Knowledge Representation Issues Research Articles

Meta-Context Ontology for Self-Adaptive Mobile Web Service Discovery in Smart Systems

Self-adaptive mobile web service (MWS) discovery in Smart Systems depends on heterogeneous context-sensitive attributes that are composed dynamically to satisfy the user’s MWS request despite the constantly changing environment. Several ontologies are developed to deal with the issues of heterogeneous knowledge representation in smart systems. Unfortunately, these ontologies are mostly inexpressive, unextendible, constricted, and slightly fragmented due to a lack of socially focused ontology development procedures. In this paper, a context ontology for self-adaptive MWS discovery in smart systems is proposed to provide a better representation of the heterogeneous context for smart systems and facilitate the delivery of MWS. The lightweight unified process for ontology building (UPON Lite) is adopted for ontology development. The ontology is experimentally evaluated in protégé using real-world smart systems from the healthcare and agriculture domain. Consequently, the ontology was found to be more expressive, extensible, and support self-adaptive MWS discovery in Smart Systems.

Existence, Hypotheses and Categories in Knowledge Representation

Cognitive architectures employ different means for knowledge representation. In this work, we describe how the Cognitive Systems Toolkit (CST), a toolkit for the construction of cognitive architectures addresses the issue of knowledge representation, by introducing the notion of a computational idea, as being an abstract and generic building block for representing multiple different pieces of knowledge. We particularly address how computational ideas can be used to represent both facts that really happened at an environment and just hypothesis that are not to be considered as being a part of existence, explaining how these are instances of general categories. At the end, we provide different examples to illustrate the subtle differences that are possible to be represented using this knowledge representation scheme.

A thematic analysis to examine the feasibility of EHR-based clinical decision support for implementing Choosing Wisely ® guidelines.

ObjectiveTo identify important barriers and facilitators relating to the feasibility of implementing clinical practice guidelines (CPGs) as clinical decision support (CDS).Materials and MethodsWe conducted a qualitative, thematic analysis of interviews from seven interviews with dyads (one clinical expert and one systems analyst) who discussed the feasibility of implementing 10 Choosing Wisely® guidelines at their institutions. We conducted a content analysis to extract salient themes describing facilitators, challenges, and other feasibility considerations regarding implementing CPGs as CDS.ResultsWe identified five themes: concern about data quality impacts implementation planning; the availability of data in a computable format is a primary factor for implementation feasibility; customized strategies are needed to mitigate uncertainty and ambiguity when translating CPGs to an electronic health record-based tool; misalignment of expected CDS with pre-existing clinical workflows impact implementation; and individual level factors of end-users must be considered when selecting and implementing CDS tools.DiscussionThe themes reveal several considerations for CPG as CDS implementations regarding data quality, knowledge representation, and sociotechnical issues. Guideline authors should be aware that using CDS to implement CPGs is becoming increasingly popular and should consider providing clear guidelines to aid implementation. The complex nature of CPG as CDS implementation necessitates a unified effort to overcome these challenges.ConclusionOur analysis highlights the importance of cooperation and co-development of standards, strategies, and infrastructure to address the difficulties of implementing CPGs as CDS. The complex interactions between the concepts revealed in the interviews necessitates the need that such work should not be conducted in silos. We also implore that implementers disseminate their experiences.

Application of knowledge engineering in spacecraft overall design

This paper proposes an advanced technology in spacecraft overall design based on knowledge engineering. Knowledge engineering is a branch of artificial intelligence. Applying knowledge engineering technology to improve the efficiency and quality of overall design process and enhance the automation of overall design, moreover focusing on solving overall spacecraft design knowledge representation, knowledge evaluation, knowledge acquisition, knowledge inference and related issues. Unlike the existing methods, this paper uses ontology technology to build a pint-sized spacecraft overall design knowledge base under protégé software environment, which achieves a better performance. Finally, simulations for the designed power subsystem based on the knowledge base are performed, and the simulation results validate the effectiveness, reliability, of the power subsystem designed by the method of spacecraft overall design based on knowledge engineering.

If Nothing Is Accepted -- Repairing Argumentation Frameworks

Conflicting information in an agent's knowledge base may lead to a semantical defect, that is, a situation where it is impossible to draw any plausible conclusion. Finding out the reasons for the observed inconsistency (so-called diagnoses) and/or restoring consistency in a certain minimal way (so-called repairs) are frequently occurring issues in knowledge representation and reasoning. In this article we provide a series of first results for these problems in the context of abstract argumentation theory regarding the two most important reasoning modes, namely credulous as well as sceptical acceptance. Our analysis includes the following problems regarding minimal repairs/diagnoses: existence, verification, computation of one and enumeration of all solutions. The latter problem is tackled with a version of the so-called hitting set duality first introduced by Raymond Reiter in 1987. It turns out that grounded semantics plays an outstanding role not only in terms of complexity, but also as a useful tool to reduce the search space for diagnoses regarding other semantics.

What Comes before a Digital Output? Eliciting and Documenting Cultural Heritage Research Processes

— Knowledge-based systems, are today part of many research protocols where they act as powerful means to model, implement and cross-examine the workflows that lead from a set of inputs to a set of outputs. They remain however tricky to apply in the specific context of heritage science where workflows include a long tail of subjective human decisions, of non-explicit research protocols, of poorly formalised pieces of knowledge, of highly individual skills, of undocumented, non-reproducible, intuitive interpretations, when not simply of licentia artistica. Yet the heritage science community has witnessed over the past decades the emergence of huge quantities of digital outputs, either following massive digitization efforts, or as a result of the growing capacity of actors to produce digital-born material. How can this move be supported in terms of reproducibility, reusability and cross-examination of results if research protocols remain non-formalised one-shot efforts? The research presented in this paper bases on the idea that what should be formalised and shared with future generations are not end results alone (outputs) but the methods and processes that lead the making of the output (human skills, tools, technological procedures, cognitive processes, scientific protocols, etc.). Our contribution addresses a pending issue: how can we today complement traditional approaches to heritage assets documentation with means to describe and record research processes and workflows? The infrastructure we propose raises knowledge representation, visualisation, and information management issues. It applies primarily to a range of specific cultural heritage related artefacts, but is expected to be fairly generic in terms of methodology. In this paper we describe the methods employed in order to elicit underlying activities, support team elicitation through ad-hoc visualisations, promote a consistent visual interfacing of the underlying Information System.

On rule acquisition in incomplete multi-scale decision tables

Granular computing and acquisition of IF-THEN rules are two basic issues in knowledge representation and data mining. A rough set approach to knowledge discovery in incomplete multi-scale decision tables from the perspective of granular computing is proposed in this paper. The concept of incomplete multi-scale information tables in the context of rough sets is first introduced. Information granules at different levels of scales in incomplete multi-scale information tables are then described. Lower and upper approximations with reference to different levels of scales in incomplete multi-scale information tables are also defined and their properties are examined. Optimal scale selection with various requirements in incomplete multi-scale decision tables are further discussed. Relationships among different notions of optimal scales in incomplete multi-scale decision tables are presented. Finally, knowledge acquisition in the sense of rule induction in consistent and inconsistent incomplete multi-scale decision tables are explored.

Knowledge Reduction in Crisply Generated Fuzzy Concept Lattices

Knowledge reduction is a basic issue in knowledge representation and data mining. Al- though various methods have been developed to reduce the size of classical formal contexts, the reduction of formal fuzzy contexts based on fuzzy lattices remains a difficult problem owing to its complicated derivation operators. To address this problem, this paper proposes a method of knowl- edge reduction by reducing attributes in a formal fuzzy context based on the crisply generated fuzzy concept lattice. Employing the proposed approach, attributes which are non-essential to the struc- ture of the crisply generated fuzzy concept lattice are removed. Discernibility matrix and Boolean function are employed to compute the attribute reducts of the formal fuzzy contexts, by which all the attribute reducts of the formal fuzzy contexts are deter mined without changing the structure of the lattice. Further, all the attributes are classified into three types by their significance in construct- ing the crisply generated fuzzy concept lattice. The characteristics of these types of attributes are also analyzed. Finally, the proposed method is used to conduct knowledge reduction in the variable threshold concept lattices, which is a complement to the existing knowledge reduction methods.

Processing Diabetes Mellitus Composite Events in MAGPIE.

The focus of this research is in the definition of programmable expert Personal Health Systems (PHS) to monitor patients affected by chronic diseases using agent oriented programming and mobile computing to represent the interactions happening amongst the components of the system. The paper also discusses issues of knowledge representation within the medical domain when dealing with temporal patterns concerning the physiological values of the patient. In the presented agent based PHS the doctors can personalize for each patient monitoring rules that can be defined in a graphical way. Furthermore, to achieve better scalability, the computations for monitoring the patients are distributed among their devices rather than being performed in a centralized server. The system is evaluated using data of 21 diabetic patients to detect temporal patterns according to a set of monitoring rules defined. The system's scalability is evaluated by comparing it with a centralized approach. The evaluation concerning the detection of temporal patterns highlights the system's ability to monitor chronic patients affected by diabetes. Regarding the scalability, the results show the fact that an approach exploiting the use of mobile computing is more scalable than a centralized approach. Therefore, more likely to satisfy the needs of next generation PHSs. PHSs are becoming an adopted technology to deal with the surge of patients affected by chronic illnesses. This paper discusses architectural choices to make an agent based PHS more scalable by using a distributed mobile computing approach. It also discusses how to model the medical knowledge in the PHS in such a way that it is modifiable at run time. The evaluation highlights the necessity of distributing the reasoning to the mobile part of the system and that modifiable rules are able to deal with the change in lifestyle of the patients affected by chronic illnesses.

Knowledge reduction in formal fuzzy contexts

Knowledge reduction is a basic issue in knowledge representation and data mining. Although various methods have been developed to reduce the size of classical formal contexts, the reduction of formal fuzzy contexts based on fuzzy lattices remains a difficult problem owing to its complicated derivation operators. To address this problem, we propose a general method of knowledge reduction by reducing attributes and objects in formal fuzzy contexts based on the variable threshold concept lattices. Employing the proposed approaches, we remove attributes and objects which are non-essential to the structure of a variable threshold concept lattice, i.e., with a given threshold level, the concept lattice constructed from a reduced formal context is made identical to that constructed from the original formal context. Discernibility matrices and Boolean functions are, respectively, employed to compute the attribute reducts and object reducts of the formal fuzzy contexts, by which all the attribute reducts and object reducts of the formal fuzzy contexts are determined without changing the structure of the lattice.

Concept Map and Internet-aided Manufacturing

Manufacturing is a knowledge-intensive activity and the Internet has become a useful tool for storing, sharing, and reusing knowledge. This study describes some issues of Internet-aided manufacturing knowledge representation from the context of concept maps (a visual means for representing knowledge). Manufacturing knowledge is categorized into three categories, namely, general, exploratory, and emergent knowledge. General and exploratory knowledge are existing knowledge, whereas emergent knowledge is new knowledge created for performing manufacturing activities (designing, monitoring, optimizing, etc.). All these three categories of knowledge have cross-referential relationships. Some of the logical processes for creating emergent knowledge are also described. Using a tool called CmapTools (developed by IHMC), Internet-embedded concept maps are constructed. Further study will be carried out for enhancing the machine comprehensibility of the concept maps of manufacturing knowledge.

Introduction to “New Conceptualizations of Transfer of Learning”

Understanding how to get learners to transfer their knowledge to new situations is a topic of both theoretical and practical importance. Theoretically, it touches on core issues in knowledge representation, analogical reasoning, generalization, embodied cognition, and concept formation. Practically, learning without transfer of what has been learned is almost always unproductive and inefficient. Although schools often measure the efficiency of learning in terms of speed and retention of knowledge, a relatively neglected and subtler component of efficiency is the generality and applicability of the acquired knowledge. This special issue of Educational Psychologist collects together new approaches toward understanding and fostering appropriate transfer in learners. Three themes that emerge from the collected articles are (a) the importance of the perspective/stance of the learner for achieving robust transfer, (b) the neglected role of motivation in determining transfer, and (c) the existence of specific, validated techniques for teaching with an eye toward facilitating students’ transfer of their learning.

Theory and applications of granular labelled partitions in multi-scale decision tables

Granular computing and acquisition of if-then rules are two basic issues in knowledge representation and data mining. A formal approach to granular computing with multi-scale data measured at different levels of granulations is proposed in this paper. The concept of labelled blocks determined by a surjective function is first introduced. Lower and upper label-block approximations of sets are then defined. Multi-scale granular labelled partitions and multi-scale decision granular labelled partitions as well as their derived rough set approximations are further formulated to analyze hierarchically structured data. Finally, the concept of multi-scale information tables in the context of rough set is proposed and the unravelling of decision rules at different scales in multi-scale decision tables is discussed.

Attribute reduction based on generalized fuzzy evidence theory in fuzzy decision systems

Attribute reduction is viewed as an important issue in data mining and knowledge representation. This paper studies attribute reduction in fuzzy decision systems based on generalized fuzzy evidence theory. The definitions of several kinds of attribute reducts are introduced. The relationships among these reducts are then investigated. In a fuzzy decision system, it is proved that the concepts of fuzzy positive region reduct, lower approximation reduct and generalized fuzzy belief reduct are all equivalent, the concepts of fuzzy upper approximation reduct and generalized fuzzy plausibility reduct are equivalent, and a generalized fuzzy plausibility consistent set must be a generalized fuzzy belief consistent set. In a consistent fuzzy decision system, an attribute set is a generalized fuzzy belief reduct if and only if it is a generalized fuzzy plausibility reduct. But in an inconsistent fuzzy decision system, a generalized fuzzy belief reduct is not a generalized fuzzy plausibility reduct in general.

Evaluating an evolutionary method of design style imitation

AbstractWe propose a computational method for producing novel constructs that fall within an existing design or artistic style. The method is based on evolutionary algorithms, and we discuss related knowledge representation issues. We then present an implementation of this method that we used in order to imitate the style of the Dutch painter Mondrian. Finally, we explain and give the results of a cognitive experiment designed to determine the effectiveness of the method, and provide a discussion of these results.

Granular Computing and Knowledge Reduction in Formal Contexts

Granular computing and knowledge reduction are two basic issues in knowledge representation and data mining. Granular structure of concept lattices with application in knowledge reduction in formal concept analysis is examined in this paper. Information granules and their properties in a formal context are first discussed. Concepts of a granular consistent set and a granular reduct in the formal context are then introduced. Discernibility matrices and Boolean functions are, respectively, employed to determine granular consistent sets and calculate granular reducts in formal contexts. Methods of knowledge reduction in a consistent formal decision context are also explored. Finally, knowledge hidden in such a context is unraveled in the form of compact implication rules.

Automated compilation of Object-Oriented Probabilistic Relational Models

This paper addresses the issues of knowledge representation and reasoning in large, complex, uncertain domains, focusing on the tactical military domain, which is characterized by all these properties. The key to reasoning efficiently under these circumstances is to provide a knowledge representation language and reasoning techniques which take advantage of the structure of the domain and facilitate reuse. First order representations such as relational logic are useful for representing structured domains because they can represent both entities and relations. However, the constraint that first order logic statements must be either true or false makes these languages unsuited to representing real world domains which involve uncertainty. Probability theory, on the other hand, provides a sound mathematical basis for representing and reasoning with uncertain information. For example, Bayesian Networks (BNs) are a well known probabilistic representation technique. However, there are several characteristics of large, complex domains which are challenging for traditional BNs. Recent research, for example [1,2], has shown there are advantages to be derived from combining probability theory with some of the expressive power of first order logics. Languages which combine probability theory with aspects of first order logic are called First Order Probabilistic Languages (FOPLs). There are a number of such languages, for example [1–4]. FOPLs have been used to model a number of domains such as military situation awareness [2], traffic surveillance [3], information extraction [5], natural language processing [6], intelligent tutoring systems [7], web site user behavior modelling [8] and automated internet fault diagnosis [9]. In this paper, we present the Object-Oriented Probabilistic Relational Modelling Language (OPRML) [10,11], a new FOPL which combines the generality and modularity of relational logic representation with a principled treatment of uncertainty. We describe the language in detail, outlining its formal syntax and semantics and compare it against its most closely related language: Probabilistic Relational Models (PRMs). We also present four novel algorithms for the automatic construction of domain models from knowledge-bases expressed using the OPRML. Two of the algorithms are based on the knowledge-based model construction approach and two are based on an Object-Oriented Bayesian Network instance tree triangulation method. We discuss the strengths and limitations of each of the algorithms and compare their performance against the algorithms developed for PRMs.

Knowledge representation issues in ontology-based clinical Knowledge Management systems

Clinical Knowledge-Management (KM) represents a specific category of KM that requires specific support. Clinical knowledge mixes formal scientific knowledge with a person-culture in which the expertise of clinicians is key. In KM life-cycles, this entails that the required processes associated to clinical knowledge diverge from other kind of activities. Further, the technological support required for clinical knowledge assets is multi-perspective. This paper deals with those differences from the viewpoint of formal ontology. An epistemological account of such knowledge is provided. Then, problems of claim evaluation and representation are approached from it, resulting in a realistic set of design guidelines for clinical KM prepared for ontology-based systems.

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.

Assembling a consistent set of sentences in relational probabilistic logic with stochastic independence

We examine the representation of judgements of stochastic independence in probabilistic logics. We focus on a relational logic where (i) judgements of stochastic independence are encoded by directed acyclic graphs, and (ii) probabilistic assessments are flexible in the sense that they are not required to specify a single probability measure. We discuss issues of knowledge representation and inference that arise from our particular combination of graphs, stochastic independence, logical formulas and probabilistic assessments.