---
title: NASA Earth Observation Knowledge Graph
url: https://www.emergentmind.com/topics/nasa-earth-observation-knowledge-graph
type: topic
---

# NASA Earth Observation Knowledge Graph

The NASA Earth Observation Knowledge Graph (NASA EO‑KG) is a public, heterogeneous knowledge graph for Earth observation that links datasets, publications, science keywords, instruments, platforms, projects, and data centers. In recent work it has been used in two distinct but complementary ways: as an authoritative offline source of publication–dataset usage for search and retrieval, and as a multi-relational graph for learning novel dataset pairings and grounding hypothesis generation [2607.02387][2607.01584]. In the search setting, its operative core is the publication–dataset citation subgraph, especially `USES_DATASET` edges from peer-reviewed publications to NASA Common Metadata Repository (CMR) datasets; in the hypothesis-generation setting, a broader typed graph is used directly for heterogeneous graph learning [2607.02387][2607.01584].

## 1. Definition and scope

NASA EO‑KG is described as the **NASA Earth Observation Knowledge Graph (NASA EO‑KG)**, released as a dataset on Hugging Face, and in later work as a public GraphML resource with **150,351 nodes** and **436,203 edges** spanning **seven node types** and **nine typed relations** [2607.02387][2607.01584]. Conceptually, it links peer-reviewed publications to NASA Earth observation datasets and also organizes dataset metadata through science keywords, platforms, instruments, projects, and data centers [2607.01584].

In the agentic-search formulation, NASA EO‑KG is treated as an existing, external resource rather than as an online reasoning substrate. The paper is explicit that the runtime system does **not** query the graph; instead, the graph is used offline to extract high-provenance `USES_DATASET` edges, which become “silver labels” for supervised retrieval [2607.02387]. This makes a crucial distinction: the graph is not synonymous with the deployed search agent, even though it underwrites that agent’s effectiveness.

A common misunderstanding is therefore that NASA EO‑KG is only a publication–dataset graph. That statement is accurate for the way the search paper uses it, but incomplete as a description of the resource itself. In the hypothesis-generation setting, EO‑KG is a broader heterogeneous graph whose nodes include `Publication`, `Dataset`, `ScienceKeyword`, `Instrument`, `Platform`, `Project`, and `DataCenter` [2607.01584].

## 2. Graph structure and representation

The broader EO‑KG schema reported for hypothesis generation includes the following node inventory and counts [2607.01584]:

| Node type | Count | Attributes noted |
|---|---:|---|
| `Publication` | 138,704 | doi, title, year, authors, abstract |
| `Dataset` | 8,058 | shortName, longName, DAAC, CMR ID, temporal extent |
| `ScienceKeyword` | 1,609 | keyword name, hierarchical subcategory |
| `Instrument` | 921 | not detailed beyond type |
| `Platform` | 455 | not detailed beyond type |
| `Project` | 415 | not detailed beyond type |
| `DataCenter` | 189 | not detailed beyond type |

The nine typed relations reported in that same graph are `CITES`, `HAS_APPLIEDRESEARCHAREA`, `USES_DATASET`, `HAS_SCIENCEKEYWORD`, `HAS_PLATFORM`, `HAS_DATASET`, `OF_PROJECT`, `HAS_INSTRUMENT`, and `HAS_SUBCATEGORY` [2607.01584]. In the narrower search usage, however, the relevant schema is essentially reduced to `Publication`, `Dataset`, and `USES_DATASET(Publication, Dataset)` [2607.02387].

Formally, the search paper relies on a bipartite citation subgraph. Let $\mathcal{P}$ be publications, $\mathcal{D}$ be NASA CMR datasets, and $\mathcal{E}_{\text{uses}} \subset \mathcal{P} \times \mathcal{D}$ the set of `USES_DATASET` edges. Each edge $(p,d) \in \mathcal{E}_{\text{uses}}$ means publication $p$ used dataset $d$ [2607.02387]. Dataset nodes are already reconciled with CMR collection identifiers, and publication nodes are already linked to NASA GES DISC literature with citation metadata [2607.02387].

This split between the full heterogeneous graph and the publication–dataset citation slice is central to understanding the resource. In one usage mode, EO‑KG behaves like a typed, multi-relational graph suitable for link prediction. In the other, it acts as an authoritative provenance layer from which text-only retrieval benchmarks can be derived.

## 3. NASA‑EO‑Bench and supervised retrieval from `USES_DATASET`

NASA‑EO‑Bench is the principal benchmark derived from EO‑KG for data discovery [2607.02387]. Its construction begins with the top **10,636 publications by citation count** from NASA EO‑KG. For each publication $p$, the graph defines a ground-truth dataset set
\[
\mathcal{G}_p = \{ d \in \mathcal{D} \mid (p,d) \in \mathcal{E}_{\text{uses}} \}.
\]
Two task-based natural-language queries are then generated per publication using GPT‑5.4 conditioned only on the abstract; the model is not told which datasets were actually used [2607.02387]. After filtering, this yields **21,272 task-based queries** and **47,654 query–dataset positive pairs** over a corpus of **8,058 NASA CMR datasets** [2607.02387].

The split is by publication to avoid leakage: **17,038 queries / 38,078 pairs** for training and **4,234 queries / 9,576 pairs** for test, stratified by `cited_by_count` [2607.02387]. The benchmark therefore preserves the literature-grounded linkage between a research need and the datasets actually used in a paper.

Evaluation is defined against the KG-derived ground-truth set $\mathcal{G}_q$. The benchmark uses **Recall@K**, **MRR**, and **MAP**, with
\[
R@K(q) = \frac{|\mathcal{R}_q^{(K)} \cap \mathcal{G}_q|}{|\mathcal{G}_q|},
\]
and macro-averaged recall and reciprocal-rank metrics over all queries [2607.02387]. Because the labels arise from actual publication usage, they encode strong provenance; because they are citation-driven rather than exhaustively adjudicated, they remain silver rather than gold labels.

This use of EO‑KG is distinctive. The graph’s information is injected not as runtime graph features, but as supervision. The neural retrievers never traverse the graph at inference time; instead, they learn from large-scale query–dataset relevance pairs extracted from the graph.

## 4. Runtime search pipeline and the non-runtime role of the graph

The deployed search pipeline has three stages: **Router**, **Hybrid retrieval**, and **Agentic reranking** [2607.02387]. The Router uses LLM routing to send queries to official NASA services such as Harmony, Science Discovery Engine, Worldview, and Giovanni when intent matches those tools. The Hybrid retrieval stage operates over the **8,058-dataset** text corpus derived from CMR, representing each dataset by `shortName + longName + abstract`, plus additional metadata for BM25 [2607.02387]. The Agentic reranking stage then sees the query and top‑$K$ candidate dataset descriptions, and may call general web search and arXiv search; in the deployed system, SDE search is also available but was not used in the reported experiments [2607.02387].

The neural retrieval stack includes BM25, cosine similarity over a NASA domain-adapted sentence transformer, a fine-tuned cosine bi-encoder, and **NN‑SSC**, a neural score-correction MLP. NN‑SSC takes L2-normalized query and dataset embeddings, concatenates them into $\mathbf{z} \in \mathbb{R}^{1536}$, and applies an MLP with architecture
\[
1536 \to 256 \to 256 \to 1
\]
with ReLU hidden layers and sigmoid output [2607.02387]. Hybrid score fusion combines a normalized lexical score $\hat{s}_{\ell}(q,d)$ and neural score $\hat{s}_{\text{NN}}(q,d)$ as
\[
s_{\text{hybrid}}(q,d)
=
\alpha \cdot \hat{s}_{\text{NN}}(q,d)
+
(1-\alpha) \cdot \hat{s}_{\ell}(q,d),
\]
with $\alpha$ set analytically from standalone training performance [2607.02387].

The reported gains are large. The **Cosine (base)** baseline achieves **R@10 = 0.0755** and **MRR = 0.0538**, whereas **NN‑SSC + BM25 (Hybrid)** achieves **R@10 = 0.4275** and **MRR = 0.2918**, a **more than 5× improvement** in both metrics [2607.02387]. On a stratified **$N=200$** subset, agentic reranking further improves the hybrid baseline from **MRR ≈ 0.302** to **0.388**, with **MAP** rising from **0.260** to **0.323**; the paper describes this as approximately **28% relative** improvement over the baseline MRR [2607.02387].

The decisive point is methodological rather than architectural: NASA EO‑KG is used **once, offline**, at benchmark-construction time. BM25, dense retrieval, and the agentic reranker all operate over text and external APIs rather than graph queries [2607.02387]. This directly rebuts the assumption that the search system is a runtime KG-reasoning engine.

## 5. Heterogeneous graph learning and hypothesis generation

A separate line of work uses the broader EO‑KG directly as a heterogeneous graph for scientific discovery [2607.01584]. Starting from the public GraphML resource, the authors derive dataset–dataset co-usage pairs from `Publication → Dataset` edges: for each publication with dataset set $D_p$, every unordered pair in $D_p$ becomes a co-usage observation tagged with publication year [2607.01584]. The resulting co-usage graph contains **2,088 dataset nodes** with at least one co-usage edge, **23,490 unique dataset–dataset co-usage pairs**, and a total co-usage weight of **64,930**; edge weights are heavy-tailed, with maximum weight **1,051**, median degree **11**, and maximum degree **314** [2607.01584].

For learning, publication nodes are removed to avoid message-passing leakage, and the training graph retains `Dataset`, `ScienceKeyword`, `Instrument`, `Platform`, and `Project` nodes with six relations: `co_usage`, `has_platform`, `has_keyword`, `has_instrument`, `of_project`, and `has_subcategory` [2607.01584]. Dataset nodes are initialized with **768-dimensional SPECTER2 embeddings** of dataset abstracts; non-dataset nodes receive learnable 768-dimensional embeddings, and all node types are projected into a shared latent dimension **128** [2607.01584].

The link predictor is a two-layer heterogeneous GraphSAGE. For relation $r$ and node $v$, the relation-specific message is
\[
\mathbf{m}_v^{(\ell+1,r)}
=
\mathbf{W}_r^{(\ell)}
\,
\mathrm{MEAN}_{u \in \mathcal{N}_r(v)}
\mathbf{h}_u^{(\ell)},
\]
and the update is
\[
\mathbf{h}_v^{(\ell+1)}
=
\sigma\!\Big(
\mathbf{W}_{\mathrm{self}}^{(\ell)}\mathbf{h}_v^{(\ell)}
+
\sum_{r \in \mathcal{R}_v}
\mathbf{m}_v^{(\ell+1,r)}
\Big)
\]
with ReLU and dropout [2607.01584]. Two scoring heads are used,
\[
z_{\mathrm{dot}}(i,j)=\mathbf{h}_i^\top \mathbf{h}_j,
\qquad
z_{\mathrm{mlp}}(i,j)=\mathrm{MLP}([\mathbf{h}_i\|\mathbf{h}_j]),
\]
trained with binary cross-entropy and degree-biased negative sampling [2607.01584].

This graph-learning stage feeds a three-agent LLM pipeline. The GNN ranks predicted-novel dataset pairs among a **1,475-dataset** evaluation universe, and the top **200** novel pairs become candidates for Agent 1 filtering [2607.01584]. Agent 2 turns selected pairs into structured research hypotheses, and Agent 3 evaluates them. Applied to **1,475 NASA datasets**, the system produces **160 hypotheses** spanning ecohydrology, glaciology, aerosol–cloud interactions, vegetation phenology, and stratospheric chemistry [2607.01584]. Agent 1 ratings show that GNN-predicted novel pairs are nearly as plausible as held-out real co-usages: under GPT‑5.2, real co-usages score **4.57** in plausibility and predicted-novel pairs **4.38**; under Claude Sonnet 4.6, the corresponding scores are **4.63** and **4.34** [2607.01584].

Here, unlike the search system, EO‑KG is not merely a label source. It is the structural object over which graph learning is performed, and the dataset metadata carried by graph nodes is passed directly into the LLM agents as grounding context.

## 6. Limitations, biases, and broader architectural implications

Both major usages of NASA EO‑KG expose structural limitations. In NASA‑EO‑Bench, labels are citation-driven, so **coverage bias** and **popular dataset bias** are unavoidable: many relevant but uncited products never appear as positives, and frequently used datasets such as MODIS, Landsat, and GPM IMERG are over-represented [2607.02387]. Citation also does not exhaust relevance: a paper may use one of several scientifically equivalent datasets, so non-cited alternatives are counted as false positives. The search paper further notes **potential label leakage** in agentic evaluation, because a model with web and arXiv access might recover the source paper and reconstruct its dataset list [2607.02387].

In the graph-learning setting, the co-usage structure is heavy-tailed, publication leakage must be controlled by removing publication nodes, and the GNN uses **binary** rather than weighted co-usage supervision, which limits direct modeling of how strongly a pairing is established in the literature [2607.01584]. Temporal information is used for splitting but not by a temporal GNN [2607.01584].

These limitations motivate the most plausible future directions already suggested by the papers themselves. The search paper explicitly points toward expanding EO‑KG entity and relation types to include variables, platforms, instruments, processing levels, and services such as Worldview, Giovanni, and Harmony, and toward possible runtime KG integration through live graph queries over publications and `USES_DATASET` edges [2607.02387]. Related geospatial KG work suggests a standards-based route for such enrichment: RDF/OWL graphs with GeoSPARQL for geometry, OWL‑Time for temporal structure, SOSA/SSN for observations, QUDT for quantities, and PROV‑O for lineage [2405.07664]. Large-scale geo-KG systems such as KnowWhereGraph further suggest that a discrete global grid, precomputed spatial relations, and modular ontologies can support integration across environmental, hazard, and socio-economic layers at very large scale [2502.13874].

Within NASA specifically, earlier Science Mission Directorate KG work had already framed knowledge graphs as a basis for dataset search engines across SMD domains, including Earth Science [2303.10871]. The recent EO‑KG literature narrows that general ambition into two concrete operational patterns: offline label extraction for search, and heterogeneous graph learning for discovery. Taken together, these uses establish NASA EO‑KG not as a single application, but as a reusable semantic asset whose publication–dataset provenance, typed metadata structure, and graph topology can support retrieval, recommendation, and hypothesis generation at different points in the Earth observation workflow.

Source: https://www.emergentmind.com/topics/nasa-earth-observation-knowledge-graph