Incremental Tabling in Support of Knowledge Representation and Reasoning

Knowledge representation systems based on the well-founded semantics can offer the degree of scalability required for semantic web applications and make use of ex- pressive semantic features such as Hilog, frame-based rea- soning, and defeasibility theories. Such features can be com- piled into Prolog tabling engines that have good support for indexing and memory management. However, due both to the power of the semantic features and to the declarative style typical of knowledge representation rules, the resources needed for query evaluation can be unpredictable. In such a situation, users need to understand the overall structure of a computation and examine problematic portions of it. This problem, of profiling a computation, differs from debug- ging and justification which address why a given answer was or wasn't derived, and so profiling requires different tech- niques. In this paper we present a trace-based analysis tech- nique called forest logging which has been used to profile large, heavily tabled computations. In forest logging, criti- cal aspects of a tabled computation are logged; afterwards the log is loaded and analyzed. As implemented in XSB, for- est logging slows down execution of practical programs by a constant factor that is often small; and logs containing tens or hundreds of millions of facts can be loaded and analyzed in minutes.

Although current efforts have indicated the need for design verification and evaluation systems, little has been done to develop knowledge representation systems for implementing this class of expert systems. In this paper, we describe CANDLE, a knowledge representation system built to solve design verification and evaluation problems. We define the class of design verification and evaluation systems and the functionality required of knowledge representation systems used to develop these systems. We present an example design verification and evaluation problem, illustrating how this problem imposes these functionality requirements on the knowledge representation system used. In the remainder of the paper we discuss CANDLE, a knowledge representation system created to develop design verification and evaluation systems. We show that CANDLE meets the knowledge representation requirements for this class of systems. We also present some of CANDLE'S functionality that exceeds the defined knowledge representation requirements and provides a knowledge engineer with the capability to expand the functionality of a design verification and evaluation system.

In this article, ethical decision‐making methods for creating, revising, and maintaining knowledge representation and organization systems are described, particularly in relation to the global use of these systems. The analysis uses a three‐level model and the literature on ethically based decision‐making in the social and technical sciences. In addition, methods for making these kinds of decisions in an ethical manner are presented. This multidisciplinary approach is generalizable to other information areas and is useful for encouraging the development of ethics policies for knowledge representation and organization systems and for other kinds of systems or institutions.

Retrieving and composing individuals' capabilities are the matter of several research fields, like competence-based management, human resources management, enterprise knowledge management systems, knowledge representation systems, etc. This talk focuses on capability representation, discovery and composition in heterogeneous, and possibly distributed, knowledge representation environments. A driving motivation of this work is to provide a contribution to the satisfaction of the need for retrieving individuals who may carry out actions, as well as the need for retrieving individuals who, when putting their competences together, may carry out actions. We outline some formal capability description languages, and we propose approaches and algorithms for capability management, discovery and composition. In addition, we outline how our proposals are implemented in mediator-based prototype systems under a service oriented architecture and a Peer-to-Peer architecture.

Dual coding of knowledge in the human brain.

The core of fault diagnosis expert system is diagnostic knowledge representation and reasoning. Fuzzy Petri nets (FPN) have been testified to an ideal tool for knowledge representation and reasoning. However, when using FPN for diagnostic knowledge representation and reasoning, fault symptoms will disappear after fault propagated. This is not congruous with factual fault propagation. Another major limitation is the lacking of formal reasoning algorithm of FPN, which leads to it can't be implemented expediently by computer. In view of the limitations of FPN based diagnostic knowledge representation and reasoning, an enhanced model named fuzzy fault Petri nets (FFPN) and its formal reasoning algorithm are presented in the paper. A fault diagnosis instance was used to verify the feasibility and validity of the proposed method. It is demonstrated that this method is quite effective.

Twenty years of AI research in knowledge representation has produced frame knowledge representation systems (FRSs) that incorporate a number of important advances. However, FRSs lack two important capabilities that prevent them from scaling up to realistic applications: they cannot provide high-speed access to large knowledge bases (KBs), and they do not support shared, concurrent KB access by multiple users. Our research investigates the hypothesis that one can employ an existing database management system (DBMS) as a storage subsystem for an FRS, to provide high-speed access to large, shared KBs. We describe the design and implementation of a general storage system that incrementally loads referenced frames from a DBMS, and saves modified frames back to the DBMS, for two different FRSs: LOOM and THEO. We also present experimental results showing that the performance of our prototype storage subsystem exceeds that of flat files for simulated applications that reference or update up to one third of the frames from a large LOOM KB.

There is a significant need for a computer-aided modeling, effective information analysis and ontology knowledge base models to support both special needs children and care providers. As this research work correlated to the symmetry scope, it proposes an innovative generic smart knowledge-based “School Care Coordination System” (SCCS), which is established on a novel holistic six-layered data management model. The development of the Smart-SCCS adopts a methodology of ontology engineering to transform the given theoretical unstructured special educational needs and disabilities (SEND) code of practice into a comprehensive knowledge representation and reasoning system. The intended purpose is to deliver a system that can coordinate and bring together education, health and social care services into a single application to meet the needs of children and young people (CYP) with SEND. Moreover, it enables coordination, integration and monitoring of education, health and social care activities between different actors (formal, informal and CYP in the education sector) involved in the school care process network to provide personalized care interventions based on a predefined care plan. The developed ontology knowledge-based model has been proven efficient and solved the enormous difficulties faced by schools and local authorities on a daily basis. It enabled the coordination of care and integration of information for CYP from different departments in health, social care and education. The developed model has received significant attention with great feedback from all the schools and the local authorities involved, showing its efficiency and robustness.

The concept of stratified graph introduces some method of knowledge representation (see [T¸ and ˘ areanu, N., ˘ Knowledge representation by labeled stratified graphs, Proc. 8th World Multi-Conference on Systemics, Cybernetics and Informatics, 5 (2004), 345–350; T¸ and ˘ areanu, N., ˘ Proving the Existence of Labelled Stratified Graphs, An. Univ. Craiova Ser. Mat. Inform., 27 (2000), 81–92]) The inference process developed for this method uses the paths of the stratified graphs, an order between the elementary arcs of a path and some results of universal algebras. The order is defined by considering a structured path instead of a regular path. In this paper we define the concept of system of knowledge representation as a tuple of the following components: a stratified graph G, a partial algebra Y of real objects, an embedding mapping (an injective mapping that embeds the nodes of G into objects of Y ) and a set of algorithms such that each of them can combine two objects of Y to get some other object of Y . We define also the concept of inference process performed by a system of knowledge processing in which the interpretation of the symbolic elements is defined by means of natural language constructions. In this manner we obtained a mechanism for texts generation in a natural language (for this approach, Romanian).

In order to represent the design knowledge of complex product, logical descriptions of different knowledge are conducted by character analysis for the generalized design knowledge. Then a knowledge representation system is proposed in this chapter utilizing the three-layer structure of “design object ontology—design process ontology—data resource” (OPR). And the semantic description frame is defined based on the suggested upper merged ontology (SUMO) to implement the ontology collaborative construction using the ontology web language (OWL). Based on the constructed ontology, the storage structure for generalized knowledge is determined to actualize the generalized knowledge representation. Finally, the feasibility and effectiveness of the proposed technique in this chapter is demonstrated by the design knowledge representation for the self-propelled gun.

Based on a detained discussion of system architectures for knowledge based speech understanding and knowledge representation techniques, criteria for both a knowledge representation scheme and system architectures are developed. Upon this background a system is introduced which is organized around a homogenuous knowledge base. Both the knowledge representation language and the content of the knowledge base are described. The knowledge representation language do not only cover declarative but also procedural knowledge. Analysis processes are guided by a flexibel bottom-up top-down strategy. Besides the procedural semantics of the language the A -Algorithm is used for this purpose. Search graph nodes are judged by a vector which reflects knowledge dependent and acoustic scores and which is admissable for the A -Algorithm.

The paper presents an approach to the development of a software environment for creation of intelligent systems. This environment is based on an integrated knowledge representation model that combines both classic knowledge representation means and constraint programming methods with agent-based techniques. In contrast to other systems following constraint programming methods, the environment presented here allows one to manipulate objects with imprecise (subdefinite) values of attributes. The process of refining such subdefinite attributes is performed by means of a special data-driven mechanism. Another feature of the environment is the use of an agent-based technique instead of the traditional production rule technique, in order to increase efficiency of the processes of logical inference and data processing. Due to a natural combination of the data-driven and event-driven mechanisms, the environment can be used for the development of efficient intelli-gent systems for various applications.

The adjective cognitive especially in conjunction with the word computing seems to be a trendy buzzword in the artificial intelligence community and beyond nowadays. However, the term is often used without explicit definition. Therefore we start with a brief review of the notion and define what we mean by cognitive reasoning. It shall refer to modeling the human ability to draw meaningful conclusions despite incomplete and inconsistent knowledge involving among others the representation of knowledge where all processes from the acquisition and update of knowledge to the derivation of conclusions must be implementable and executable on appropriate hardware. We briefly introduce relevant approaches and methods from cognitive modeling, commonsense reasoning, and subsymbolic approaches. Furthermore, challenges and important research questions are stated, e.g., developing a computational model that can compete with a (human) reasoner on problems that require common sense.

To promote the research and development of modern digital and intelligent manufacturing technology and overcome shortcomings of computerized numerical control(CNC) programming for complex components, an innovative idea has been proposed to introduce knowledge based engineering (KBE) into the field of CNC programming within the integrated environment of CAD/CAPP/ CAM. This paper constructs the architecture of CNC programming based on KBE within CAD/CAPP/CAM and explores the key technology of applying KBE to CNC programming — knowledge representation, knowledge acquisition, knowledge reasoning, and generalized knowledge base system of CNC programming. The integration of the CAD/CAM system and the CAPP system of enterprises has been achieved by taking the powerful CAD/CAM system of UG NX as a platform. The prototype system of CNC programming for complex components based on KBE within CAD/CAPP/CAM has been developed by means of UG/Open,VC ++ 6.0, and SQL Sever 2000. Finally, a frame example, one of the complex components of a marine diesel, is presented, and the academic production and the intelligence of the system are verified.

Knowledge graph technology is a critical part of artificial intelligence research. It establishes a knowledge base with the capacity of semantic processing and open interconnection in order to provide intelligent information service, such as search, question-answering, personalized recommendation, and so on. This article first presents a comprehensive study on definitions and architectures of knowledge graphs. Then we summarizes recent advances in knowledge graphs, including knowledge extraction, knowledge representation, knowledge fusion, and knowledge reasoning, with typical applications. Finally, this article concludes with future challenges of knowledge graphs.