A hybrid reasoning system for terminologies and first-order clauses in knowledge bases

2006 ◽  
Vol 24 (1) ◽  
pp. 29-51
Author(s):  
Ken Kaneiwa
Keyword(s):  
Knowledge Bases ◽  
First Order ◽  
Reasoning System ◽  
Hybrid Reasoning

Dual systems for all: Higher-order, role-based relational reasoning as a uniquely derived feature of human cognition

2019 ◽  
Vol 42 ◽  
Author(s):  
Daniel J. Povinelli ◽  
Gabrielle C. Glorioso ◽  
Shannon L. Kuznar ◽  
Mateja Pavlic
Keyword(s):  
Temporal Reasoning ◽  
Higher Order ◽  
Important Case ◽  
Human Cognition ◽  
Relational Reasoning ◽  
Dual Systems ◽  
First Order ◽  
Reasoning System ◽  
Role Based

Abstract Hoerl and McCormack demonstrate that although animals possess a sophisticated temporal updating system, there is no evidence that they also possess a temporal reasoning system. This important case study is directly related to the broader claim that although animals are manifestly capable of first-order (perceptually-based) relational reasoning, they lack the capacity for higher-order, role-based relational reasoning. We argue this distinction applies to all domains of cognition.

scholarly journals Integrating molecular profiles into clinical frameworks through the Molecular Oncology Almanac to prospectively guide precision oncology

Nature Cancer ◽  
2021 ◽  
Author(s):  
Brendan Reardon ◽  
Nathanael D. Moore ◽  
Nicholas S. Moore ◽  
Eric Kofman ◽  
Saud H. AlDubayan ◽  
...  
Keyword(s):  
Point Of Care ◽  
Single Gene ◽  
Knowledge Bases ◽  
Computational Method ◽  
Precision Oncology ◽  
Clinical Interpretation ◽  
First Order ◽  
Molecular Features ◽  
Molecular Oncology ◽  
Molecular Profiles

AbstractTumor molecular profiling of single gene-variant (‘first-order’) genomic alterations informs potential therapeutic approaches. Interactions between such first-order events and global molecular features (for example, mutational signatures) are increasingly associated with clinical outcomes, but these ‘second-order’ alterations are not yet accounted for in clinical interpretation algorithms and knowledge bases. We introduce the Molecular Oncology Almanac (MOAlmanac), a paired clinical interpretation algorithm and knowledge base to enable integrative interpretation of multimodal genomic data for point-of-care decision making and translational-hypothesis generation. We benchmarked MOAlmanac to a first-order interpretation method across multiple retrospective cohorts and observed an increased number of clinical hypotheses from evaluation of molecular features and profile-to-cell line matchmaking. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 47% of patients. Overall, we present an open-source computational method for integrative clinical interpretation of individualized molecular profiles.

scholarly journals Detecting Inconsistencies in Large First-Order Knowledge Bases

2017 ◽  
pp. 310-325 ◽  
Author(s):  
Stephan Schulz ◽  
Geoff Sutcliffe ◽  
Josef Urban ◽  
Adam Pease
Keyword(s):  
Knowledge Bases ◽  
First Order

scholarly journals Characterizing Boundedness in Chase Variants

2020 ◽  
Vol 21 (1) ◽  
pp. 51-79
Author(s):  
STATHIS DELIVORIAS ◽  
MICHEL LECLÈRE ◽  
MARIE-LAURE MUGNIER ◽  
FEDERICO ULLIANA
Keyword(s):  
Knowledge Bases ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Universal Models ◽  
Knowing That ◽  
Significant Interest ◽  
Horn Rules ◽  
Boundedness Problem ◽  
Undecidable Problem

AbstractExistential rules are a positive fragment of first-order logic that generalizes function-free Horn rules by allowing existentially quantified variables in rule heads. This family of languages has recently attracted significant interest in the context of ontology-mediated query answering. Forward chaining, also known as the chase, is a fundamental tool for computing universal models of knowledge bases, which consist of existential rules and facts. Several chase variants have been defined, which differ on the way they handle redundancies. A set of existential rules is bounded if it ensures the existence of a bound on the depth of the chase, independently from any set of facts. Deciding if a set of rules is bounded is an undecidable problem for all chase variants. Nevertheless, when computing universal models, knowing that a set of rules is bounded for some chase variant does not help much in practice if the bound remains unknown or even very large. Hence, we investigate the decidability of the k-boundedness problem, which asks whether the depth of the chase for a given set of rules is bounded by an integer k. We identify a general property which, when satisfied by a chase variant, leads to the decidability of k-boundedness. We then show that the main chase variants satisfy this property, namely the oblivious, semi-oblivious (aka Skolem), and restricted chase, as well as their breadth-first versions.

Chapter 5. Answering Natural-Language Questions with Neuro-Symbolic Knowledge Bases

2021 ◽  
Author(s):  
Haitian Sun ◽  
Pat Verga ◽  
William W. Cohen
Keyword(s):  
Neural Networks ◽  
Query Language ◽  
Feedforward Neural Networks ◽  
Knowledge Bases ◽  
Language Models ◽  
Computationally Efficient ◽  
Knowledge Based ◽  
Reasoning System ◽  
Symbolic Reasoning ◽  
Computational Intractability

Symbolic reasoning systems based on first-order logics are computationally powerful, and feedforward neural networks are computationally efficient, so unless P=NP, neural networks cannot, in general, emulate symbolic logics. Hence bridging the gap between neural and symbolic methods requires achieving a delicate balance: one needs to incorporate just enough of symbolic reasoning to be useful for a task, but not so much as to cause computational intractability. In this chapter we first present results that make this claim precise, and then use these formal results to inform the choice of a neuro-symbolic knowledge-based reasoning system, based on a set-based dataflow query language. We then present experimental results with a number of variants of this neuro-symbolic reasoner, and also show that this neuro-symbolic reasoner can be closely integrated into modern neural language models.

scholarly journals MEBN: A language for first-order Bayesian knowledge bases

2008 ◽  
Vol 172 (2-3) ◽  
pp. 140-178 ◽  
Author(s):  
Kathryn Blackmond Laskey
Keyword(s):  
Knowledge Bases ◽  
First Order
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