# Compositional Vector Space Models for Knowledge Base Completion

arXiv: https://arxiv.org/abs/1504.06662 · HTML: https://mlsanity.com/paper/1504.06662/

## Key facts

- Cited by 33 papers in the map (data through 14 Oct 2026).
- First posted on arXiv 24 Apr 2015.

## Built on by (most cited first, 30 of 33)

- [Neural Bellman-Ford Networks: A General Graph Neural Network Framework for Link Prediction](https://mlsanity.com/paper/2106.06935/) (2021)
- [A Survey of Knowledge Graph Reasoning on Graph Types: Static, Dynamic, and Multimodal](https://mlsanity.com/paper/2212.05767/) (2022)
- [A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs](https://mlsanity.com/paper/2206.04798/) (2022)
- [Knowledge Base Question Answering by Case-based Reasoning over Subgraphs](https://mlsanity.com/paper/2202.10610/) (2022)
- [Explicit Planning Helps Language Models in Logical Reasoning](https://mlsanity.com/paper/2303.15714/) (2023)
- [Meta-Path Learning for Multi-relational Graph Neural Networks](https://mlsanity.com/paper/2309.17113/) (2023)
- [Perform Like an Engine: A Closed-Loop Neural-Symbolic Learning Framework for Knowledge Graph Inference](https://mlsanity.com/paper/2112.01040/) (2021)
- [Quant 4.0: Engineering Quantitative Investment with Automated, Explainable and Knowledge-driven Artificial Intelligence](https://mlsanity.com/paper/2301.04020/) (2022)
- [Rule Mining over Knowledge Graphs via Reinforcement Learning](https://mlsanity.com/paper/2202.10381/) (2022)
- [KnowFormer: Revisiting Transformers for Knowledge Graph Reasoning](https://mlsanity.com/paper/2409.12865/) (2024)
- [FamilyTool: A Multi-hop Personalized Tool Use Benchmark](https://mlsanity.com/paper/2504.06766/) (2025)
- [Path-based knowledge reasoning with textual semantic information for medical knowledge graph completion](https://mlsanity.com/paper/2105.13074/) (2021)
- [Inductive Knowledge Graph Completion with GNNs and Rules: An Analysis](https://mlsanity.com/paper/2308.07942/) (2023)
- [Positive-Unlabeled Domain Adaptation](https://mlsanity.com/paper/2202.05695/) (2022)
- [Binder: Hierarchical Concept Representation through Order Embedding of Binary Vectors](https://mlsanity.com/paper/2404.10924/) (2024)
- [Knowledge Graph Reasoning with Self-supervised Reinforcement Learning](https://mlsanity.com/paper/2405.13640/) (2024)
- [River of No Return: Graph Percolation Embeddings for Efficient Knowledge Graph Reasoning](https://mlsanity.com/paper/2305.09974/) (2023)
- [DuetGraph: Coarse-to-Fine Knowledge Graph Reasoning with Dual-Pathway Global-Local Fusion](https://mlsanity.com/paper/2507.11229/) (2025)
- [Two Training Strategies for Improving Relation Extraction over Universal Graph](https://mlsanity.com/paper/2102.06540/) (2021)
- [Entity Context Graph: Learning Entity Representations fromSemi-Structured Textual Sources on the Web](https://mlsanity.com/paper/2103.15950/) (2021)
- [ZeroKBC: A Comprehensive Benchmark for Zero-Shot Knowledge Base Completion](https://mlsanity.com/paper/2212.03091/) (2022)
- [Wikidata as a seed for Web Extraction](https://mlsanity.com/paper/2401.07812/) (2024)
- [Pathformer: Recursive Path Query Encoding for Complex Logical Query Answering](https://mlsanity.com/paper/2406.14880/) (2024)
- [Two-dimensional Taxonomy for N-ary Knowledge Representation Learning Methods](https://mlsanity.com/paper/2506.05626/) (2025)
- [DegreEmbed: incorporating entity embedding into logic rule learning for knowledge graph reasoning](https://mlsanity.com/paper/2112.09933/) (2021)
- [Learning Attention-based Representations from Multiple Patterns for Relation Prediction in Knowledge Graphs](https://mlsanity.com/paper/2206.04801/) (2022)
- [VN Network: Embedding Newly Emerging Entities with Virtual Neighbors](https://mlsanity.com/paper/2402.14033/) (2024)
- [T- Hop: A framework for studying the importance path information in molecular graphs for chemical property prediction](https://mlsanity.com/paper/2407.14270/) (2024)
- [From Primes to Paths: Enabling Fast Multi-Relational Graph Analysis](https://mlsanity.com/paper/2411.11149/) (2024)
- [PathE: Leveraging Entity-Agnostic Paths for Parameter-Efficient Knowledge Graph Embeddings](https://mlsanity.com/paper/2501.19095/) (2025)

## Abstract (arXiv)

Knowledge base (KB) completion adds new facts to a KB by making inferences from existing facts, for example by inferring with high likelihood nationality(X,Y) from bornIn(X,Y). Most previous methods infer simple one-hop relational synonyms like this, or use as evidence a multi-hop relational path treated as an atomic feature, like bornIn(X,Z) -> containedIn(Z,Y). This paper presents an approach that reasons about conjunctions of multi-hop relations non-atomically, composing the implications of a path using a recursive neural network (RNN) that takes as inputs vector embeddings of the binary relation in the path. Not only does this allow us to generalize to paths unseen at training time, but also, with a single high-capacity RNN, to predict new relation types not seen when the compositional model was trained (zero-shot learning). We assemble a new dataset of over 52M relational triples, and show that our method improves over a traditional classifier by 11%, and a method leveraging pre-trained embeddings by 7%.