(ML)2: A formal language for KADS models of expertise

This paper reports on an investigation into a formal language for specifying KADS models of expertise. After arguing the need for and the use of such formal representations, we discuss each of the layers of a KADS model of expertise in the subsequent sections, and define the formal constructions that we use to represent the KADS entities at every layer: order-sorted logic at the domain layer, meta-logic at the inference layer and dynamic-logic at the task layer. All these constructions together make up (ML)², the language that we use to represent models of expertise. We illustrate the use of (ML)² in a small example model. We conclude by describing our experience to date with constructing such formal models in (ML)², and by discussing some open problems that remain for future work.     

Linked-learning for knowledge acquisition: a pilot's associate case study

We developed a knowledge acquisition system that uses an Explanation-Based Learning domain theory as a knowledge repository from which general knowledge structures can be compiled and then translated by smart translators into the various specialized representations required for the separate expert system modules of a distributed pilot aiding system. We call this two-stage learning-plus-translation process linked learning. This architecture addresses learning for multiple modules with different knowledge representations and performance goals, but which must all perform together in an integrated fashion. It also addresses learning for an intelligent agent which must perform in a real-world, dynamically-changing environment with multiple sources of uncertainty. Finally, it serves as a case study offering insights into the integration of machine learning into the system engineering process for a large knowledge-based system development effort.     

On a common approach to joining productive rules obtained from different knowledge sources

This paper discusses the problem of joining production rules obtained from knowledge sources of different typology (experts, problem-oriented texts, electronic carriers in the form of databases) with a view to building complete and consistent knowledge bases in integrated expert systems. It describes the properties of distributed knowledge acquisition within the target-oriented methodology for building integrated expert systems and automatic technology created on its basis that includes new-generation instruments - an AT-TECHNOLOGY software complex. The problems of using the theory of multisets to join productive rules obtained from different knowledge sources are considered.     

An efficient heuristic algorithm for mapping parallel programs onto multicomputers

Mapping, a crucial problem to be solved for optimal utilization of multicomputers, is the problem of allocating the tasks of a parallel program to the processors of a multicomputer in a way that minimizes the total completion time. Unfortunately, this problem is known to be NP-hard and hence we cannot optimally solve it in a reasonable amount of computation time. In this paper, we present a new heuristic algorithm for solving the mapping problem. Our heuristic move-exchange (HME) algorithm works by iterating a sequence of task moves followed by a task exchange in an efficient way and tries to minimize the completion time by minimizing a cost function. This function is a weighted sum of the cost of load imbalance and the cost of all communications. We then present two competing algorithms, viz. the Simulated Annealing (SA) algorithm and the Recursive Mincut (RM) bipartitioning algorithm, and compare the performance of the HME algorithm with that of SA and RM algorithms. The results of our study clearly demonstrate that our HME algorithm is better than both SA and RM algorithms for solving the mapping problem.     

Domain-driven knowledge modelling for knowledge acquisition

Knowledge modelling is undoubtedly a major problem in knowledge acquisition. Drawing from industrial case studies that have been carried out, the paper lists some key problems which still dog knowledge modelling. Next, it critically reviews current knowledge modelling techniques and tools and concludes that these real knowledge acquisition issues are not tackled by them. We consider the spelling out of these problems and the fact that they are not addressed by current tools and techniques to be a major contribution of this paper. The paper strongly argues for knowledge modelling to be domain-driven, i.e. driven by the nature of the domain being modelled. The key argument in this paper is that ignoring the nature or characterization of the domain inevitably results in knowledge imposition rather than knowledge acquisition as domains get shoe-horned into some (current) set of models, representations and tools. After examining the nature of domains, the paper proceeds to outline an emerging hypothesis for knowledge modelling. It concludes with a specification of a tool suite for addressing the issues identified in this paper.     

Digitalization as a policy response to social acceleration: Comparing democratic problem solving in Denmark and the Netherlands

Social acceleration – the progressively faster rate of technological, social and life-pace change – poses a dilemma for democratic problem solving: It increases the amount of new social problems emerging on the political agenda and hence amplifies the demand for rapid and effective policy solutions. Democratic politics is, however, slow. So either the political system speeds up decision making at the cost of democracy, or it holds on to democracy at the cost of problem solving. Obviously, neither option is desirable. How do governments try to solve this dilemma and provide positive-sum solutions that are both effective and democratic? We present two so-called paradigm cases of governments, in Denmark and the Netherlands, that have developed a remarkably quick and effective digitalization response to social acceleration. By focusing on four markers – (1) awareness and timing; (2) motivation; (3) new strategic goals; and (4) goal-directed strategic policy action – we theorize how governments aim to solve the dilemma for democratic problem solving under conditions of social acceleration.     

Optimal scheduling of independent jobs in multiprocessor systems

In this paper, we consider the problem of finding an optimal schedule of several independent jobs in multiprocessor systems, i.e. for a given job list, in what order the jobs should be processed and how to assign these jobs to the processors in a multiprocessor system such that the completion time of the last completed job is minimized. We first discuss two models of this problem, one in which time and processor requirements of a job are fixed and the other in which time requirement of a jobs varies according to the number of processors allocated to the job for execution. We then present efficient algorithms for solving these problem models, which are known to be NP-hard in the strong sense, using a branch and bound strategy. Our algorithms use several new reduction rules to arrive at optimal solutions in an efficient way. Finally, we demonstrate the effectiveness of our algorithms by experimental results.     

The architecture of response-pipelined content addressable memories

Content Addressable Memories (CAMs) are useful as accelerators for search-intensive programs such as rule-based systems, database applications, documents retrieval and pattern matching. Many applications in these areas required searching long words that are too wide to fit into most commercially available CAM chips. To date, only one commercial chip is available with on-chip support for searching multi-word keys associatively. This chip, however, needs K cycles to search logical keys that are K words long. In this paper, we introduce the architecture of a CAM chip that allows cascading to increase the word-size and the number of words, and yet maintain the search rate constant through the use of a technique called response pipelining. Response pipelining allows the logical word size to be increased by a factor of K but maintains a search and operation completion rate of one per cycle, i.e. the search rate remains independent of K. Response pipelining provides this ability by incorporating very negligible hardware in each chip; response pipelining also allows the board level wiring to be kept very simple. Prototype CAM chips (Response Pipelined CAMs-RPCAMS) have been implemented in a 2-micron CMOS process with 2 metal layers. We describe the response pipelining technique and its potential advantages, as well as the architectural details of the RPCAM chips. Results of using the RPCAM as accelerators for search-intensive applications that require relatively long keys show that significant performance gains are possible through their use. The RPCAM chips are thus ideal as building blocks for large associative arrays.     

Document retrieval on repetitive string collections

Most of the fastest-growing string collections today are repetitive, that is, most of the constituent documents are similar to many others. As these collections keep growing, a key approach to handling them is to exploit their repetitiveness, which can reduce their space usage by orders of magnitude. We study the problem of indexing repetitive string collections in order to perform efficient document retrieval operations on them. Document retrieval problems are routinely solved by search engines on large natural language collections, but the techniques are less developed on generic string collections. The case of repetitive string collections is even less understood, and there are very few existing solutions. We develop two novel ideas, interleaved LCPs and precomputed document lists, that yield highly compressed indexes solving the problem of document listing (find all the documents where a string appears), top-k document retrieval (find the k documents where a string appears most often), and document counting (count the number of documents where a string appears). We also show that a classical data structure supporting the latter query becomes highly compressible on repetitive data. Finally, we show how the tools we developed can be combined to solve ranked conjunctive and disjunctive multi-term queries under the simple \({\textsf{tf}}{\textsf{-}}{\textsf{idf}}\) model of relevance. We thoroughly evaluate the resulting techniques in various real-life repetitiveness scenarios, and recommend the best choices for each case.     

Developing K-ONCOCIN: a case study in the cognitive process of knowledge engineers

The domain of knowledge engineering reflects a strong emphasis on the acquisition and modelling of knowledge structures and the processes used to manipulate and transform them. Knowledge engineering activity has been largely focused on the epistemological and representational aspects of knowledge. A different, yet complementary view would identify knowledge engineering as the interaction of cogniting agents with the emphasis on the cognitive processes used in knowledge engineering. This paper presents the results of an intensive discussion between the author and Marc Linster who acted as the interviewee. The topic of discussion focused on his activity with Mark Musen during the modelling stages of knowledge engineering activity in the development of K-ONCOCIN which is a KADS conceptual model of a part of the ONCOCIN system. For each state of this ‘bottom-up’ modelling, the tasks were identified, the available knowledge and information resources identified, the classes of cognitive processes inferred and the outcome structures described.     

面向劣构问题求解的知识管理系统及其构建

首先介绍组织中大量存在的劣构问题的求解现状和存在的问题;其次,在分析面向特定领域劣构问题求解的知识管理系统研究和应用情况的基础上,研究劣构问题的特点及其求解过程,建立一般性劣构问题求解的过程模型,讨论劣构问题求解过程中的知识结构和信息结构,并提出其知识管理系统的设计和实现方式;最后给出系统应用实例。     

Extending WHIRL with background knowledge for improved text classification

Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.

A bird's-eye view of scientific trading: Dependency relations among fields of science

We use a trading metaphor to study knowledge transfer in the sciences as well as the social sciences. The metaphor comprises four dimensions: (a) Discipline Self-dependence, (b) Knowledge Exports/Imports, (c) Scientific Trading Dynamics, and (d) Scientific Trading Impact. This framework is applied to a dataset of 221 Web of Science subject categories. We find that: (i) the Scientific Trading Impact and Dynamics of materials science and transportation science have increased; (ii) biomedical disciplines, physics, and mathematics are significant knowledge exporters, as is statistics and probability; (iii) in the social sciences, economics, business, psychology, management, and sociology are important knowledge exporters; and (iv) Discipline Self-dependence is associated with specialized domains which have ties to professional practice (e.g., law, ophthalmology, dentistry, oral surgery and medicine, psychology, psychoanalysis, veterinary sciences, and nursing).     

An efficient DM switchbox router

Routing is an important problem in the computer-aided VLSI layout design process. We consider the switchbox routing problem in a new routing model, diagonal model (DM)[11], whose grid consists of right and left diagonal tracks displayed at +45° and -45° on two layers. We present an efficient DM switchbox router. It attempts to reach the optimality to maximum extent locally and globally. Experimental results demonstrate that it completes successfully the routing for many known benchmark examples.     

Knowledge diffusion path analysis of data quality literature: A main path analysis

This study presents a unique approach in investigating the knowledge diffusion structure for the field of data quality through an analysis of the main paths. We study a dataset of 1880 papers to explore the knowledge diffusion path, using citation data to build the citation network. The main paths are then investigated and visualized via social network analysis. This paper takes three different main path analyses, namely local, global, and key-route, to depict the knowledge diffusion path and additionally implements the g-index and h-index to evaluate the most important journals and researchers in the data quality domain.     

Material-Mind-Method: On the Teaching of Reference beyond the Lecture: In-Class Hands-On Contact with Reference Sources

ABSTRACT

Reference librarians need to know how to use a vast array of reference sources. How do they acquire this knowledge? How can students arrive at their entry level positions with an arsenal of known sources and strategies for advancing this knowledge? Various strategies can be employed to provide students with options on how to launch their understanding for how and when to use specific sources. This column provides examples of how to incorporate active learning activities devoted to providing students in a group setting with options on how to begin and share their learning about reference sources.     

Exploring criteria for successful query expansion in the genomic domain

Query Expansion is commonly used in Information Retrieval to overcome vocabulary mismatch issues, such as synonymy between the original query terms and a relevant document. In general, query expansion experiments exhibit mixed results. Overall TREC Genomics Track results are also mixed; however, results from the top performing systems provide strong evidence supporting the need for expansion. In this paper, we examine the conditions necessary for optimal query expansion performance with respect to two system design issues: IR framework and knowledge source used for expansion. We present a query expansion framework that improves Okapi baseline passage MAP performance by 185%. Using this framework, we compare and contrast the effectiveness of a variety of biomedical knowledge sources used by TREC 2006 Genomics Track participants for expansion. Based on the outcome of these experiments, we discuss the success factors required for effective query expansion with respect to various sources of term expansion, such as corpus-based cooccurrence statistics, pseudo-relevance feedback methods, and domain-specific and domain-independent ontologies and databases. Our results show that choice of document ranking algorithm is the most important factor affecting retrieval performance on this dataset. In addition, when an appropriate ranking algorithm is used, we find that query expansion with domain-specific knowledge sources provides an equally substantive gain in performance over a baseline system.

An inquiry into the availability of Canadian fiction in U.S. libraries with special attention to the influence of reviews

In order to investigate the availability of Canadian fiction in U.S. libraries and the influence of reviews in U.S. sources on this availability, 182 “Novels” that had been reviewed in Canadian Book Review Annual were searched in the OCLC data base and in Book Review Index. Findings suggest that Canadian fiction is widely owned by U.S. libraries only when a work has been reviewed in U.S. sources—especially Booklist, Kirkus, Library Journal, and Publishers Weekly. Furthermore, results suggest that Canadian fiction is normally reviewed in U.S. sources only when the work is available in a U.S. edition. Anomalies are noted and suggestions are made for further research.