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 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 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.

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

scholarly journals Towards closed world reasoning in dynamic open worlds

2010 ◽  
Vol 10 (4-6) ◽  
pp. 547-563 ◽  
Author(s):  
MARTIN SLOTA ◽  
JOÃO LEITE
Keyword(s):  
Semantic Web ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Order Logic ◽  
First Order Logic ◽  
Static Case ◽  
First Order ◽  
Closed World ◽  
Hybrid Knowledge ◽  
First Time

AbstractThe need for integration of ontologies with nonmonotonic rules has been gaining importance in a number of areas, such as the Semantic Web. A number of researchers addressed this problem by proposing a unified semantics forhybrid knowledge basescomposed of both an ontology (expressed in a fragment of first-order logic) and nonmonotonic rules. These semantics have matured over the years, but only provide solutions for the static case when knowledge does not need to evolve.In this paper we take a first step towards addressing the dynamics of hybrid knowledge bases. We focus on knowledge updates and, considering the state of the art of belief update, ontology update and rule update, we show that current solutions are only partial and difficult to combine. Then we extend the existing work on ABox updates with rules, provide a semantics for such evolving hybrid knowledge bases and study its basic properties.To the best of our knowledge, this is the first time that an update operator is proposed for hybrid knowledge bases.

scholarly journals Clinical interpretation of integrative molecular profiles to guide precision cancer medicine

2020 ◽  
Author(s):  
Brendan Reardon ◽  
Nathaniel D Moore ◽  
Nicholas Moore ◽  
Eric Kofman ◽  
Saud Aldubayan ◽  
...  
Keyword(s):  
Single Gene ◽  
Knowledge Bases ◽  
Second Order ◽  
Computational Method ◽  
Precision Oncology ◽  
Clinical Interpretation ◽  
Cancer Data ◽  
First Order ◽  
Molecular Features ◽  
Molecular Profiles

ABSTRACTIndividual tumor molecular profiling is routinely used to detect single gene-variant (“first-order”) genomic alterations that may inform therapeutic actions -- for instance, a tumor with a BRAF p.V600E variant might be considered for RAF/MEK inhibitor therapy. Interactions between such first-order events (e.g., somatic-germline) and global molecular features (e.g. mutational signatures) are increasingly associated with clinical outcomes, but these “second order” alterations are not yet generally accounted for in clinical interpretation algorithms and knowledge bases. Here, we introduce the Molecular Oncology Almanac (MOAlmanac), a clinical interpretation algorithm paired with a novel underlying knowledge base to enable integrative interpretation of genomic and transcriptional cancer data for point-of-care treatment decision-making and translational hypothesis generation. We compared MOAlmanac to first-order interpretation methodology in multiple retrospective patient cohorts and observed that the inclusion of preclinical and inferential evidence as well as second-order molecular features increased the number of nominated clinical hypotheses. MOAlmanac also performed matchmaking between patient molecular profiles and cancer cell lines to further expand individualized clinical actionability. When applied to a prospective precision oncology trial cohort, MOAlmanac nominated a median of two therapies per patient and identified therapeutic strategies administered in 46% of patient profiles. Overall, we present a novel computational method to perform integrative clinical interpretation of individualized molecular profiles. MOAlmanc increases clinical actionability over conventional approaches by considering second-order molecular features and additional evidence sources, and is available as an open-source framework.

Principles and practice in verifying rule-based systems

1992 ◽  
Vol 7 (2) ◽  
pp. 115-141 ◽  
Author(s):  
Alun D. Preece ◽  
Rajjan Shinghal ◽  
Aïda Batarekh
Keyword(s):  
Expert System ◽  
Knowledge Base ◽  
State Of The Art ◽  
Knowledge Bases ◽  
Order Logic ◽  
First Order Logic ◽  
Rule Based ◽  
First Order ◽  
Rule Bases ◽  
System Knowledge

AbstractThis paper surveys the verification of expert system knowledge bases by detecting anomalies. Such anomalies are highly indicative of errors in the knowledge base. The paper is in two parts. The first part describes four types of anomaly: redundancy, ambivalence, circularity, and deficiency. We consider rule bases which are based on first-order logic, and explain the anomalies in terms of the syntax and semantics of logic. The second part presents a review of five programs which have been built to detect various subsets of the anomalies. The four anomalies provide a framework for comparing the capabilities of the five tools, and we highlight the strengths and weaknesses of each approach. This paper therefore provides not only a set of underlying principles for performing knowledge base verification through anomaly detection, but also a survey of the state-of-the-art in building practical tools for carrying out such verification. The reader of this paper is expected to be familiar with first-order logic.

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