Knowledge Base Completion
64 papers with code • 0 benchmarks • 2 datasets
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.
Benchmarks
These leaderboards are used to track progress in Knowledge Base Completion
Aristo-v4
ZeroKBC
Most implemented papers
Interpreting Embedding Models of Knowledge Bases: A Pedagogical Approach
Knowledge bases are employed in a variety of applications from natural language processing to semantic web search; alas, in practice their usefulness is hurt by their incompleteness.
A Comparative Study of Distributional and Symbolic Paradigms for Relational Learning
Many real-world domains can be expressed as graphs and, more generally, as multi-relational knowledge graphs.
Type-Sensitive Knowledge Base Inference Without Explicit Type Supervision
State-of-the-art knowledge base completion (KBC) models predict a score for every known or unknown fact via a latent factorization over entity and relation embeddings.
Attributed and Predictive Entity Embedding for Fine-Grained Entity Typing in Knowledge Bases
Fine-grained entity typing aims at identifying the semantic type of an entity in KB.
Cross-lingual Knowledge Projection Using Machine Translation and Target-side Knowledge Base Completion
Considerable effort has been devoted to building commonsense knowledge bases.
Modeling relation paths for knowledge base completion via joint adversarial training
By treating relations and multi-hop paths as two different input sources, we use a feature extractor, which is shared by two downstream components (i. e. relation classifier and source discriminator), to capture shared/similar information between them.
End-to-end Structure-Aware Convolutional Networks for Knowledge Base Completion
JD-AI-Research-Silicon-Valley/SACN
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The recent graph convolutional network (GCN) provides another way of learning graph node embedding by successfully utilizing graph connectivity structure.
Learning from positive and unlabeled data: a survey
Learning from positive and unlabeled data or PU learning is the setting where a learner only has access to positive examples and unlabeled data.
Combining Axiom Injection and Knowledge Base Completion for Efficient Natural Language Inference
In logic-based approaches to reasoning tasks such as Recognizing Textual Entailment (RTE), it is important for a system to have a large amount of knowledge data.
Fact Discovery from Knowledge Base via Facet Decomposition
We also propose a novel auto-encoder based facet component to estimate some facets of the fact.
