayer et al., 2022).
• Cypher and SPARQL Queries: KGs are interrogated for components or subgraphs matching criteria, e.g., all flyback diodes:

  SELECT ?d WHERE { ?d rdf:type w:FlybackDiode . }

• Domain-Specific Retrieval: In AMSnet-KG RAG, LLMs propose design strategies, extract triples, and generate Cypher queries to retrieve candidate circuit blocks and testbenches for automated design flows (Shi et al., 7 Nov 2024).
• Augmented Reasoning: ChipMind uses information-theoretic adaptive retrieval and semantic filtering on ChipKG to support fine-grained, intent-aware reasoning over IC specs (Xing et al., 5 Dec 2025).

5. Evaluation Metrics and Empirical Performance

Graph quality and semantic completeness are quantified on multiple axes:

Metric	Definition/Approach	Reference
Precision/Recall (nodes, edges)	Classical set overlap on nodes/edges	(Xing et al., 5 Dec 2025)
Atomic-ROUGE	Fact-level match between extracted and ground-truth	(Xing et al., 5 Dec 2025)
Coverage Score	Fraction of ground-truth multi-hop paths traversed	(Xing et al., 5 Dec 2025)
Overall Graph Quality	Weighted sum of F1 (nodes), F1 (edges), coverage	(Xing et al., 5 Dec 2025)
Linking Accuracy	Prop. of dispatch texts correctly linked in KG	(Li et al., 2023)

In power grid dispatch KGs, entity linking via CNNs with semantic, phonetic, and POS features achieves 91.94% full-match accuracy, greatly exceeding word-based and semantic-only baselines (Li et al., 2023). For ChipKG, end-to-end reasoning over IC specifications yields substantial (>30%) improvement over baselines on long-context benchmarks (Xing et al., 5 Dec 2025). AMSnet-KG entity extraction from PDFs attains 96–97% netlist accuracy after auto-processing, and the RAG loop achieves fully automated op-amp design with simulation-evaluated netlists (Shi et al., 7 Nov 2024).

6. Domain Adaptations and Practical Applications

Semantic-aware KGs are tailored to domain requirements:

• Power Distribution Networks: Domain-specific schemas encode equipment, operation types, states, and topological containment mirroring feeder and transformer hierarchy. Dense embeddings and circuit-constrained candidate filtering address linguistic/semantic ambiguity and synonymy (Li et al., 2023).
• AMS and IC Design Automation: KGs annotate building blocks, simulation context, sizing constraints, and performance targets, supporting RAG workflows for automated netlist generation, optimization, and design iteration (Shi et al., 7 Nov 2024, Xing et al., 5 Dec 2025).
• Legacy Schematic Digitization: The RDF-based approach of (Bayer et al., 2022) generalizes to legacy paper diagrams as well as CAE-derived designs.

A plausible implication is that unified, semantic-rich KGs bridge the gap from raw multimodal artefacts to explainable, automated circuit reasoning, design, and optimization pipelines.

7. Limitations and Future Directions

Although semantic-aware KG construction frameworks demonstrate strong performance, several limitations exist:

• Reliability for Safety-Critical Applications: ~92% entity-linking accuracy may be insufficient for high-assurance domains; interactive disambiguation and dynamic lexicon updates are suggested as enhancements (Li et al., 2023).
• Ontology and Annotation Coverage: Fine-grained architectural and performance annotation relies on expert curation or large LLM-powered pattern extraction, which can be bottlenecks in novel or rapidly evolving domains (Shi et al., 7 Nov 2024).
• Scalability and Semantic Generalization: For industrial-scale IC specifications with deeply nested and procedural semantics, KG construction and inference require robust attribute resolution, coreference handling, and hierarchical merging (Xing et al., 5 Dec 2025).

Ongoing research focuses on augmenting semantic parsers, evolving ontologies, leveraging Transformer-based models for context-rich extraction, and improving active learning or human-in-the-loop design feedback. The open-sourcing of annotated datasets such as AMSnet-KG is poised to accelerate progress in this domain (Shi et al., 7 Nov 2024, Xing et al., 5 Dec 2025).