Intelligent Geospatial Information Retrieval for Agricultural Experts—A Knowledge-Based Method

2010 ◽  
Vol 8 (1) ◽  
pp. 178-183 ◽  
Author(s):  
Jian Wang ◽  
Bing-Bo Gao ◽  
Zhi-Qiang Wang ◽  
Fang-Qu Niu ◽  
Meng Yu
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Knowledge Based

A Survey on Information Retrieval Models, Techniques and Applications

2017 ◽  
Vol 7 (7) ◽  
pp. 16 ◽  
Author(s):  
Ndengabaganizi Tonny James ◽  
Rajkumar Kannan
Keyword(s):  
Information Retrieval ◽  
Retrieval Models ◽  
Knowledge Based ◽  
Long Time

It has been long time many people have realized the importance of archiving and finding information. With the advent of computers, it became possible to store large amounts of information; and finding useful information from such collections became a necessity. Over the last forty years, Information Retrieval (IR) has matured considerably. Several IR systems are used on an everyday basis by a wide variety of users. Information retrieval (IR) is generally concerned with the searching and retrieving of knowledge-based information from database. In this paper, we will discuss about the various models and techniques and for information retrieval. We are also providing the overview of traditional IR models.

CIKM 2020 conference report

2021 ◽  
pp. 1-4
Author(s):  
Mathieu D'Aquin ◽  
Stefan Dietze
Keyword(s):  
United States ◽  
Machine Learning ◽  
Knowledge Management ◽  
Information Retrieval ◽  
Conference Report ◽  
The United States ◽  
Knowledge Based ◽  
The World ◽  
Science Conference ◽  
Information And Knowledge Management

The 29th ACM International Conference on Information and Knowledge Management (CIKM) was held online from the 19 th to the 23 rd of October 2020. CIKM is an annual computer science conference, focused on research at the intersection of information retrieval, machine learning, databases as well as semantic and knowledge-based technologies. Since it was first held in the United States in 1992, 28 conferences have been hosted in 9 countries around the world.

scholarly journals GeoIRIS: Geospatial Information Retrieval and Indexing System—Content Mining, Semantics Modeling, and Complex Queries

2007 ◽  
Vol 45 (4) ◽  
pp. 839-852 ◽  
Author(s):  
Chi-Ren Shyu ◽  
Matt Klaric ◽  
Grant J. Scott ◽  
Adrian S. Barb ◽  
Curt H. Davis ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Geospatial Information ◽  
Complex Queries ◽  
Indexing System ◽  
Content Mining

scholarly journals GeoFairy: Towards a one-stop and location based Service for Geospatial Information Retrieval

2017 ◽  
Vol 62 ◽  
pp. 156-167 ◽  
Author(s):  
Ziheng Sun ◽  
Liping Di ◽  
Gil Heo ◽  
Chen Zhang ◽  
Hui Fang ◽  
...  
Keyword(s):  
Information Retrieval ◽  
Location Based Service ◽  
Geospatial Information ◽  
One Stop

LIS4: Lesk Inspired Sense Specific Semantic Similarity using WordNet

2021 ◽  
pp. 2150006
Author(s):  
Saravanakumar Kandasamy ◽  
Aswani Kumar Cherukuri
Keyword(s):  
Information Retrieval ◽  
Natural Language Processing ◽  
Natural Language ◽  
Semantic Similarity ◽  
Language Processing ◽  
Gold Standard ◽  
Question Answering ◽  
Knowledge Based ◽  
Benchmark Datasets ◽  
Processing Information

Semantic similarity quantification between concepts is one of the inevitable parts in domains like Natural Language Processing, Information Retrieval, Question Answering, etc. to understand the text and their relationships better. Last few decades, many measures have been proposed by incorporating various corpus-based and knowledge-based resources. WordNet and Wikipedia are two of the Knowledge-based resources. The contribution of WordNet in the above said domain is enormous due to its richness in defining a word and all of its relationship with others. In this paper, we proposed an approach to quantify the similarity between concepts that exploits the synsets and the gloss definitions of different concepts using WordNet. Our method considers the gloss definitions, contextual words that are helping in defining a word, synsets of contextual word and the confidence of occurrence of a word in other word’s definition for calculating the similarity. The evaluation based on different gold standard benchmark datasets shows the efficiency of our system in comparison with other existing taxonomical and definitional measures.

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