Knowledge-Based Information Retrieval in Project Extranets

2009 ◽  
Author(s):  
E.T. Santos ◽  
L.A. Nascimento
Keyword(s):  
Information Retrieval ◽  
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.

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.

Beyond the keyword barrier: knowledge-based information retrieval*

1987 ◽  
Vol 7 (4-5) ◽  
pp. 103-117 ◽  
Author(s):  
Michael Mauldin ◽  
Jaime Carbonell ◽  
Richmond Thomason
Keyword(s):  
Information Retrieval ◽  
Knowledge Based

scholarly journals Similarity analysis of court judgments using clustering of case citation data: a study

2018 ◽  
Vol 7 (2) ◽  
pp. 855
Author(s):  
Disna Davis Kachappilly ◽  
Rupali Sunil Wagh
Keyword(s):  
Information Retrieval ◽  
Data Clustering ◽  
Legal Information ◽  
Knowledge Based ◽  
Retrieval Systems ◽  
Requirements Clustering ◽  
Information Retrieval Systems ◽  
Legal Domain ◽  
Using Data ◽  
Automatic Mechanism

Information retrieval (IR) is an automatic mechanism to extract required information from a collection of unstructured or semi-structured data. IR systems minimize the effort of a user to locate the information based on the requirements. Clustering of documents is carried out as a preprocessing step for filtering irrelevant information in an IR system. Legal domain is a producer as well as consumer of huge in-formation which also contains invaluable legal knowledge and its interpretation. Knowledge based legal information retrieval systems is need of the day. Citation analysis is a technique to find the hidden relationships between the documents and is used for understanding knowledge transfer across various domains and hence becomes very important in legal domain. In this study, similarities among documents are analyzed using data clustering when applied on data of citations in court judgments.

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