Automatic probabilistic knowledge acquisition from data

A computer program for extracting significant correlations of attributes from masses of data is outlined. This information can then be used to develop a knowledge base for a probabilistic expert system. The method determines the best estimate of joint probabilities of attributes from data put into contingency table form. A major output from the program is a general formula for calculating any probability relation associated with the data. These probability relations can be utilized to form IF-THEN rules with associated probability, useful for expert systems

Psychological tools for knowledge acquisition

Knowledge acquisition is said to be the biggest bottleneck in the development of expert systems. The problem is getting the knowledge out of the expert's head and into a computer. In cognitive psychology, characterizing metal structures and why experts are good at what they do is an important research area. Is there some way that the tools that psychologists have developed to uncover mental structure can be used to benefit knowledge engineers? We think that the way to find out is to browse through the psychologist's toolbox to see what there is in it that might be of use to knowledge engineers. Expert system developers have relied on two standard methods for extracting knowledge from the expert: (1) the knowledge engineer engages in an intense bout of interviews with the expert or experts, or (2) the knowledge engineer becomes an expert himself, relying on introspection to uncover the basis of his own expertise. Unfortunately, these techniques have the difficulty that often the expert himself isn't consciously aware of the basis of his expertise. If the expert himself isn't conscious of how he solves problems, introspection is useless. Cognitive psychology has faced similar problems for many years and has developed exploratory methods that can be used to discover cognitive structure from simple data

SKILLS AND TECHNIQUES FOR KNOWLEDGE ACQUISITION: A SURVEY, ASSESSMENT, AND FUTURE DIRECTIONS

In recent years there has been a tremendous increase in the development of expert systems in organizations. This increased development is straining the already limited supply of qualified expert system developers. These expert system developers have come to be known as knowledge engineers, and their job as knowledge engineering. The process of knowledge engineering is divided into two tasks: knowledge acquisition and expert system construction. Knowledge acquisition has been defined as The process of extracting, structuring, and organizing knowledge from several sources, usually human experts, so it can be used in a program (Waterman 1986, p. 392). This process of knowledge acquisition has been identified as the bottleneck that currently constrains the development of expert systems. This paper summarizes what is known about the skills required and the techniques utilized in the knowledge acquisition process. Due to the similarities that exist between expert systems and traditional systems development, the literature pertaining to traditional information requirements determination and to systems analysts will be utilized to guide this exploration. Case study reports of actual expert system development projects and the practitioner literature will also be referenced. Given the lack of research in this area, future research directions are suggested to aid in developing a better understanding of the knowledge acquisition process. Pursuing these research questions should lead to the identification of· the skills and techniques necessary to successfully perform knowledge acquisition. Once these skills have been identified, selection and training programs can be developed to help reduce the shortage of qualified knowledge engineers and, ultimately, facilitate the increased development of expert systems in organizations

An Expert System for Predicting Radiated EMI from PCB\u27s

This paper describes an expert systems approach, based on symbolic reasoning techniques, to the problem of predicting radiated EMI levels from printed circuit boards. The expert system, currently under development at the University of Missouri-Rolla, USA, starts by extracting board geometry information from the board layout files. This information is fed into the classification algorithm, which determines the signal properties and nature of each net, using the knowledge stored in the knowledge base. The evaluation algorithm uses the available in formation to identify and evaluate critical circuit geometries, and then estimates the effect that these geometries have on system radiation levels. The expert system also looks for violations of basic EMC design rules. The main advantage of such a system over conventional software is that the expert system does not require the user to be an expert in EMC or circuit design

The case of Amazon.com, Inc.

Castelli, M., Manzoni, L., Vanneschi, L., & Popovič, A. (2017). An expert system for extracting knowledge from customers’ reviews: The case of Amazon.com, Inc. Expert Systems with Applications, 84(October), 117-126. https://doi.org/10.1016/j.eswa.2017.05.008E-commerce has proliferated in the daily activities of end-consumers and firms alike. For firms, consumer satisfaction is an important indicator of e-commerce success. Today, consumers’ reviews and feedback are increasingly shaping consumer intentions regarding new purchases and repeated purchases, while helping to attract new customers. In our work, we use an expert system to predict the sentiment of a product considering a subset of available customers’ reviews.authorsversionpublishe

KNOWLEDGE ACQUISITION METHODOLOGIES: SURVEY AND EMPIRICAL ASSESSMENT

Knowledge acquisition, the process of extracting information from human experts, is one of the challenges in building expert systems. Modern practitioners and researchers need more guidance than is provided by existing knowledge acquisition guidelines. However, there has been little empirical research upon which to base the needed guidelines. This paper surveys the available knowledge acquisition techniques and describes a knowledge acquisition experiment which contrasts three of these methods. A framework was developed to categorize the types of heuristic which can be elicited with different means of knowledge acquisition. This research represents the initial steps in a research program focused on the development of empirically evaluated, generalized guidelines for effecting .knowledge acquisition

Knowledge Author: Facilitating user-driven, Domain content development to support clinical information extraction

Background: Clinical Natural Language Processing (NLP) systems require a semantic schema comprised of domain-specific concepts, their lexical variants, and associated modifiers to accurately extract information from clinical texts. An NLP system leverages this schema to structure concepts and extract meaning from the free texts. In the clinical domain, creating a semantic schema typically requires input from both a domain expert, such as a clinician, and an NLP expert who will represent clinical concepts created from the clinician's domain expertise into a computable format usable by an NLP system. The goal of this work is to develop a web-based tool, Knowledge Author, that bridges the gap between the clinical domain expert and the NLP system development by facilitating the development of domain content represented in a semantic schema for extracting information from clinical free-text. Results: Knowledge Author is a web-based, recommendation system that supports users in developing domain content necessary for clinical NLP applications. Knowledge Author's schematic model leverages a set of semantic types derived from the Secondary Use Clinical Element Models and the Common Type System to allow the user to quickly create and modify domain-related concepts. Features such as collaborative development and providing domain content suggestions through the mapping of concepts to the Unified Medical Language System Metathesaurus database further supports the domain content creation process. Two proof of concept studies were performed to evaluate the system's performance. The first study evaluated Knowledge Author's flexibility to create a broad range of concepts. A dataset of 115 concepts was created of which 87 (76%) were able to be created using Knowledge Author. The second study evaluated the effectiveness of Knowledge Author's output in an NLP system by extracting concepts and associated modifiers representing a clinical element, carotid stenosis, from 34 clinical free-text radiology reports using Knowledge Author and an NLP system, pyConText. Knowledge Author's domain content produced high recall for concepts (targeted findings: 86%) and varied recall for modifiers (certainty: 91% sidedness: 80%, neurovascular anatomy: 46%). Conclusion: Knowledge Author can support clinical domain content development for information extraction by supporting semantic schema creation by domain experts

Fourth special issue on knowledge discovery and business intelligence

[Excerpt] Expert Systems (ES) are a core element of human decision making. Initially, in the 70s and 80s, ES were focused on extracting explicit knowledge from human experts. With the availability of big data, after the 2000s, ES incorporated data-driven models, thus being associated with business intelligence, big data, data science and machine learning systems [Cortez and Santos, 2017]. The importance of data-driven models in the ES area is confirmed by the recent Wiley’s Expert Systems (EXSY) literature survey that analyzed all journal research articles published from 2000 to 2016 [Cortez et al., 2018]. The survey revealed data-driven as the most prevalent ES method type, corresponding to around 35% of all recently published EXSY papers. [...]We would like to thank the other KDBI 2017 track (of EPIA) co-organizers: Albert Bifet, Luis Cavique, and Nuno Marques. Also, we thank the authors, who contributed with their papers, and the reviewers (from the KDBI 2017 program committee and the EXSY journal). This work has been supported by COMPETE: POCI-01-0145-FEDER-007043 and FCT - Fundacao para a Ciencia e Tecnologia within the Project Scope: UID/CEC/00319/2013

Expert Finding by Capturing Organisational Knowledge from Legacy Documents

Organisations capitalise on their best knowledge through the improvement of shared expertise which leads to a higher level of productivity and competency. The recognition of the need to foster the sharing of expertise has led to the development of expert finder systems that hold pointers to experts who posses specific knowledge in organisations. This paper discusses an approach to locating an expert through the application of information retrieval and analysis processes to an organization’s existing information resources, with specific reference to the engineering design domain. The approach taken was realised through an expert finder system framework. It enables the relationships of heterogeneous information sources with experts to be factored in modelling individuals’ expertise. These valuable relationships are typically ignored by existing expert finder systems, which only focus on how documents relate to their content. The developed framework also provides an architecture that can be easily adapted to different organisational environments. In addition, it also allows users to access the expertise recognition logic, giving them greater trust in the systems implemented using this framework. The framework were applied to real world application and evaluated within a major engineering company