---
title: 'AceKG: Academic Knowledge Graph'
url: https://www.emergentmind.com/topics/acekg
type: topic
---

# AceKG: Academic Knowledge Graph

AceKG is a large-scale academic knowledge graph (KG) comprising 3.13 billion RDF triples derived from a consistent ontology designed for high-fidelity academic data mining. Its schema represents essential entities and their relations—spanning papers, authors, fields of study, venues, and institutes—with fine-grained resolution achieved via unique URIs, addressing long-standing issues of name ambiguity and insufficient multi-relational coverage. AceKG serves as both a foundational academic resource and a rigorous benchmark environment for algorithmic evaluation in knowledge representation, link prediction, scholarly community detection, and author profiling [1807.08484].

## 1. Ontology: Entity Classes, Relations, and Hierarchies

AceKG instantiates a directed, labeled multi-relational graph \( G = (E, R, T) \) with \(E\) as entities, \(R\) as relation types, and \(T \subseteq E \times R \times E\) as factual triples. The five disjoint top-level entity classes are:
$$
\mathcal{C} = \{\, \mathsf{Paper},\; \mathsf{Author},\; \mathsf{Field},\; \mathsf{Venue},\; \mathsf{Institute}\, \}
$$
Each entity \(e \in E\) is assigned a unique URI for unambiguous identification, enabling, for example, distinct representations for similarly-named authors (such as ace:7E7A3A69 vs. ace:7E0D6766 for two different "Jiawei Han" entities).

The relation vocabulary \(R\) is extensive (\(\sim\)100 types) and includes:
- $\mathsf{paper\_is\_written\_by}$: encodes authorship,
- $\mathsf{paper\_cites\_paper}$: denotes citation,
- $\mathsf{paper\_published\_in\_venue}$,
- $\mathsf{author\_affiliated\_with\_institute}$,
- $\mathsf{field\_is\_part\_of}$: defines a DAG over fields of study,
- and further relations such as $\mathsf{venue\_located\_in\_institute}$ and $\mathsf{institute\_has\_research\_field}$.

Hierarchical, compositional, and numeric data is attached via relations such as $\mathsf{field\_is\_part\_of}$ (for taxonomy) and unary predicates (for literals including citation counts and publication dates).

## 2. Scale, Statistics, and Dataset Partitions

AceKG encodes $|T| = 3.13 \times 10^9$ triples and $|E| = 1.14 \times 10^8$ entities, with explicit class counts:

| Entity Class    | Count               |
|-----------------|--------------------|
| Papers          | $6.17 \times 10^7$ |
| Authors         | $5.25 \times 10^7$ |
| Fields of Study | $5.02 \times 10^4$ |
| Venues          | $2.17 \times 10^4$ (journals) + $1.28 \times 10^3$ (conferences) |
| Institutes      | $1.98 \times 10^4$ |

AceKG-derived benchmark datasets include:
- **AK18K** for link prediction (\(n_{rel}=7\), \(n_{ent}=18464\); train/validation/test counts: $\sim$130,265/7,429/7,336),
- Six field-specific heterogeneous collaboration graphs (e.g., Biology, Computer Science, Economics, Medicine, Physics, and a union of five fields),
- A "Google-Scholar-venue" graph of 600K papers, 635K authors, 151 venues, and 2.37M edges.

All primary data originates from the in-house Acemap repository, normalized and deduplicated; disambiguation is ensured via systematic URI assignment. Preprocessing also normalizes literals and dates to facilitate large-scale, machine-compliant operations.

## 3. Entity Alignment and Rule-based Inference

Entity alignment integrates AceKG with prominent external bibliographic databases (IEEE, ACM, DBLP). Mappings are computed by matching paper title and author lists, principally using cosine similarity of TF-IDF representations:
$$
\operatorname{sim}(\mathrm{pm},\mathrm{qm}) = \cos(\mathrm{tfidf}(\mathrm{pm}),\mathrm{tfidf}(\mathrm{qm}))
$$
Titles may also be matched using edit distance. At threshold $\tau \approx 0.9$, the mapping achieves coverage of 2.33M (IEEE), 1.91M (ACM), and 2.27M (DBLP) papers.

Rule-based inference employs Horn-style rules to further augment the graph. Representative rules include:
- $(a\;\mathsf{paper\_is\_written\_by}\;b) \wedge (b\;\mathsf{affiliated\_with}\;I) \longrightarrow (a\;\mathsf{paper\_has\_institute}\;I)$,
- $(p_1\;\mathsf{paper\_cites\_paper}\;p_2) \wedge (p_2\;\mathsf{paper\_cites\_paper}\;p_3) \longrightarrow (p_1\;\mathsf{paper\_co\_cite}\;p_3)$.

*This suggests* that AceKG provides both explicit and inferred relational knowledge, improving its utility for downstream analytics.

## 4. Benchmark Tasks: Link Prediction, Community Detection, and Scholar Profiling

AceKG supports systematic evaluation via canonical academic data mining tasks:

### 4.1 Link Prediction (Knowledge Embedding)

Given a triple set $T_{train} \subseteq T$, entities $e \in E$ are embedded as $\mathbf{e} \in \mathbb{R}^d$ and relations $r \in R$ as $\mathbf{r}$, using scoring functions $f_r(h, t)$ to predict missing links. Evaluated methods and representative scoring functions include:
- **TransE**: $f_r(h, t) = -\|\mathbf{h} + \mathbf{r} - \mathbf{t}\|_2$,
- **TransH**: projection onto relation-specific hyperplanes,
- **DistMult**: $f_r(h, t) = \sum_{i=1}^d h_i\,r_i\,t_i$,
- **HolE**: $f_r(h, t) = \mathbf{r}^\top (\mathbf{h} \star \mathbf{t})$ (circular correlation),
- **ComplEx**: $f_r(h, t) = \Re\langle\mathbf{h},\,\mathbf{r},\,\overline{\mathbf{t}}\rangle$ with complex-valued embeddings.

All models use embedding dimension $d=100$, margin $\gamma=1.0$, learning rate $\alpha=0.01$, and SGD with early stopping on validation MRR; loss $\mathcal{L}$ is a margin ranking loss with one-entity corrupted negatives per triple.

### 4.2 Community Detection and Scholar Classification

On extracted collaboration graphs, network representation learning is performed via:
- **DeepWalk**: homogeneous random walks with Skip-Gram,
- **LINE**: first- and second-order proximity preservation,
- **PTE**: joint text-network embeddings,
- **metapath2vec**: heterogeneous walks along user-defined metapaths.

Resulting node embeddings feed into logistic regression (5-fold CV) for classification (metrics: Micro-F1, Macro-F1) and k-means clustering (NMI metric) for community detection.

## 5. Experimental Results and Analytical Insights

### 5.1 Link Prediction Performance on AK18K

Filtered MRR and Hits@$k$ (for $k=1,3,10$) for several knowledge embedding models:

| Model     | MRR   | Hits@1 | Hits@10 |
|-----------|-------|--------|---------|
| TransE    | 0.719 | 62.7%  | 89.2%   |
| TransH    | 0.701 | 61.0%  | 84.6%   |
| DistMult  | 0.749 | 68.7%  | 86.1%   |
| HolE      | 0.864 | 83.8%  | 88.2%   |
| ComplEx   | 0.817 | 75.4%  | 89.0%   |

HolE and ComplEx exhibit superior capability for modeling antisymmetric relationships, which characterize all seven AK18K relation types. TransE outperforms TransH in Hits@10, plausibly due to a high proportion of many-to-many relations (94%) and a limited relation schema, where the benefit from hyperplane projection (TransH) is minimal. AK18K results fall between FB15K and WN18 benchmarks; this is attributed to moderate graph sparsity and the relatively simple relation schema.

### 5.2 Scholar Classification and Clustering

Micro-F1 and Macro-F1 scores (classification) and normalized mutual information (clustering):

| Dataset        | DeepWalk    | LINE(1+2)   | PTE         | metapath2vec |
|----------------|-------------|-------------|-------------|--------------|
| biology        | .792/.547   | .722/.445   | .759/.495   | .828/.637    |
| computer sci.  | .545/.454   | .633/.542   | .574/.454   | .678/.570    |
| economics      | .692/.277   | .717/.385   | .654/.276   | .753/.485    |
| medicine       | .663/.496   | .701/.577   | .694/.555   | .770/.659    |
| physics        | .774/.592   | .779/.640   | .723/.571   | .794/.635    |
| 5-Fields union | .731/.589   | .755/.655   | .664/.528   | .831/.682    |
| Google-venues  | .948/.942   | .955/.949   | .966/.961   | .971/.968    |

Clustering (NMI):

| Method        | FOS     | Google |
|---------------|---------|--------|
| DeepWalk      | 0.277   | 0.394  |
| LINE(1+2)     | 0.305   | 0.459  |
| PTE           | 0.153   | 0.602  |
| metapath2vec  | 0.427   | 0.836  |

Metapath2vec, by exploiting heterogeneous context, consistently achieves superior classification and clustering performance. The Google-venue graph's higher data quality (top venues, reduced interdisciplinarity) yields increased performance across methods. A pronounced Micro-F1 to Macro-F1 drop indicates major label imbalance and cross-field scholarly activity.

## 6. Applications and Prospective Research Avenues

AceKG underpins multiple academic data mining tasks:

- **Cooperation Prediction**: Embeddings encode institutional, field, and citation proximity, supporting co-authorship link prediction.
- **Author Disambiguation**: Hetero-typed relational context enables distinction of same-name authors; iterative URI refinement is supported via embedding-space clustering.
- **Rising-Star Detection**: Temporal AceKG snapshots, combined with feature trajectories and embedding novelty, support early-career impact forecasting.

Future research directions include dynamic KG updates (streaming ingestion), fine-grained event extraction, graph neural networks over heterogeneous schemas, and cross-modal knowledge integration (e.g., PDF content, code repositories). The scale, ontological clarity, and connection to external bibliographic resources position AceKG as a foundational asset for scalable, multi-facet academic knowledge discovery and benchmarking [1807.08484].

Source: https://www.emergentmind.com/topics/acekg