95,106 research outputs found
Systemy ekspertowe w działalności bibliotecznej i informacyjnej : stan badań, problemy badawcze, przykłady zastosowań
Purpose/thesis: The purpose of this paper is to answer the questions of whether and to what extent
expert systems are currently used in library and information services. Attention is drawn to the following
elements of the information process: acquisition, storage and cataloging/description of information
(search characteristics, search instruction), information retrieval (querying, natural language
queries), transmission and implementation of information.
Methods: The analysis of the literature acquired through queries in selected databases provided
basis for the discussion of research trends in the field of expert systems as one of the methodologies
of knowledge management using methods of artificial intelligence: problem solving and representation
of knowledge (knowledge generation, valuation and management, knowledge extraction and
synthesis in the construction of expertise), „self-learning”, performance evaluation. In the final part
of the paper an attempt was made to identify possible areas of expert systems application in libraries
and information centers.
Results:The research helped to specify the number of bibliographic records containing information
about expert systems and recorded in selected databases. Expert systems vocabulary presented
in the example thesaurus was discussed and the literature of the field was studied.
Conclusions: The quantitative analysis of the literature showed a significant decrease in the number
of publications on expert systems after 2010. Nevertheless, the expert systems (both experimental
and operational) are used in numerous fields. It is expected that these systems will be used in specialized
libraries for the acquisition and codification of knowledge in selected areas of specialization
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Retrieving information from heterogeneous freight data sources to answer natural language queries
textThe ability to retrieve accurate information from databases without an extensive knowledge of the contents and organization of each database is extremely beneficial to the dissemination and utilization of freight data. The challenges, however, are: 1) correctly identifying only the relevant information and keywords from questions when dealing with multiple sentence structures, and 2) automatically retrieving, preprocessing, and understanding multiple data sources to determine the best answer to user’s query. Current named entity recognition systems have the ability to identify entities but require an annotated corpus for training which in the field of transportation planning does not currently exist. A hybrid approach which combines multiple models to classify specific named entities was therefore proposed as an alternative. The retrieval and classification of freight related keywords facilitated the process of finding which databases are capable of answering a question. Values in data dictionaries can be queried by mapping keywords to data element fields in various freight databases using ontologies. A number of challenges still arise as a result of different entities sharing the same names, the same entity having multiple names, and differences in classification systems. Dealing with ambiguities is required to accurately determine which database provides the best answer from the list of applicable sources. This dissertation 1) develops an approach to identify and classifying keywords from freight related natural language queries, 2) develops a standardized knowledge representation of freight data sources using an ontology that both computer systems and domain experts can utilize to identify relevant freight data sources, and 3) provides recommendations for addressing ambiguities in freight related named entities. Finally, the use of knowledge base expert systems to intelligently sift through data sources to determine which ones provide the best answer to a user’s question is proposed.Civil, Architectural, and Environmental Engineerin
The SP theory of intelligence: benefits and applications
This article describes existing and expected benefits of the "SP theory of
intelligence", and some potential applications. The theory aims to simplify and
integrate ideas across artificial intelligence, mainstream computing, and human
perception and cognition, with information compression as a unifying theme. It
combines conceptual simplicity with descriptive and explanatory power across
several areas of computing and cognition. In the "SP machine" -- an expression
of the SP theory which is currently realized in the form of a computer model --
there is potential for an overall simplification of computing systems,
including software. The SP theory promises deeper insights and better solutions
in several areas of application including, most notably, unsupervised learning,
natural language processing, autonomous robots, computer vision, intelligent
databases, software engineering, information compression, medical diagnosis and
big data. There is also potential in areas such as the semantic web,
bioinformatics, structuring of documents, the detection of computer viruses,
data fusion, new kinds of computer, and the development of scientific theories.
The theory promises seamless integration of structures and functions within and
between different areas of application. The potential value, worldwide, of
these benefits and applications is at least $190 billion each year. Further
development would be facilitated by the creation of a high-parallel,
open-source version of the SP machine, available to researchers everywhere.Comment: arXiv admin note: substantial text overlap with arXiv:1212.022
An overview of decision table literature 1982-1995.
This report gives an overview of the literature on decision tables over the past 15 years. As much as possible, for each reference, an author supplied abstract, a number of keywords and a classification are provided. In some cases own comments are added. The purpose of these comments is to show where, how and why decision tables are used. The literature is classified according to application area, theoretical versus practical character, year of publication, country or origin (not necessarily country of publication) and the language of the document. After a description of the scope of the interview, classification results and the classification by topic are presented. The main body of the paper is the ordered list of publications with abstract, classification and comments.
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