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Found 17 papers, 7 papers with code
Testing the General Deductive Reasoning Capacity of Large Language Models Using OOD Examples
Given the intractably large size of the space of proofs, any model that is capable of general deductive reasoning must generalize to proofs of greater complexity.
TwiRGCN: Temporally Weighted Graph Convolution for Question Answering over Temporal Knowledge Graphs
Recent years have witnessed much interest in temporal reasoning over knowledge graphs (KG) for complex question answering (QA), but there remains a substantial gap in human capabilities.
SLAPS: Self-Supervision Improves Structure Learning for Graph Neural Networks
In this work, we propose the Simultaneous Learning of Adjacency and GNN Parameters with Self-supervision, or SLAPS, a method that provides more supervision for inferring a graph structure through self-supervision.
Ranked #1 on
Graph structure learning
on Cora
Out-of-Sample Representation Learning for Knowledge Graphs
Many important problems can be formulated as reasoning in knowledge graphs.
Out-of-Sample Representation Learning for Multi-Relational Graphs
Many important problems can be formulated as reasoning in knowledge graphs.
Time2Vec: Learning a Vector Representation of Time
Time is an important feature in many applications involving events that occur synchronously and/or asynchronously.
Diachronic Embedding for Temporal Knowledge Graph Completion
In this paper, we build novel models for temporal KG completion through equipping static models with a diachronic entity embedding function which provides the characteristics of entities at any point in time.
Representation Learning for Dynamic Graphs: A Survey
Graphs arise naturally in many real-world applications including social networks, recommender systems, ontologies, biology, and computational finance.
Structure Learning for Relational Logistic Regression: An Ensemble Approach
We consider the problem of learning Relational Logistic Regression (RLR).
Record Linkage to Match Customer Names: A Probabilistic Approach
We provide a probabilistic model using relational logistic regression to find the probability of each record in the database being the desired record for a given query and find the best record(s) with respect to the probabilities.
SimplE Embedding for Link Prediction in Knowledge Graphs
We prove SimplE is fully expressive and derive a bound on the size of its embeddings for full expressivity.
Ranked #19 on
Link Prediction
on FB15k
RelNN: A Deep Neural Model for Relational Learning
Statistical relational AI (StarAI) aims at reasoning and learning in noisy domains described in terms of objects and relationships by combining probability with first-order logic.
Comparing Aggregators for Relational Probabilistic Models
Relational probabilistic models have the challenge of aggregation, where one variable depends on a population of other variables.
Domain Recursion for Lifted Inference with Existential Quantifiers
In this paper, we show that domain recursion can also be applied to models with existential quantifiers.
New Liftable Classes for First-Order Probabilistic Inference
Statistical relational models provide compact encodings of probabilistic dependencies in relational domains, but result in highly intractable graphical models.
A Learning Algorithm for Relational Logistic Regression: Preliminary Results
We compare our learning algorithm to other structure and parameter learning algorithms in the literature, and compare the performance of RLR models to standard logistic regression and RDN-Boost on a modified version of the MovieLens data-set.
Why is Compiling Lifted Inference into a Low-Level Language so Effective?
First-order knowledge compilation techniques have proven efficient for lifted inference.
