scholarly journals Creation of a consulting tool and implementation of an ontology for a Master’s Degree Program in Computer Sciences

2018 ◽  
Vol 19 (1) ◽  
pp. 29-38
Author(s):  
Cecilia Reyes Peña ◽  
Mireya Tovar Vidal ◽  
Concepción Stephanie Vázquez González
Keyword(s):  
Knowledge Base ◽  
Master's Degree ◽  
Order Logic ◽  
First Order Logic ◽  
Class Diagram ◽  
Design Phase ◽  
Master Program ◽  
First Order ◽  
Python Language ◽  
Master’S Degree

In this paper, a manual ontology for a Computer Sciences Master program constructed, that uses some elements from the METHONTOLOGY, Grüninger and Fox, and Bravo’s methodologies, is presented. A series of steps to identify and represent the Master’s Degree program’s knowledge base has been followed. Afterwards, first order logic axioms and competency questions to evaluate the ontology are used. The development of a module written in Python language is used for evaluating the ontology through competency questions defined during design phase. This module is flexible enough to present predefined or defined questions by the user in running time and to obtain results to the queries representing the competency questions. Elements as a hierarchy class diagram and a description of the relations and attributes are used in this ontology’s construction. Keywords: Ontology; Python tool; SPARQL language.

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.

scholarly journals On the Expressiveness of Levesque's Normal Form

2008 ◽  
Vol 31 ◽  
pp. 259-272
Author(s):  
Y. Liu ◽  
G. Lakemeyer
Keyword(s):  
Normal Form ◽  
Knowledge Base ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Method ◽  
Closed World Assumption ◽  
First Order ◽  
Closed World ◽  
Polynomial Size

Levesque proposed a generalization of a database called a proper knowledge base (KB), which is equivalent to a possibly infinite consistent set of ground literals. In contrast to databases, proper KBs do not make the closed-world assumption and hence the entailment problem becomes undecidable. Levesque then proposed a limited but efficient inference method V for proper KBs, which is sound and, when the query is in a certain normal form, also logically complete. He conjectured that for every first-order query there is an equivalent one in normal form. In this note, we show that this conjecture is false. In fact, we show that any class of formulas for which V is complete must be strictly less expressive than full first-order logic. Moreover, in the propositional case it is very unlikely that a formula always has a polynomial-size normal form.

scholarly journals Counting Query Answers over a DL-Lite Knowledge Base

2020 ◽  
Author(s):  
Diego Calvanese ◽  
Julien Corman ◽  
Davide Lanti ◽  
Simon Razniewski
Keyword(s):  
Knowledge Base ◽  
Data Access ◽  
Upper Bounds ◽  
Order Logic ◽  
Query Answering ◽  
First Order Logic ◽  
Management Systems ◽  
Query Rewriting ◽  
Data Complexity ◽  
First Order

Counting answers to a query is an operation supported by virtually all database management systems. In this paper we focus on counting answers over a Knowledge Base (KB), which may be viewed as a database enriched with background knowledge about the domain under consideration. In particular, we place our work in the context of Ontology-Mediated Query Answering/Ontology-based Data Access (OMQA/OBDA), where the language used for the ontology is a member of the DL-Lite family and the data is a (usually virtual) set of assertions. We study the data complexity of query answering, for different members of the DL-Lite family that include number restrictions, and for variants of conjunctive queries with counting that differ with respect to their shape (connected, branching, rooted). We improve upon existing results by providing PTIME and coNP lower bounds, and upper bounds in PTIME and LOGSPACE. For the LOGSPACE case, we have devised a novel query rewriting technique into first-order logic with counting.

scholarly journals Deterministic planning in incompletely known domains with local effects

2021 ◽  
Author(s):  
Vitaliy Batusov
Keyword(s):  
Knowledge Base ◽  
Order Logic ◽  
Query Answering ◽  
First Order Logic ◽  
Situation Calculus ◽  
Local Effects ◽  
First Order ◽  
Conformant Planning ◽  
Recent Advances ◽  
Planning Algorithm

Conformant planning has been traditionally studied in the form of classical planning extended with a mechanism for expressing unknown facts and/or disjunctive knowledge. Despite a sizable body of research, most approaches do not attempt to move beyond essentially propositional planning. We address this shortcoming by defining conformant planning in terms of the situation calculus semantics and use recent advances in the fields of first-order knowledge base progression and query answering to develop a sound and complete conformant planning algorithm capable of handling knowledge defined in an expressive fragment of first-order logic. We implement a prototype planner and evaluate its performance on several existing domains.

scholarly journals Deterministic planning in incompletely known domains with local effects

2021 ◽  
Author(s):  
Vitaliy Batusov
Keyword(s):  
Knowledge Base ◽  
Order Logic ◽  
Query Answering ◽  
First Order Logic ◽  
Situation Calculus ◽  
Local Effects ◽  
First Order ◽  
Conformant Planning ◽  
Recent Advances ◽  
Planning Algorithm

Conformant planning has been traditionally studied in the form of classical planning extended with a mechanism for expressing unknown facts and/or disjunctive knowledge. Despite a sizable body of research, most approaches do not attempt to move beyond essentially propositional planning. We address this shortcoming by defining conformant planning in terms of the situation calculus semantics and use recent advances in the fields of first-order knowledge base progression and query answering to develop a sound and complete conformant planning algorithm capable of handling knowledge defined in an expressive fragment of first-order logic. We implement a prototype planner and evaluate its performance on several existing domains.

scholarly journals Abductive Learning with Ground Knowledge Base

2021 ◽  
Author(s):  
Le-Wen Cai ◽  
Wang-Zhou Dai ◽  
Yu-Xuan Huang ◽  
Yu-Feng Li ◽  
Stephen Muggleton ◽  
...  
Keyword(s):  
Machine Learning ◽  
Knowledge Base ◽  
Domain Knowledge ◽  
Ground Truth ◽  
Order Logic ◽  
Abductive Reasoning ◽  
First Order Logic ◽  
Learning Models ◽  
First Order ◽  
Machine Learning Models

Abductive Learning is a framework that combines machine learning with first-order logical reasoning. It allows machine learning models to exploit complex symbolic domain knowledge represented by first-order logic rules. However, it is challenging to obtain or express the ground-truth domain knowledge explicitly as first-order logic rules in many applications. The only accessible knowledge base is implicitly represented by groundings, i.e., propositions or atomic formulas without variables. This paper proposes Grounded Abductive Learning (GABL) to enhance machine learning models with abductive reasoning in a ground domain knowledge base, which offers inexact supervision through a set of logic propositions. We apply GABL on two weakly supervised learning problems and found that the model's initial accuracy plays a crucial role in learning. The results on a real-world OCR task show that GABL can significantly reduce the effort of data labeling than the compared methods.

A First-Order Logic of Limited Belief Based on Possible Worlds

2020 ◽  
Author(s):  
Gerhard Lakemeyer ◽  
Hector J. Levesque
Keyword(s):  
Knowledge Base ◽  
Epistemic Logic ◽  
Possible Worlds ◽  
Order Logic ◽  
First Order Logic ◽  
Possible World ◽  
Possible World Semantics ◽  
First Order ◽  
Epistemic States

In a recent paper Lakemeyer and Levesque proposed a first-order logic of limited belief to characterize the beliefs of a knowledge base (\KB). Among other things, they show that their model of belief is expressive, eventually complete, and tractable. This means, roughly, that a \KB\ may consist of arbitrary first-order sentences, that any sentence which is logically entailed by the \KB\ is eventually believed, given enough reasoning effort, and that reasoning is tractable under reasonable assumptions. One downside of the proposal is that epistemic states are defined in terms of sets of clauses, possibly containing variables, giving the logic a distinct syntactic flavour compared to the more traditional possible-world semantics found in the literature on epistemic logic. In this paper we show that the same properties as above can be obtained by defining epistemic states as sets of three-valued possible worlds. This way we are able to shed new light on those properties by recasting them using the more familiar notion of truth over possible worlds.

Improved knowledge management through first-order logic in engineering design ontologies

2009 ◽  
Vol 24 (2) ◽  
pp. 245-257 ◽  
Author(s):  
Paul Witherell ◽  
Sundar Krishnamurty ◽  
Ian R. Grosse ◽  
Jack C. Wileden
Keyword(s):  
Knowledge Management ◽  
Knowledge Acquisition ◽  
Knowledge Base ◽  
Engineering Design ◽  
Design Process ◽  
Order Logic ◽  
First Order Logic ◽  
Test Bed ◽  
First Order ◽  
Engineering Design Process

AbstractThis paper presents the use of first-order logic to improve upon currently employed engineering design knowledge management techniques. Specifically, this work uses description logic in unison with Horn logic, to not only guide the knowledge acquisition process but also to offer much needed support in decision making during the engineering design process in a distributed environment. The knowledge management methods introduced are highlighted by the ability to identify modeling knowledge inconsistencies through the recognition of model characteristic limitations, such as those imposed by model idealizations. The adopted implementation languages include the Semantic Web Rule Language, which enables Horn-like rules to be applied to an ontological knowledge base and the Semantic Web's native Web Ontology Language. As part of this work, an ontological tool, OPTEAM, was developed to capture key aspects of the design process through a set of design-related ontologies and to serve as an application platform for facilitating the engineering design process. The design, analysis, and optimization of a classical I-beam problem are presented as a test-bed case study to illustrate the capabilities of these ontologies in OPTEAM. A second, more extensive test-bed example based on an industry-supplied medical device design problem is also introduced. Results indicate that well-defined, networked relationships within an ontological knowledge base can ultimately lead to a refined design process, with guidance provided by the identification of infeasible solutions and the introduction of “best-case” alternatives. These case studies also show how the application of first-order logic to engineering design improves the knowledge acquisition, knowledge management, and knowledge validation processes.

First-Order Logic Reasoning Support for the Semantic Web

2009 ◽  
Vol 19 (12) ◽  
pp. 3091-3099 ◽  
Author(s):  
Gui-Hong XU ◽  
Jian ZHANG
Keyword(s):  
Semantic Web ◽  
Order Logic ◽  
First Order Logic ◽  
First Order ◽  
Logic Reasoning

Boolean-valued structures

2018 ◽  
Author(s):  
Tim Button ◽  
Sean Walsh
Keyword(s):  
Model Theory ◽  
Order Logic ◽  
First Order Logic ◽  
Inference Rules ◽  
Mathematical Concepts ◽  
Semantic Framework ◽  
First Order ◽  
Standard Models ◽  
Boolean Valued Model ◽  
Semantic Values

Chapters 6-12 are driven by questions about the ability to pin down mathematical entities and to articulate mathematical concepts. This chapter is driven by similar questions about the ability to pin down the semantic frameworks of language. It transpires that there are not just non-standard models, but non-standard ways of doing model theory itself. In more detail: whilst we normally outline a two-valued semantics which makes sentences True or False in a model, the inference rules for first-order logic are compatible with a four-valued semantics; or a semantics with countably many values; or what-have-you. The appropriate level of generality here is that of a Boolean-valued model, which we introduce. And the plurality of possible semantic values gives rise to perhaps the ‘deepest’ level of indeterminacy questions: How can humans pin down the semantic framework for their languages? We consider three different ways for inferentialists to respond to this question.

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