Logic Knowledge Representation Research Articles

LHP: Logical hypergraph link prediction

Logical knowledge mining has become increasingly important as it is the foundation of precise reasoning, such as for clinical diagnosis or chemical reaction discovery. Knowledge hypergraphs provide a natural format for expressing inherently high-order relationships beyond pairwise associations. Although logical knowledge (the conjunct operator in first-order logic) is apparently a higher-order relation that can be represented by a hypergraph, methods of representing logical knowledge in this form and completing knowledge by hyperlink prediction have not yet been explored. In this study, logical knowledge is represented by directed hyperedges and effectively quantified by geometric operations in a neural network. The proposed logical hyperlink predictor (LHP) leverages logical knowledge features including permutation invariant and information aggregation of the logical ‘conjunct’ operation. LHP captures logical representation as a whole unit containing the different relationship types embedded within separate hyperedges, making it the novel method to integrate logical knowledge contained within hyperedges. We conduct extensive experiments on the iAF1260b, iJO1366, USPTO, and our Chinese Medical High-order Relational (CMHR) dataset. LHP achieved best performance with linear complexity compared to the state-of-the-art hyperlink prediction methods on a mean area under the receiver operating characteristic curve (AUC), Macro_F1 and accuracy. It was particularly effective on the CMHR demonstrating the importance of logical knowledge representation in the medical field. LHP also achieved a mean AUC of 0.924 in performing hyperedge relationship classification on the CMHR.

A Survey of Knowledge-Based Intelligent Fault Diagnosis Techniques

With the development of information technologies, more and more real-time data can be obtained from production and operation process. Thus, how to extract effective information from these massive data, so as to carry out in-depth statistics and mining of faults, and gradually explore the faults laws and causes are crucial for intelligent factories. In recent years, a variety of statistical learning and data analysis methods have been used in fault diagnosis. Due to the complex structure, multi-source failure and suddenness of the industrial production system, the combination of empirical knowledge and mechanism principles can solve various fault problems. This paper summarizes several commonly used fault diagnosis methods, and focuses on knowledge-based intelligent fault diagnosis, including first-order logic knowledge representation method, production knowledge representation method, framework knowledge representation method, object-oriented knowledge representation method and Semantic-based knowledge representation methods.

A Logical Theory of Localization

A central problem in applying logical knowledge representation formalisms to traditional robotics is that the treatment of belief change is categorical in the former, while probabilistic in the latter. A typical example is the fundamental capability of localization where a robot uses its noisy sensors to situate itself in a dynamic world. Domain designers are then left with the rather unfortunate task of abstracting probabilistic sensors in terms of categorical ones, or more drastically, completely abandoning the inner workings of sensors to black-box probabilistic tools and then interpreting their outputs in an abstract way. Building on a first-principles approach by Bacchus, Halpern and Levesque, and a recent continuous extension to it by Belle and Levesque, we provide an axiomatization that shows how localization can be realized wrt a basic action theory, thereby demonstrating how such capabilities can be enabled in a single logical framework. We then show how the framework can also enable localization for multiple agents, where an agent can appeal to the sensing already performed by another agent and the knowledge of their relative positions to localize itself.

개념그래프 도구의 기능 및 특성 조사

Abstract Intelligent systems are systems that mainly use knowledge rather than data or information. Therefore, knowledge representation is an important factor for intelligent system construction. Conceptual graph is a logical knowledge representation language which has graphical form and it can represent knowledge efficiently. It is, however, cumbersome to use conceptual graphs directly for programming. Various tools were developed to overcome this difficulties. In this paper, we survey on functions and characteristics of conceptual graph tools that can be utilized for constructing intelligent systems by using conceptual graphs. The result of this survey will be very helpful to use conceptual graphs for development of intelligent systems. Keywords : Conceptual Graph, Intelligent System, Knowledge Representation, Conceptual Graph Tool Received 12 September 2014, Revised 25 October 2014Accepted 20 December 2014Corresponding Author: Gi-Chul Yang(Mokpo National University) Email: gcyang@mokpo.ac.krⒸ The Society of Digital Policy & Management. All rights reserved. This is an open-access article distributed under the terms of the Creative Commons Attribution Non-Commercial License (http://creativecommons.org/licenses/by-nc/3.0), which permits unrestricted non-commercial use, distribution, and reproduction in any medium, provided the original work is ISSN: 1738-1916 properly cited.

Exploiting unified modelling language (UML) as a preliminary design tool for Common Logic-based ontologies in manufacturing

This paper proposes a particular method which utilises the unified modelling language (UML) as a design visualisation tool for modelling ontologies based on the Common Logic knowledge representation language. The use of this method will enable Common Logic ontological concepts to be more readily accessible to general engineers and provide a valuable ontology design aid. The method proposed is explored using the knowledge frame language (KFL) which provides constructs to facilitate ontology building and is built on Common Logic. The major constructs of KFL are briefly defined and a description of how each construct may be represented in UML is given. Examples are presented showing how the constructs may be modelled in UML and a Common Logic-based implementation founded on a UML design is illustrated and discussed. The manufacturing domain is utilised as an experimental basis for demonstrating the proposed method.

Normalization Strategy of Logical Knowledge Representation for Text Document

This paper discussed the idea of the computer system capable of simulating understanding with respect to reading a text document. The research is concerned with the problem of generating sophisticated knowledge representation for the purpose of understanding the natural language. Due to that, a simplification form of logical-oriented model of knowledge representation called Pragmatic Skolem Clauses (PSC) is proposed to represent the semantic formalism for the computational linguistic. Each set of pragmatic skolem clauses containing at least one skolem constant, which shows the thematic role relationship between clauses. Semantically and pragmatically-accented approach will be discussed in this paper in the context of formal grammar and linguistic semantic.

Inference in probabilistic logic programs with continuous random variables

AbstractProbabilistic Logic Programming (PLP), exemplified by Sato and Kameya's PRISM, Poole's ICL, Raedt et al.'s ProbLog and Vennekens et al.'s LPAD, is aimed at combining statistical and logical knowledge representation and inference. However, the inference techniques used in these works rely on enumerating sets of explanations for a query answer. Consequently, these languages permit very limited use of random variables with continuous distributions. In this paper, we present a symbolic inference procedure that uses constraints and represents sets of explanations without enumeration. This permits us to reason over PLPs with Gaussian or Gamma-distributed random variables (in addition to discrete-valued random variables) and linear equality constraints over reals. We develop the inference procedure in the context of PRISM; however the procedure's core ideas can be easily applied to other PLP languages as well. An interesting aspect of our inference procedure is that PRISM's query evaluation process becomes a special case in the absence of any continuous random variables in the program. The symbolic inference procedure enables us to reason over complex probabilistic models such as Kalman filters and a large subclass of Hybrid Bayesian networks that were hitherto not possible in PLP frameworks.

Two-level pattern recognition in a class of knowledge-based systems

In the paper, the problem of pattern recognition in a two-level expert system with the logical knowledge representation is investigated. In the first part, formulation and solution algorithm of this problem are presented. To solve the pattern recognition problem, a logic-algebraic method is used. In the second part, it is shown how the method of learning may be applied to the pattern recognition system with unknown parameters in knowledge representation. The concepts considered here are illustrated by a simple numerical example.

Application of learning algorithms and uncertain variables in knowledge-based pattern recognition

A class of knowledge-based pattern recognition problems with relational and logical knowledge representation is formulated. In the first part, the learning algorithm consisting in using the learning sequence for astep-by-step evaluation, and updating of the knowledge is described. In the second part, the application of so-called uncertain variables and certainty distributions given by an expert is presented for an independent recognition problem and for recognition in a simple decision-making system. Simple examples and the results of simulations illustrating the general methods are included.

Learning algorithms in a class of knowledge-based systems

A control system with a static plant described by knowledge representation in the forms of relations and of logical formulas is considered. The learning process consists in using successive knowledge validation and updating it to make current control decisions. Two approaches and algorithms are described: one for the validation and updating of knowledge about the plant, and one on the form of control. In both cases two versions are presented: the learning process in either an open-loop or a closed-loop control system. For a plant with logical knowledge representation, the logic-algebraic method is applied.

Semantics and complexity of abduction from default theories

Abductive reasoning (roughly speaking, find an explanation for observations out of hypotheses) has been recognized as an important principle of common-sense reasoning. Since logical knowledge representation is commonly based on nonclassical formalisms like default logic, autoepistemic logic, or circumscription, it is necessary to perform abductive reasoning from theories (i.e., knowledge bases) of nonclassical logics. In this paper, we investigate how abduction can be performed from theories in default logic. In particular, we present a basic model of abduction from default theories. Different modes of abduction are plausible, based on credulous and skeptical default reasoning; they appear useful for different applications such as diagnosis and planning. Moreover, we thoroughly analyze the complexity of the main abductive reasoning tasks, namely finding an explanation, deciding relevance of a hypothesis and deciding necessity of a hypothesis. These problems are intractable even in the prepositional case and we locate them into the appropriate slots of the polynomial hierarchy. However, we also present known classes of default theories for which abduction is tractable. Moreover, we also consider first-order default theories, based on domain closure and the unique names assumption. In this setting, the abduction tasks are decidable, but have exponentially higher complexity than in the propositional case.

A new methodology of fuzzy constraint-based controller design via constraint-network processing

A complete design framework for a constraint-based controller based on fuzzy-constraint processing and its semantics and relationship to logic is presented. In this paper, the concept of fuzzy in problem solving is introduced, and some basic definitions of fuzzy-constraint processing in a constraint network and its semantic modeling are addressed. Then a local propagation inference mechanism for reasoning about imprecise information applying the filter operation in a network of constraints is proposed. Moreover, we advance the concurrent fuzzy-logic controller (FLC) to a new type of controller, the constraint-based controller (FCC), using a more general predicate calculus and full first-order logic knowledge representation and making use of the idea of fuzzy-constraint processing to model practical dynamic control systems. Finally, simulation results show that a FCC achieves equivalent performance as PD type and PI type FLCs and it also demonstrates superior outcomes to a conventional PID controller in terms of rise time and peak-percent overshoot.

Advanced CAD Environments: A Knowledge‐Based Foundation

Abstract: Current computer‐aided design (CAD) systems are extremely powerful tools for the development of products in an industrial setting. However, they still leave a lot to be desired when it comes to fulfilling a designer's demands and dreams. We believe that knowledge engineering, or more broadly artificial intelligence (AI), is a promising candidate for providing advanced design environments. This paper proposes assorted techniques from AI—such as logical knowledge representation, naive physics, and commonsense reasoning—as effective means of obtaining such environments.In order to make adept, temporal comments, an architecture machine must have a certain basic understanding of qualities. Though at first primitive, this qualitative appreciation itself would evolve within a value system that is very personal, between a man and a machine.

Logical knowledge-representation formalisms

The resolution-like graph grammar is described and solvability of the extended Ackermann class is proved.

A knowledge-based system for computer-aided architectural design for energy savings and thermal comfort

This paper describes EKSPRO, a knowledge-based system integrating a 3-D computer-aided design system for materials, heating, ventilation, lighting equipment, building codes and occupational health regulations, and design guidelines user-defined by architects and engineers. The system features an object-oriented predicate logic knowledge representation, and interfaces with calculation packages (CAD, thermal balance, illumination, daylight).

Geometric and conceptual knowledge representation within a generative model of visual perception

A representation scheme of knowledge at both the geometric and conceptual levels is offered which extends a generative theory of visual perception. According to this theory, the perception process proceeds through different scene representations at various levels of abstraction. The geometric domain is modeled following the CSG (constructive solid geometry) approach, taking advantage of the geometric modelling scheme proposed by A. Pentland, based on superquadrics as representation primitives. Recursive Boolean combinations and deformations are considered in order to enlarge the scope of the representation scheme and to allow for the construction of real-world scenes. In the conceptual domain, objects and relationships are represented using KL-ONE, a frame-based knowledge representation formalism which allows hierarchical and structural descriptions. Questions arising from the integration of logical and analogical knowledge representation are also faced; in the end likeness and approximation relationships between objects and prototypical conceptual models for classification purposes are investigated within the framework of fuzzy set theory.