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Knowledge Base Completion

23 papers with code ยท Knowledge Base

Knowledge base completion is the task which automatically infers missing facts by reasoning about the information already present in the knowledge base. A knowledge base is a collection of relational facts, often represented in the form of "subject", "relation", "object"-triples.

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Latest papers without code

Projected Canonical Decomposition for Knowledge Base Completion

ICLR 2020

However, as we show in this paper through experiments on standard benchmarks of link prediction in knowledge bases, ComplEx, a variant of CP, achieves similar performances to recent approaches based on Tucker decomposition on all operating points in terms of number of parameters.

KNOWLEDGE BASE COMPLETION LINK PREDICTION

Tensor Decompositions for Temporal Knowledge Base Completion

ICLR 2020

Additionally, we propose a new dataset for knowledge base completion constructed from Wikidata, larger than previous benchmarks by an order of magnitude, as a new reference for evaluating temporal and non-temporal link prediction methods.

KNOWLEDGE BASE COMPLETION LINK PREDICTION RECOMMENDATION SYSTEMS REPRESENTATION LEARNING

Dynamically Pruned Message Passing Networks for Large-scale Knowledge Graph Reasoning

ICLR 2020

We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning.

KNOWLEDGE BASE COMPLETION

Neural Markov Logic Networks

ICLR 2020

Instead, NMLNs learn an implicit representation of such rules as a neural network that acts as a potential function on fragments of the relational structure.

KNOWLEDGE BASE COMPLETION RELATIONAL REASONING

Reasoning Over Paths via Knowledge Base Completion

WS 2019

We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS

Reasoning Over Paths via Knowledge Base Completion

WS 2019

We demonstrate that our method is able to effectively rank a list of known paths between a pair of entities and also come up with plausible paths that are not present in the knowledge graph.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS

Commonsense Knowledge Base Completion with Structural and Semantic Context

7 Oct 2019

Our results demonstrate the effectiveness of language model representations in boosting link prediction performance and the advantages of learning from local graph structure (+1. 5 points in MRR for ConceptNet) when training on subgraphs for computational efficiency.

KNOWLEDGE BASE COMPLETION KNOWLEDGE GRAPHS LANGUAGE MODELLING LINK PREDICTION TRANSFER LEARNING

Scene Graph Prediction With Limited Labels

ICCV 2019

All scene graph models to date are limited to training on a small set of visual relationships that have thousands of training labels each.

KNOWLEDGE BASE COMPLETION QUESTION ANSWERING TRANSFER LEARNING VISUAL QUESTION ANSWERING

Dynamically Pruned Message Passing Networks for Large-Scale Knowledge Graph Reasoning

25 Sep 2019

We propose Dynamically Pruned Message Passing Networks (DPMPN) for large-scale knowledge graph reasoning.

KNOWLEDGE BASE COMPLETION

Text-Based Joint Prediction of Numeric and Categorical Attributes of Entities in Knowledge Bases

RANLP 2019

Our analysis indicates that this is the case because categorical attributes, many of which describe membership in various classes, provide useful {`}background knowledge{'} for numeric prediction, while this is true to a lesser degree in the inverse direction.

KNOWLEDGE BASE COMPLETION MULTI-TASK LEARNING