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={EMNLP},
  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…Expand
Title Knowledge base completion using distinct subgraph paths
Graph feature models facilitate efficient and interpretable predictions of missing links in knowledge bases with network structure (i.e. knowledge graphs). However, existing graph feature models—Expand
Knowledge base completion using distinct subgraph paths
TLDR
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. Expand
A Knowledge Base Completion Model Based on Path Feature Learning
TLDR
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. Expand
Context-aware Path Ranking for Knowledge Base Completion
TLDR
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. Expand
Fine-Grained Evaluation of Rule- and Embedding-Based Systems for Knowledge Graph Completion
TLDR
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. Expand
Path Ranking with Attention to Type Hierarchies
TLDR
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. Expand
A path-based relation networks model for knowledge graph completion
TLDR
It is shown that a simple neural network module for relational reasoning through the path extracted from the knowledge base can be used to reliably infer new facts for the missing link. Expand
CAFE: Fact Checking in Knowledge Graphs using Neighborhood-Aware Features
Knowledge Graphs (KGs) currently contain a vast amount of structured information in the form of entities and relations. Because KGs are often constructed automatically by means of informationExpand
Abstract Graphs and Abstract Paths for Knowledge Graph Completion
TLDR
A method for representing knowledge graphs that capture an intensional representation of the original extensional information, which abstracts away from individual links, allowing us to find better path candidates, as shown by the results of link prediction using this information. Expand
Using Patterns in Knowledge Graphs for Targeted Information Extraction
Knowledge graphs are useful structures for capturing information in machine readable format, that can be used for higher level tasks such as question answering. Knowledge graphs are incomplete. WeExpand
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