activity
20032023
most citedNeural-Symbolic Learning and Reasoning: A Survey and Interpretation

241 citations · 469 across the 31 of their papers we have counts for

collaborators
Showing cs.AIShow all

21 papers · 1 filter

cs.AI2023

An Ontology Design Pattern for Role-Dependent Names

Rushrukh Rayan, Cogan Shimizu, Pascal Hitzler

We present an ontology design pattern for modeling Names as part of Roles, to capture scenarios where an Agent performs different Roles using different Names associated with the di…

cs.AI2022

Towards Human-Compatible XAI: Explaining Data Differentials with Concept Induction over Background Knowledge

Cara Widmer, Md Kamruzzaman Sarker, Srikanth Nadella +6

Concept induction, which is based on formal logical reasoning over description logics, has been used in ontology engineering in order to create ontology (TBox) axioms from the base…

cs.AI2022★ 2 cited

Ontology Design Facilitating Wikibase Integration -- and a Worked Example for Historical Data

Cogan Shimizu, Andrew Eells, Seila Gonzalez +5

Wikibase -- which is the software underlying Wikidata -- is a powerful platform for knowledge graph creation and management. However, it has been developed with a crowd-sourced kno…

cs.AI2021★ 1 cited

On the Capabilities of Pointer Networks for Deep Deductive Reasoning

Monireh Ebrahimi, Aaron Eberhart, Pascal Hitzler

The importance of building neural networks that can learn to reason has been well recognized in the neuro-symbolic community. In this paper, we apply neural pointer networks for co…

cs.AI2021★ 8 cited

Neuro-Symbolic Artificial Intelligence: Current Trends

Md Kamruzzaman Sarker, Lu Zhou, Aaron Eberhart +1

Neuro-Symbolic Artificial Intelligence -- the combination of symbolic methods with methods that are based on artificial neural networks -- has a long-standing history. In this arti…

cs.AI2019★ 1 cited

Completion Reasoning Emulation for the Description Logic EL+

Aaron Eberhart, Monireh Ebrahimi, Lu Zhou +2

We present a new approach to integrating deep learning with knowledge-based systems that we believe shows promise. Our approach seeks to emulate reasoning structure, which can be i…