206 citations · 347 across the 17 of their papers we have counts for
5 papers · 1 filter
Neuro-Symbolic Inductive Logic Programming with Logical Neural Networks
Prithviraj Sen, Breno W. S. R. de Carvalho, Ryan Riegel +1
Recent work on neuro-symbolic inductive logic programming has led to promising approaches that can learn explanatory rules from noisy, real-world data. While some proposals approxi…
Combining Rules and Embeddings via Neuro-Symbolic AI for Knowledge Base Completion
Prithviraj Sen, Breno W. S. R. Carvalho, Ibrahim Abdelaziz +4
Recent interest in Knowledge Base Completion (KBC) has led to a plethora of approaches based on reinforcement learning, inductive logic programming and graph embeddings. In particu…
LNN-EL: A Neuro-Symbolic Approach to Short-text Entity Linking
Hang Jiang, Sairam Gurajada, Qiuhao Lu +5
Entity linking (EL), the task of disambiguating mentions in text by linking them to entities in a knowledge graph, is crucial for text understanding, question answering or conversa…
Deep Indexed Active Learning for Matching Heterogeneous Entity Representations
Arjit Jain, Sunita Sarawagi, Prithviraj Sen
Given two large lists of records, the task in entity resolution (ER) is to find the pairs from the Cartesian product of the lists that correspond to the same real world entity. Typ…
Logic Embeddings for Complex Query Answering
Francois Luus, Prithviraj Sen, Pavan Kapanipathi +4
Answering logical queries over incomplete knowledge bases is challenging because: 1) it calls for implicit link prediction, and 2) brute force answering of existential first-order…