Knowledge Graph Completion
205 papers with code • 7 benchmarks • 16 datasets
Knowledge graphs $G$ are represented as a collection of triples $\{(h, r, t)\}\subseteq E\times R\times E$, where $E$ and $R$ are the entity set and relation set. The task of Knowledge Graph Completion is to either predict unseen relations $r$ between two existing entities: $(h, ?, t)$ or predict the tail entity $t$ given the head entity and the query relation: $(h, r, ?)$.
Benchmarks
These leaderboards are used to track progress in Knowledge Graph Completion
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Libraries
Use these libraries to find Knowledge Graph Completion models and implementationsDatasets
MovieLens
FB15k
WN18
InferWiki
CoDEx Medium
Subtasks
Latest papers with no code
HyperMono: A Monotonicity-aware Approach to Hyper-Relational Knowledge Representation
This paper proposes the HyperMono model for hyper-relational knowledge graph completion, which realizes stage reasoning and qualifier monotonicity.
Progressive Knowledge Graph Completion
In this paper, we investigate three crucial processes relevant to real-world construction scenarios: (a) the verification process, which arises from the necessity and limitations of human verifiers; (b) the mining process, which identifies the most promising candidates for verification; and (c) the training process, which harnesses verified data for subsequent utilization; in order to achieve a transition toward more realistic challenges.
Zero-Shot Relational Learning for Multimodal Knowledge Graphs
Relational learning is an essential task in the domain of knowledge representation, particularly in knowledge graph completion (KGC). While relational learning in traditional single-modal settings has been extensively studied, exploring it within a multimodal KGC context presents distinct challenges and opportunities.
KGExplainer: Towards Exploring Connected Subgraph Explanations for Knowledge Graph Completion
Knowledge graph completion (KGC) aims to alleviate the inherent incompleteness of knowledge graphs (KGs), which is a critical task for various applications, such as recommendations on the web.
IME: Integrating Multi-curvature Shared and Specific Embedding for Temporal Knowledge Graph Completion
However, existing Temporal Knowledge Graph Completion (TKGC) methods either model TKGs in a single space or neglect the heterogeneity of different curvature spaces, thus constraining their capacity to capture these intricate geometric structures.
Hyper-CL: Conditioning Sentence Representations with Hypernetworks
While the introduction of contrastive learning frameworks in sentence representation learning has significantly contributed to advancements in the field, it still remains unclear whether state-of-the-art sentence embeddings can capture the fine-grained semantics of sentences, particularly when conditioned on specific perspectives.
HDReason: Algorithm-Hardware Codesign for Hyperdimensional Knowledge Graph Reasoning
When conducting cross-models and cross-platforms comparison, HDReason yields an average 4. 2x higher performance and 3. 4x better energy efficiency with similar accuracy versus the state-of-the-art FPGA-based GCN training platform.
Uncertainty-Aware Relational Graph Neural Network for Few-Shot Knowledge Graph Completion
Uncertainty representation is first designed for estimating the uncertainty scope of the entity pairs after transferring feature representations into a Gaussian distribution.
Temporal Knowledge Graph Completion with Time-sensitive Relations in Hypercomplex Space
Temporal knowledge graph completion (TKGC) aims to fill in missing facts within a given temporal knowledge graph at a specific time.
VN Network: Embedding Newly Emerging Entities with Virtual Neighbors
To address this issue, recent works apply the graph neural network on the existing neighbors of the unseen entities.
