ATLAS Knowledge Graph Overview

• ATLAS Knowledge Graph is a large-scale, schema-rich, causally annotated framework built using advanced LLM-driven extraction pipelines.
• It employs sophisticated normalization, semantic clustering, and dynamic schema induction to support multi-layer knowledge integration.
• Graph analytics in ATLAS, including community detection and structural hole analysis, establish new benchmarks in extraction fidelity and scalability.

The ATLAS Knowledge Graph comprises a class of large-scale, schema-rich, and causally annotated knowledge graphs constructed through advanced LLM-driven extraction and conceptualization pipelines. ATLAS spans multiple technical incarnations, including interactive, sensemaking knowledge graphs for meta-science (human-AI interaction evidence synthesis) and autonomously built, web-scale graphs with dynamic schemas for general-purpose knowledge integration tasks. Its development has established new quantitative benchmarks in graph scale, extraction fidelity, schema induction, and practical integration with LLMs.

1. Formal Definition and Structure

ATLAS Knowledge Graphs are formalized as $G=(V, E, A)$, with $V$ the set of nodes (entities), $E \subseteq V \times V$ the set of directed, semantically typed edges (relations), and $A$ the set of attribute mappings associated with both nodes and edges.

• Node Structure: Each node $v \in V$ is endowed with an attribute triple $$v.label = (\mathrm{type}, \mathrm{subtype}, \mathrm{feature})$$. In specialized graphs (e.g., human-AI interaction), “type” partitions the graph into human actors, AI components, and abstract conceptual entities (“co”).
• Embedding and Clustering: Nodes possess high-dimensional embeddings (e.g., $\mathbb{R}^{4096}$ via Qwen3-Embedding-8B). Clusters are induced semantically within type classes using k-means or DBSCAN, supporting both synonym merge and thematic aggregation.
• Edge Typology: Edges encode directional and causal semantics, labeled from the set $$\{\mathrm{INCREASES}, \mathrm{DECREASES}, \mathrm{INFLUENCES}\}$$ and further tagged by “net outcome” (positive, negative, neutral, undetermined).

A distinguishable feature is explicit schema and taxonomy, dynamically induced or human-aided, that stratify types, subtypes, and features, supporting multi-level querying and subgraph retrieval.

2. Extraction, Linking, and Schema Induction Methodology

ATLAS graphs are constructed through an LLM-powered multi-phase pipeline:

a) Findings and Triple Extraction

1. Empirical Literature (Meta-Science Use Case)
   • Abstracts are processed in two LLM-assisted stages:
     • First, findings are identified via direct prompting, yielding SPO-form insights from AI-involving studies.
     • Second, Claude Opus 4.1 augments findings into formal JSON triplets: $$(\text{cause}, \text{relationship}, \text{effect}, \text{net outcome})$$.
2. General Corpus (Web/Scholarly Text)
   • Documents segmented according to model context limits (e.g., $C_{\max} = L_{\max} - L_{\mathrm{inst}}$).
   • Three prompt families (entity–entity, entity–event, event–event) guide LLMs in extraction, creating nodes $V_N$ (entities), $V_E$ (events), and $R$ (relations).

b) Post-Processing Normalization

• Synonym Merging: Embeddings are clustered via DBSCAN with cosine metrics (e.g., $\varepsilon = 0.2$), unifying semantically redundant keys.
• Semantic Clustering: k-means is applied independently for each type to establish topic/thematic granularity; e.g., $k_{\mathrm{human}} = 7$, $k_{\mathrm{ai}} = 8$, $k_{\mathrm{co}} = 9$ per silhouette analysis.

c) Dynamic Schema Induction and Conceptualization

• For every node, 3–5 “abstract” phrases are induced via LLM prompts, capturing type or concept, mapping $V \rightarrow \wp(C)$, $R \rightarrow \wp(C)$ where $C$ is the induced concept set.
• Contextualization leverages local graph neighborhoods as disambiguation signals when inducing entity types.
• Concepts and nodes share an embedding space (e.g., RoBERTa or token-based) for probabilistic type assignments,

$$P(c \mid v) = \frac{\exp(\mathbf{e}_v^\top \mathbf{e}_c/\tau)}{\sum_{c'} \exp(\mathbf{e}_v^\top \mathbf{e}_{c'}/\tau)}$$

enabling crisp or soft typing dependent on thresholding.

3. Graph Analytics, Taxonomy, and Topology

a) Schema, Ontology, and Thematic Organization

• Three-Level Taxonomy: Type $\rightarrow$ Subtype $\rightarrow$ Feature (for meta-science) or dynamically induced concept hierarchies (general KBs).
• Community Detection: Louvain algorithm to maximize modularity

$$Q = \frac{1}{2m} \sum_{ij} [A_{ij} - \frac{k_i k_j}{2m}] \delta(c_i, c_j)$$

producing 126 communities with $Q=0.669$ in the HAI instance.

b) Disconnected Area and “Structural Hole” Analytics

• Burt’s Constraint $c_i$ is used to quantify node influence across communities,

$$c_i = \sum_{j \ne i}(p_{ij}+\sum_{q \neq i, j} p_{iq}p_{qj})^2$$

where low $c_i$ highlights bridge nodes (“structural holes”).

• Active Bridge Nodes: Measured as the number of communities a given node connects.

c) Statistics and Scale

• In HAI Atlas: $|V|=1310$ nodes, $|E|=3908$ edges, cluster sizes up to 285 subtypes (AI), average degree ≈ 6.0, diameter ≈ 14.
• In web-scale ATLAS: up to 937.3M nodes, 5.96B edges (ATLAS-CC corpus), mobilizing over 78,000 GPU hours for construction and utilizing FlashAttention on 8B-parameter LLMs.

ATLAS Variant	Nodes (M)	Edges (B)	Concepts (M)
ATLAS-Wiki	243.9	1.49	8.09
ATLAS-Pes2o	174.4	1.15	5.9
ATLAS-CC	937.3	5.96	31.1

4. Interactive Systems and API Architecture

The ATLAS Human-AI Interaction platform is realized via a multi-layered architecture:

• Backend: Python with NetworkX for ingestion, LLM-extraction, clustering, and JSON export.
• Data Store: JSON-serialized graphs with rich node/edge properties.
• Frontend: Built in Svelte.js, Three.js, and D3.js, comprising
  • 3D Graph View (Three.js, d3-force-3d)
  • Cause-Effect View (Sankey diagrams, D3-sankey)
  • Paper View (tabular, searchable)
• Interactive Capabilities:
  • Real-time navigation, filter by taxonomy, edge masking.
  • Embedded node/edge attributes for in-UI exploration.
• Component Breakdown:
  • Controller (Svelte stores), Layout Engine (force-directed/stable/Sankey), Renderer (3D+SVG), UI Widgets (filters, sliders, search).

A distinguishing property is the integration of explorable causal chains, gap identification, and the capacity for multi-modal exploration.

5. Evaluation Metrics, Empirical Benchmarks, and Applications

a) Statistical and Qualitative Evaluation

• Human-AI Interaction ATLAS: 1124/1888 abstracts (59.5%) yielded findings (2037 triplets).
• Network Metrics: Structural holes and bridges identified among frequent types (e.g., ai:LLM, ai:generative).
• User Study: Experts (N=20) rated the 3D Graph gap identification $M=4.95/7$; cause-effect exploration $M=5.45/7$; overall favorability over Google Scholar/ACM DL $M\approx4.6$.
• Qualitative Findings: Participants endorsed camera manipulation for discovery, Sankey “sanity checks,” and multi-modal navigation. Weaknesses included the depth of paper summaries and lack of temporal analysis.

b) Technical Evaluation (Corpus-Scale ATLAS)

• Triple Extraction Quality: Entity–Entity F1 (94.1%), Event–Entity F1 (95.6%), Event–Event F1 (96.0%) on ATLAS-Wiki.
• Schema Induction: >92% alignment with human-crafted ontologies (semantic alignment score).
• Multi-Hop QA Tasks: EM/F1 improvements over retrieval baselines (e.g., MuSiQue Full-KG F1 = 47.3%, +12–18 points over BM25).
• Factuality Enhancement: Wikipedia-derived graph + HippoRAG2 reached 56.43% accuracy, outperforming Freebase-based alternatives.

6. Integration with LLMs and Knowledge-Augmented Generation

ATLAS graphs have demonstrated utility as external memory modules in RAG or memory-augmented reasoning pipelines (e.g., HippoRAG2, Think-on-Graph). In these settings:

• Graph-derived triples and schema act as fact retrieval substrates for LLMs.
• No manual schema curation is required; ATLAS enables domain-adaptable RAG across web and scholarly corpora.
• Empirical results support efficacy for both information retention and downstream performance on knowledge-intensive subject domains.

A plausible implication is that as parametric LLM recall plateaus or proves unreliable, dynamically built ATLAS-style graphs will become indispensable for grounded QA, synthesis, and sensemaking in evolving knowledge domains.

7. Significance and Future Directions

The ATLAS Knowledge Graph family unites advances in scalable LLM-based extraction, dynamic schema induction, and interactive exploration for both computational meta-science and general-purpose knowledge representation. Its approaches establish benchmarks for precision and schema-alignment at unprecedented scale, removing the burden of manual ontology construction while supporting both exploratory and integrative workflows.

• In meta-research, causally annotated, semantically clustered maps enable discovery of research gaps, synthesize evidence, and reveal underexplored pathways.
• In general-language AI, ATLAS graphs serve as rapidly refreshable, schema-rich knowledge stores, suitable for augmenting LLM factuality, multi-hop reasoning, and retrieval tasks.
• The fully automated, language-model-agnostic extraction pipeline positions ATLAS as a generalizable substrate for future knowledge graph construction, including domain, language, and temporal expansion.

Continued progress is likely to focus on deepening event structure, temporal reasoning, explainable navigation, and continual updates as new corpora emerge, establishing ATLAS as a foundational layer for computational scientific discovery and AI reliability.