Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction

@inproceedings{Gardner2015EfficientAE,
  title={Efficient and Expressive Knowledge Base Completion Using Subgraph Feature Extraction},
  author={Matt Gardner and Tom Michael Mitchell},
  booktitle={Conference on Empirical Methods in Natural Language Processing},
  year={2015}
}
We explore some of the practicalities of using random walk inference methods, such as the Path Ranking Algorithm (PRA), for the task of knowledge base completion. [] Key Method In addition to being conceptually simpler than PRA, SFE is much more efficient, reducing computation by an order of magnitude, and more expressive, allowing for much richer features than paths between two nodes in a graph. We show experimentally that this technique gives substantially better performance than PRA and its variants…
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Title Knowledge base completion using distinct subgraph paths

This paper addresses the limitations of existing works by introducing a new graph-based feature model – Distinct Subgraph Paths (DSP), which uses a richer set of graph features and therefore can predict new relevant facts that neither SFE, nor PRA or its variants can discover by principle.

Knowledge base completion using distinct subgraph paths

This paper addresses the limitations of existing works by introducing a new graph-based feature model - Distinct Subgraph Paths (DSP), which uses a richer set of graph features and therefore can predict new relevant facts that neither SFE, nor PRA or its variants can discover by principle.

A Knowledge Base Completion Model Based on Path Feature Learning

The path feature learning model (PFLM) is proposed, which aims to learn path features from the existing knowledge base and extra parsed corpus and uses these path features to predict new relations to improve the scalability of knowledge inference.

Context-aware Path Ranking for Knowledge Base Completion

Experimental results show that the path features discovered by the Context-aware Path Ranking algorithm not only improve predictive performance but also are more interpretable than existing baselines.

Learning to Efficiently Propagate for Reasoning on Knowledge Graphs

This paper proposes A*Net, an efficient model for path-based reasoning on knowledge graphs, Inspired by the classical A* algorithm for shortest path problems, which prioritizes important nodes and edges at each propagation step, to reduce the time and memory footprint.

Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion

This work presents a fine-grained evaluation that gives insight into characteristics of the most popular datasets and points out the different strengths and shortcomings of the examined approaches, and combines both families of approaches via ensemble learning.

Path Ranking with Attention to Type Hierarchies

Attentive Path Ranking is introduced, a novel path pattern representation that leverages type hierarchies of entities to both avoid ambiguity and maintain generalization and experiments demonstrate that the proposed model outperforms existing methods on the fact prediction task.

A*Net: A Scalable Path-based Reasoning Approach for Knowledge Graphs

To the best knowledge, A*Net is the first path-based method for knowledge graph reasoning at such a scale, and inspired by the A* algorithm for shortest path problems, which learns a priority function to select important nodes and edges at each iteration.

A path-based relation networks model for knowledge graph completion

CAFE: Fact Checking in Knowledge Graphs using Neighborhood-Aware Features

This paper presents an approach to completing KGs based on evaluating candidate triples using a novel set of features, which exploits the highly relational nature of KGs by analyzing the entities and relations surrounding any given pair of entities.
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