A knowledge representation for conceptual information retrieval

1989 ◽  
Vol 4 (3) ◽  
pp. 259-283 ◽  
Author(s):  
Richard M. Tong ◽  
Lee A. Appelbaum ◽  
Victor N. Askman
Keyword(s):  
Information Retrieval ◽  
Knowledge Representation ◽  
Conceptual Information

CONSTRUCTING AN ONTOLOGY-BASED AND GRAPH-BASED KNOWLEDGE REPRESENTATION OF ENGLISH QURAN

2016 ◽  
Vol 78 (8-2) ◽  
Author(s):  
Mohamad Fauzan Noordin ◽  
Tengku Mohd. Tengku Sembok ◽  
Roslina Othman ◽  
Ria Hari Gusmita
Keyword(s):  
Information Retrieval ◽  
Knowledge Representation ◽  
Question Answering ◽  
Graph Database ◽  
Retrieval Process ◽  
Valuable Contribution ◽  
Question Answering System ◽  
Part Of Speech

This paper describes a work in constructing two models of knowledge representation (KR) in aiming to do evaluation of their achievement in contributing to increase performance of retrieving information on English Quran domain. Due to many approaches available to construct a KR in providing data for information retrieval process, there is a need to find out in what model the KR could provide a valuable contribution for retrieving information. We focused on ontology-based KR and graph database-based KR. We use Quranic Arabic corpus that available at http://www.corpus.quran.com as a source to build the KR. We extracted several data from it i.e. English token, token location, and token Part of Speech (POS). Protégé is used to construct the ontology and Neo4j is utilized in developing the graph database. Both KR models will be equipped in developing of an English Quran Question Answering system in order to evaluate their benefit.

A New Method of Calculating the Concept Similarity

2013 ◽  
Vol 321-324 ◽  
pp. 1951-1956
Author(s):  
Guo Wei Yang ◽  
Min Chen ◽  
Xiao Feng Zhang
Keyword(s):  
Artificial Intelligence ◽  
Information Retrieval ◽  
Knowledge Representation ◽  
Influencing Factor ◽  
Domain Ontology ◽  
New Method ◽  
Area Density ◽  
Concept Similarity

The study of Concept Similarity is a very important aspect of Knowledge Representation and Information Retrieval in Artificial Intelligence, and it is also a bottleneck that hasn’t been well solved in the Ontology Research. In this article, we take every influencing factor into account, especially the area density, a new method of concept similarity based-on Domain Ontology is suggested. The experiment results show that: the new method we proposed in this article can more reasonably describe the concept similarity.

scholarly journals On Knowledge Organization

2012 ◽  
Vol 13 (1) ◽  
pp. 249-256
Author(s):  
Alan Gilchrist
Keyword(s):  
Information Retrieval ◽  
Knowledge Representation ◽  
End Users ◽  
Knowledge Organization ◽  
Working Environment ◽  
Knowledge Models ◽  
Self Testing ◽  
The World ◽  
Other Information ◽  
Information Professionals

It is argued that because knowledge is abstract and every person has a unique perception of his environment and the properties and behaviour of its components, it follows that those people engaged in Knowledge Organization (and less directly Knowledge Representation) must base their work on physical records, which we may call carriers of information, or messages. The products based on analysis of these messages can then be considered as models of knowledge. Models are created in order to reduce complexity and to gain a clearer understanding of aspects of the world around us, but they must be continuously tested and revised in a working environment. The testing of the products of Knowledge Organization is often carried out by information scientists in their provision of information retrieval, whereas while the products of Knowledge Representation also rely on Knowledge Organization, they may be considered, to some extent, to be self-testing. It follows that much can be gained by a closer collaboration between those engaged in Knowledge Organization, Knowledge Representation and various other information professionals engaged in delivering information to end users.

scholarly journals Knowledge Representation and Management. From Ontology to Annotation

2015 ◽  
Vol 24 (01) ◽  
pp. 134-136
Author(s):  
S.J. Darmoni ◽  
J. Charlet ◽  
Keyword(s):  
Information Retrieval ◽  
Knowledge Representation ◽  
Medical Informatics ◽  
Comprehensive Review ◽  
Temporal Ontology ◽  
Semantic Models

Summary Objective: To summarize the best papers in the field of Knowledge Representation and Management (KRM). Methods: A comprehensive review of medical informatics literature was performed to select some of the most interesting papers of KRM published in 2014. Results: Four articles were selected, two focused on annotation and information retrieval using an ontology. The two others focused mainly on ontologies, one dealing with the usage of a temporal ontology in order to analyze the content of narrative document, one describing a methodology for building multi-lingual ontologies.Conclusion: Semantic models began to show their efficiency, coupled with annotation tools.

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