A knowledge representation language for large knowledge bases and “intelligent” information retrieval systems

1990 ◽  
Vol 26 (3) ◽  
pp. 349-370 ◽  
Author(s):  
Gian Piero Zarri
Keyword(s):  
Information Retrieval ◽  
Knowledge Representation ◽  
Knowledge Bases ◽  
Retrieval Systems ◽  
Intelligent Information Retrieval ◽  
Information Retrieval Systems ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Intelligent Information

scholarly journals RESEARCH OF APPROACHES TO THE RECOGNITION OF SEMANTIC IMAGES OF SCIENTIFIC PUBLICATIONS BASED ON NEURAL NETWORKS

2021 ◽  
Vol 2 ◽  
pp. 38-43
Author(s):  
Iuliia Bruttan ◽  
Igor Antonov ◽  
Dmitry Andreev ◽  
Victor Nikolaev ◽  
Tatyana Klets
Keyword(s):  
Neural Network ◽  
Neural Networks ◽  
Information Retrieval ◽  
Scientific Publication ◽  
Scientific Publications ◽  
Network Training ◽  
Retrieval Systems ◽  
Intelligent Information Retrieval ◽  
Information Retrieval Systems ◽  
Intelligent Information

The paper is devoted to the problems of orientation and navigation in the world of verbal presentation of scientific knowledge. The solution of these problems is currently hampered by the lack of intelligent information retrieval systems that allow comparing descriptions of various scientific works at the level of coincidence of semantic situations, rather than keywords. The article discusses methods for the formation and recognition of semantic images of scientific publications belonging to specific subject areas. The method for constructing a semantic image of a scientific text developed by Iuliia Bruttan allows to form an image of the text of a scientific publication, which can be used as input data for a neural network. Training of this neural network will automate the processes of pattern recognition and classification of scientific publications according to specified criteria. The approaches to the recognition of semantic images of scientific publications based on neural networks considered in the paper can be used to organize the semantic search for scientific publications, as well as in the design of intelligent information retrieval systems.

An Implemented Representation and Reasoning Systems for Creating and Exploiting Large Knowledge Bases of Narrative Information

2011 ◽  
pp. 137-166
Author(s):  
Gian Piero Zarri
Keyword(s):  
Knowledge Representation ◽  
Physical Object ◽  
Knowledge Bases ◽  
Hierarchical Organization ◽  
Indirect Speech ◽  
Reasoning Systems ◽  
Knowledge Representation Language ◽  
Representation Language ◽  
Inference Techniques ◽  
Information Link

In this chapter, we evoke first the ubiquity and the importance of the so-called ‘narrative’ information, showing that the usual ontological tools are unable to offer complete and reliable solutions for representing and exploiting this type of information. We then supply some details about NKRL (Narrative Knowledge Representation Language), a fully implemented knowledge representation and inferencing environment especially created for an ‘intelligent’ exploitation of narrative knowledge. The main innovation of NKRL consists in associating with the traditional ontologies of concepts an ‘ontology of events’, in other words, a new sort of hierarchical organization where the nodes correspond to n-ary structures representing formally generic classes of elementary events like ‘move a physical object’, ‘be present in a place’, or ‘send/receive a message’. More complex, second order tools based on the ‘reification’ principle allow one to encode the ‘connectivity phenomena’ like causality, goal, indirect speech, coordination, and subordination that, in narrative information, link together ‘elementary events’. The chapter includes a description of the inference techniques proper to NKRL, and some information about the last developments of this language.

A linked open data framework to enhance the discoverability and impact of culture heritage

2018 ◽  
Vol 45 (6) ◽  
pp. 756-766 ◽  
Author(s):  
Gustavo Candela ◽  
Pilar Escobar ◽  
Rafael C Carrasco ◽  
Manuel Marco-Such
Keyword(s):  
Information Retrieval ◽  
Open Data ◽  
Knowledge Bases ◽  
Linked Open Data ◽  
Miguel De Cervantes ◽  
Data Framework ◽  
Retrieval Systems ◽  
All Solutions ◽  
Information Retrieval Systems ◽  
Culture Heritage

Cultural heritage institutions have recently begun to consider the benefits of sharing their collections using linked open data to disseminate and enrich their metadata. As datasets become very large, challenges appear, such as ingestion, management, querying and enrichment. Furthermore, each institution has particular features related to important aspects such as vocabularies and interoperability, which make it difficult to generalise this process and provide one-for-all solutions. In order to improve the user experience as regards information retrieval systems, researchers have identified that further refinements are required for the recognition and extraction of implicit relationships expressed in natural language. We introduce a framework for the enrichment and disambiguation of locations in text using open knowledge bases such as Wikidata and GeoNames. The framework has been successfully used to publish a dataset based on information from the Biblioteca Virtual Miguel de Cervantes, thus illustrating how semantic enrichment can help information retrieval. The methods applied in order to automate the enrichment process, which build upon open source software components, are described herein.

Sign in / Sign up

Export Citation Format

Share Document