Knowledge Representation Applications Research Articles

Adjunctive three-way concepts from positive and negative concepts in lattice-valued formal contexts

Three-way concept analysis is an integration of formal concept analysis and three-way decision, which has been found wide applications in knowledge representation, cognitive computing and decision sciences. There have been already a huge number of literature on theory and applications on such topics. Most researches on formal concept analysis focus, independently, on specific scenarios such as Boolean formal contexts and fuzzy formal contexts. In this paper, a unified framework for three-way concept analysis is built in lattice-valued formal contexts. The notions of positive concept and negative concept are given in a lattice-valued formal context based on an overlapping function, one of significant operators for information fusion. Two pairs of object induced adjunctive three-way concept operators and attribute induced adjunctive three-way concept operators are introduced. Their properties have been fully investigated. With those operators, the algebraic structures of adjunctive three-way concepts, their extensions and intensions have been explored. Specifically, the local structure within pairs of sub-concept and sup-concept has been finely characterized. The derived results provide ones a way to construct the complete lattice of a lattice-valued formal context or its sublattice by determining all pairs of sub-concept and sup-concept provided that an object fuzzy set or an attribute fuzzy set is given. A case analysis on a real world dataset from UCI repository is presented to verify the rationality and significance of obtained results.

PToPI: A Comprehensive Review, Analysis, and Knowledge Representation of Binary Classification Performance Measures/Metrics.

Although few performance evaluation instruments have been used conventionally in different machine learning-based classification problem domains, there are numerous ones defined in the literature. This study reviews and describes performance instruments via formally defined novel concepts and clarifies the terminology. The study first highlights the issues in performance evaluation via a survey of 78 mobile-malware classification studies and reviews terminology. Based on three research questions, it proposes novel concepts to identify characteristics, similarities, and differences of instruments that are categorized into ‘performance measures’ and ‘performance metrics’ in the classification context for the first time. The concepts reflecting the intrinsic properties of instruments such as canonical form, geometry, duality, complementation, dependency, and leveling, aim to reveal similarities and differences of numerous instruments, such as redundancy and ground-truth versus prediction focuses. As an application of knowledge representation, we introduced a new exploratory table called PToPI (Periodic Table of Performance Instruments) for 29 measures and 28 metrics (69 instruments including variant and parametric ones). Visualizing proposed concepts, PToPI provides a new relational structure for the instruments including graphical, probabilistic, and entropic ones to see their properties and dependencies all in one place. Applications of the exploratory table in six examples from different domains in the literature have shown that PToPI aids overall instrument analysis and selection of the proper performance metrics according to the specific requirements of a classification problem. We expect that the proposed concepts and PToPI will help researchers comprehend and use the instruments and follow a systematic approach to classification performance evaluation and publication.

Belief functions and rough sets: Survey and new insights

Rough set theory and belief function theory, two popular mathematical frameworks for uncertainty representation, have been widely applied in different settings and contexts. Despite different origins and mathematical foundations, the fundamental concepts of the two formalisms (i.e., approximations in rough set theory, belief and plausibility functions in belief function theory) are closely related. In this survey article, we review the most relevant contributions studying the links between these two uncertainty representation formalisms. In particular, we discuss the theoretical relationships connecting the two approaches, as well as their applications in knowledge representation and machine learning. Special attention is paid to the combined use of these formalisms as a way of dealing with imprecise and uncertain information. The aim of this work is, thus, to provide a focused picture of these two important fields, discuss some known results and point to relevant future research directions.

Forgetting in multi-agent modal logics

In the past decades, forgetting has been investigated for many logics and has found many applications in knowledge representation and reasoning. In this paper, we study forgetting in multi-agent modal logics. We adopt the semantic definition of existential bisimulation quantifiers as that of forgetting. We resort to canonical formulas of modal logics introduced by Moss. An arbitrary modal formula is equivalent to the disjunction of a unique set of satisfiable canonical formulas. We show that, for the logics of Kn, Dn, Tn, K45n, KD45n and S5n, the result of forgetting an atom from a satisfiable canonical formula can be computed by simply substituting the literals of the atom with ⊤. Thus we show that these logics are closed under forgetting, and hence have uniform interpolation. Finally, we generalize the above results to include common knowledge of propositional formulas.

Safe inductions and their applications in knowledge representation

In many knowledge representation formalisms, a constructive semantics is defined based on sequential applications of rules or of a semantic operator. These constructions often share the property that rule applications must be delayed until it is safe to do so: until it is known that the condition that triggers the rule will continue to hold. This intuition occurs for instance in the well-founded semantics of logic programs and in autoepistemic logic. In this paper, we formally define the safety criterion algebraically. We study properties of so-called safe inductions and apply our theory to logic programming and autoepistemic logic. For the latter, we show that safe inductions manage to capture the intended meaning of a class of theories on which all classical constructive semantics fail.

Knowledge means ‘all’, belief means ‘most’

We introduce a bimodal epistemic logic intended to capture knowledge as truth in all epistemically alternative states and belief as a generalised ‘majority’ quantifier, interpreted as truth in most (i.e. a ‘majority’) of the epistemically alternative states. This doxastic interpretation is of interest in knowledge-representation applications and it also holds an independent philosophical and technical appeal. The logic comprises an epistemic modal operator, a doxastic modal operator of consistent and complete belief and ‘bridge’ axioms which relate knowledge to belief. To capture the notion of a ‘majority’ we use the ‘large sets’ introduced independently by K. Schlechta and V. Jauregui, augmented with a requirement of completeness, which furnishes a ‘weak ultrafilter’ concept. We provide semantics in the form of possible-worlds frames, properly blending relational semantics with a version of general Scott–Montague (neighbourhood) frames and we obtain soundness and completeness results. We examine the validity of certain epistemic principles discussed in the literature, in particular some of the ‘bridge’ axioms discussed by W. Lenzen and R. Stalnaker, as well as the ‘paradox of the perfect believer’, which is not a theorem of .

Grounded fixpoints and their applications in knowledge representation

In various domains of logic, researchers have made use of a similar intuition: that facts (or models) can be derived from the ground up. They typically phrase this intuition by saying, e.g., that the facts should be grounded, or that they should not be unfounded, or that they should be supported by cycle-free arguments, et cetera. In this paper, we formalise this intuition in the context of algebraical fixpoint theory. We define when a lattice element x∈L is grounded for lattice operator O:L→L. On the algebraical level, we investigate the relationship between grounded fixpoints and the various classes of fixpoints of approximation fixpoint theory, including supported, minimal, Kripke–Kleene, stable and well-founded fixpoints. On the logical level, we investigate groundedness in the context of logic programming, autoepistemic logic, default logic and argumentation frameworks. We explain what grounded points and fixpoints mean in these logics and show that this concept indeed formalises intuitions that existed in these fields. We investigate which existing semantics are grounded. We study the novel semantics for these logics that is induced by grounded fixpoints, which has some very appealing properties, not in the least its mathematical simplicity and generality. Our results unveil a remarkable uniformity in intuitions and mathematics in these fields.

An analysis of fundamental concepts in the conceptual framework using ontology technologies

The interpretation of financial data obtained from the accounting process for reporting purposes is regulated by financial accounting standards (FAS). The history and mechanisms used for the development of ʻThe Conceptual Framework for Financial Reporting’ (the Conceptual Framework) as well as the financial accounting standards resulted in impressive volumes of material that guides modern financial reporting practices, but unfortunately, as is often the case with textual manuscripts, it contains descriptions that are vague, inconsistent or ambiguous. As part of the on-going initiatives to improve International Financial Reporting Standards (IFRS), the International Accounting Standards Board (IASB) promotes the development of principle-based IFRS, which aim to address the problems of vagueness, inconsistency and ambiguity. This paper reports on the findings of a design science research (DSR) project that, as artefact, developed a first version ontology-based formal language representing the definitions of asset, liability and equity (the fundamental elements of the statement of financial position as defined in the Conceptual Framework) through the application of knowledge representation (ontology) techniques as used within computing. We suggest that this artefact may assist with addressing vagueness, inconsistencies and ambiguities within the definitions of the Conceptual Framework. Based on our findings, we include suggestions for the further development of a formal language and approach to assist the formulation of the Conceptual Framework. The project focuses on the Conceptual Framework for Financial Reporting after the incorporation of Phase A in the convergence project between the Financial Accounting Standards Board (FASB) and IASB.

The Application of Knowledge Representation on the Design of Loading Device of Garbage Truck

To meet the requirement of mass customization of garbage truck, knowledge engineering plays an important part in modern garbage truck design. As the basis of knowledge engineering application, the principles of common knowledge representation methods were analyzed in detail, and the application places of different knowledge representation methods were presented in the paper. Finally, examples of production knowledge representation, sate space representation, object-oriented knowledge representation, and semantic network-based knowledge representation were introduced independently to show the feasibility of applying these methods on the design of loading device of garbage truck.

Medical Treatment Conflict Resolving in Answer Set Programming

Medical treatment decision making is a good application of knowledge representation and reasoning. We are particularly interested in using it to resolve treatment conflicts, a complicated condition when two treatments cannot be given simultaneously to a patient of multiple symptoms. The logic system is required to reason on cases with and without treatment conflicts. Thanks to the nonmonotonicity of Answer Set Programming (ASP), we elegantly automate medical treatment conflict resolving on an example problem and show the importance of nonmonotonicity in medical reasoning.

Knowledge Processing for Cognitive Robots

Knowledge processing methods are an important resource for robots that perform challenging tasks in complex, dynamic environments. When applied to robot control, such methods allow to write more general and flexible control programs and enable reasoning about the robot’s observations, the actions involved in a task, action parameters and the reasons why an action was performed. However, the application of knowledge representation and reasoning techniques to autonomous robots creates several hard research challenges. In this article, we discuss some of these challenges and our approaches to solving them.

A survey of formalisms for representing and reasoning with scientific knowledge

AbstractWith the rapid growth in the quantity and complexity of scientific knowledge available for scientists, and allied professionals, the problems associated with harnessing this knowledge are well recognized. Some of these problems are a result of the uncertainties and inconsistencies that arise in this knowledge. Other problems arise from heterogeneous and informal formats for this knowledge. To address these problems, developments in the application of knowledge representation and reasoning technologies can allow scientific knowledge to be captured in logic-based formalisms. Using such formalisms, we can undertake reasoning with the uncertainty and inconsistency to allow automated techniques to be used for querying and combining of scientific knowledge. Furthermore, by harnessing background knowledge, the querying and combining tasks can be carried out more intelligently. In this paper, we review some of the significant proposals for formalisms for representing and reasoning with scientific knowledge.

Knowledge representation and applied decision making (KREAM)

Abstract The aim of this workshop is to provide a forum for discussion and debate on the application of knowledge representation and ontologies in computational science and the techniques used for the manipulation of such data. Examples of topics of interest include: the gene ontology with combined research from knowledge representation and data mining techniques; evaluation of social capital encompassing all the value-producing aspects that emerge from the application of computational techniques to networks of social ties and the dynamics of social interaction; financial computing with representations such as XBRL; etc.

Notions of Bisimulation for Heyting-Valued Modal Languages

We examine the notion of bisimulation and its ramifications, in the context of the family of Heyting-valued modal languages introduced by M. Fitting. Each modal language in this family is built on an underlying space of truth values, a Heyting algebra H⁠. All the truth values are directly represented in the language, which is interpreted on relational frames with an H-valued accessibility relation. We define two notions of bisimulation that allow us to obtain truth invariance results. We provide game semantics and, for the more interesting and complicated notion, we are able to provide characteristic formulae and prove a Hennessy–Milner-type theorem. If the underlying algebra H is finite, Heyting-valued modal models can be equivalently reformulated to a form relevant to epistemic situations with many interrelated experts. Our definitions and results draw inspiration from this formulation, which is of independent interest to Knowledge Representation applications.

Knowledge Representation in Civil and Structural Engineering

In this paper we survey the literature of both Knowledge Representation and Civil Engineering regarding the Applications of Knowledge Representation and Reasoning Techniques to Civil and Structural Engineering. The reviewed works are analyzed in terms of the scope, objectives and approach employed in the investigation, and compared to each other based on a preliminary analysis of the problems posed by the application fields. The conclusions of the review are fundamentally three: (1) the need for deeper investigations of the applicability of Artificial Intelligence approaches to Civil and Structural Engineering, especially in view of the new techniques and methods lately developed; (2) the need for new systematic analysis of the domain of Civil and Structural Engineering, in particular from an ontological point of view; (3) the expectation of the birth of a new discipline, as the junction of civil and structural engineering with artificial intelligence, whose specific AI point of view will be centered on Formal Ontology. For this new discipline we propose the name of Structurotics.

Knowledge Representation issues in structural engineering: a framework for application in the case of structures in healthcare

We survey the literature on Applications of Knowledge Representation and Reasoning Techniques to Structural Engineering. We focus on the aspects related to healthcare: Hospitals and Structures related to accidents and emergencies, which are actors of the healthcare system. The conclusions of the review are three: focus is necessarily on actual systems for industrial uses; there are three trends emerging in recent investigations in the field: ontological foundations, collaboration management and soft computing; these are the basis for the a 'new' discipline: Structurotics. The investigation is ended with an example in a concrete application in the field of Tunnel Upgrade.

GASP: Answer Set Programming with Lazy Grounding

In recent years, Answer Set Programming has gained popularity as a viable paradigm for applications in knowledge representation and reasoning. This paper presents a novel methodology to compute answer sets of an answer set program. The proposed methodology maintains a bottom-up approach to the computation of answer sets (as in existing systems), but it makes use of a novel structuring of the computation, that originates from the non-ground version of the program. Grounding is lazily performed during the computation of the answer sets. The implementation has been realized using Constraint Logic Programming over finite domains.

Knowledge Representation in Civil and Structural Engineering

In this paper we survey the literature of both Knowledge Representation and Civil Engineering regarding the Applications of Knowledge Representation and Reasoning Techniques to Civil and Structural Engineering. The reviewed works are analysed in terms of the scope, objectives and approach employed in the investigation, and compared to each other based on a preliminary analysis of the problems posed by the application fields. Recent patents considered here to complete the survey can be efficiently used in engineering applications, e.g. land development problems, engineering calculations or structural engineering projects (through the generation of a fuzzy implication operator). The conclusions of the review are fundamentally three: (1) the need for deeper investigations of the applicability of Artificial Intelligence approaches to Civil and Structural Engineering, especially in view of the new techniques and methods lately developed; (2) the need for new systematic analysis of the domain of Civil and Structural Engineering, in particular from an ontological point of view; (3) the expectation of the birth of a new discipline, as the junction of civil and structural engineering with artificial intelligence, whose specific AI point of view will be centred on Formal Ontology. For this new discipline we propose the name of Structurotics, that is intended to refer both to the novel applications based upon the above mentioned trends, and to the consequent theoretical investigations.

Hypothetical revision and matter-of-fact supposition

The recent literature offers several models of the notion of matter of fact supposition1 revealed in the acceptance of the so-called indicative conditionals. Some of those models are qualitative [Collins 90], [Levi 96], [Stalnaker 84]. Other probabilistic models appeal either to infinitesimal probability or two place probability functions ([Adams 75], [van Fraassen 95], [Joyce 99], [McGee 94]). Recent work has made possible to understand which is the exact qualitative counterpart of the latter probabilistic models. In this article we show that the qualitative notion of change that thus arises is hypothetical revision, a notion previously axiomatized in [Arló-Costa 97] and [Arló-Costa & Thomason 96]. This notion is incompatible with AGM as well as with other standard methods of theory change (like Katsuno and Mendelzon's UPDATE). The way in which matter-of-fact supposition is modeled by hypothetical revision is illustrated via examples. The model is compared with other qualitative accounts of the notion of supposition encoded in two-place probability functions (like the one offered in [Hajek & Harper 96]), with models of subjunctive supposition, as well as with some of the well know models of learning. Applications in knowledge representation and in the theory of games and decisions are summarized.

Prioritized logic programming and its application to commonsense reasoning

Representing and reasoning with priorities are important in commonsense reasoning. This paper introduces a framework of prioritized logic programming (PLP), which has a mechanism of explicit representation of priority information in a program. When a program contains incomplete or indefinite information, PLP is useful for specifying preference to reduce non-determinism in logic programming. Moreover, PLP can realize various forms of commonsense reasoning in AI such as abduction, default reasoning, circumscription, and their prioritized variants. The proposed framework increases the expressive power of logic programming and exploits new applications in knowledge representation.