Knowledge Representation and WordNets

Knowledge itself is a representation of “real facts”. Knowledge is a logical model that presents facts from “the real world” witch can be expressed in a formal language. Representation means the construction of a model of some part of reality. Knowledge representation is contingent to both cognitive science and artificial intelligence. In cognitive science it expresses the way people store and process the information. In the AI field the goal is to store knowledge in such way that permits intelligent programs to represent information as nearly as possible to human intelligence. Knowledge Representation is referred to the formal representation of knowledge intended to be processed and stored by computers and to draw conclusions from this knowledge. Examples of applications are expert systems, machine translation systems, computer-aided maintenance systems and information retrieval systems (including database front-ends).knowledge, representation, ai models, databases, cams

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

An integrated information retrieval and document management system

This paper describes the requirements and prototype development for an intelligent document management and information retrieval system that will be capable of handling millions of pages of text or other data. Technologies for scanning, Optical Character Recognition (OCR), magneto-optical storage, and multiplatform retrieval using a Standard Query Language (SQL) will be discussed. The semantic ambiguity inherent in the English language is somewhat compensated-for through the use of coefficients or weighting factors for partial synonyms. Such coefficients are used both for defining structured query trees for routine queries and for establishing long-term interest profiles that can be used on a regular basis to alert individual users to the presence of relevant documents that may have just arrived from an external source, such as a news wire service. Although this attempt at evidential reasoning is limited in comparison with the latest developments in AI Expert Systems technology, it has the advantage of being commercially available

Implementing an Intelligent Retrieval System: The CODER System, Version 1.0

For individuals requiring interactive access to online text, information storage and retrieval systems provide a way to retrieve desired documents and/or text passages. The CODER (COmposite Document Expert/effective/extended Retrieval) system is a testbed for determining how useful various artificial intelligence techniques are for increasing the effectiveness of information storage and retrieval systems. The system, designed previously, has three components: an analysis subsystem for analyzing and storing document contents, a central spine for manipulations and storage of world and domain knowledge, and a retrieval subsystem for matching user queries to relevant documents. This thesis discusses the implementation of the retrieval subsystem and portions of the spine and analysis subsystem. It illustrates that logic programming, specifically with the Prolog language, is suitable for development of an intelligent information retrieval system. Furthermore, it shows that system modularity provides a flexible research testbed, allowing many individuals to work on different parts of the system which may later be quickly integrated. The retrieval subsystem has been implemented in a modular fashion so that new approaches to information can be easily compared to more traditional ones. A powerful knowledge representation language, a comprehensive lexicon, and individually tailored experts using standardized blackboard modules for communication and control allowed rapid prototyping, incremental development and ready adaptability to change. The system executes on a DEC VAX 11/785 running ULTRIX (TM), a variant of 4.2 BSD UNIX. It has been implemented as a set of MU-Prolog and C modules communicating through TCP/IP sockets

Intellectual systems in the management of medical technologies and quality of life

The article is devoted to the development and use of intelligent systems in the management of medical technological processes and health-related quality of life (HRQOL). The relevance of the work is due to the need for effective use of intelligent systems in healthcare. The purpose of this work is to study the possibilities and prospects of using information technologies and artificial intelligence systems in clinical medicine to improve the efficiency of providing medical care to the population. Information retrieval method; theoretical analysis of legislative and regulatory documents, literary sources, Internet resources, research results; spectral-dynamic and mathematical analysis of the current state and assessment of the quality of life of an individual using the artificial intelligence system "CME". The paper analyzes the development trends of information technologies and artificial intelligence systems, as well as the features of their use in medical technological processes. As an example, the technological capabilities of the intelligent system Complex Medical Expert are briefly described

Integrated Access to a Large Medical Literature Database

Project INCARD (INtegrated CARdiology Database) has adapted the CODER (COmposite Document Expert/effective/extended Retrieval) system and LEND (Large External Network object oriented Database) to provide integrated access to a large collection of bibliographic citations, a full text document in cardiology, and a large thesaurus of medical terms. CODER is a distributed expert-based information system that incorporates techniques from artificial intelligence, information retrieval, and human-computer interaction to support effective access to information and knowledge bases. LEND is an object-oriented database which incorporates techniques from information retrieval and database systems to support complex objects, hypertext/hypermedia and semantic network operations efficiently with very large sets of data. LEND stores the CED lexicon, MeSH thesaurus, MEDLARS bibliographics records on cardiology, and the syllabus for the topic Abnormal Human Biology (Cardiology Section) taught at Columbia University. Together, CODER/LEND allow efficient and flexible access to all of this information while supporting rapid "intelligent" searching and hypertext-style browsing by both novice and expert users. This report gives statistics on the collections, illustrations of the system's use, and details on the overall architecture and design for Project INCARD

Intelligent approaches to performance support

Electronic performance support systems provide an important method of meeting on‐demand educational and training requirements. They also provide efficient and effective ways of enabling the knowledge and expertise within an organization to be shared. This paper discusses the design of a distributed electronic performance support system and the ways in which ‘intelligent agents’ based on expert systems and neural networks can be used to locate and share distributed expertise. A case study illustrating our approach to the implementation and use of intelligent agents is presented

An Analysis of Using Expert Systems and Intelligent Agents for the Virtual Library Project at the Naval Surface Warfare Center-Carderock Division

The Virtual Library Project1 at the Naval Surface Warfare Center/Carderock Division (NSWC/CD) is being developed to facilitate the incorporation and use of library documents via the Internet. These documents typically relate to the design and manufacture of ships for the U.S. Navy Fleet. As such, the libraries will store documents that contain not only text but also images, graphs and design configurations. Because of the dynamic nature of digital documents, particularly those related to design, rapid and effective cataloging of these documents becomes challenging. We conducted a research study to analyze the use of expert systems and intelligent agents to support the function of cataloging digital documents. This chapter provides an overview of past research in the use of expert systems and intelligent agents for cataloging digital documents and discusses our recommendations based on NSWC/CD’s requirements

Improving Knowledge Retrieval in Digital Libraries Applying Intelligent Techniques

Nowadays an enormous quantity of heterogeneous and distributed information is stored in the digital University. Exploring online collections to find knowledge relevant to a user’s interests is a challenging work. The artificial intelligence and Semantic Web provide a common framework that allows knowledge to be shared and reused in an efficient way. In this work we propose a comprehensive approach for discovering E-learning objects in large digital collections based on analysis of recorded semantic metadata in those objects and the application of expert system technologies. We have used Case Based-Reasoning methodology to develop a prototype for supporting efficient retrieval knowledge from online repositories. We suggest a conceptual architecture for a semantic search engine. OntoUS is a collaborative effort that proposes a new form of interaction between users and digital libraries, where the latter are adapted to users and their surroundings