scholarly journals SEACOIN 2.0 – an interactive mining and visualization tool for information retrieval, summarization, and knowledge discovery

2017 ◽  
Author(s):  
Karan Uppal ◽  
Eva K. Lee
Keyword(s):  
Information Retrieval ◽  
Knowledge Discovery ◽  
Information Overload ◽  
Document Retrieval ◽  
Hypothesis Generation ◽  
Dynamic Visualization ◽  
Relational Networks ◽  
Ranking Scheme ◽  
Interactive Mining ◽  
Efficient Information

ABSTRACTMotivationThe rapidly increasing size of biomedical databases such as MEDLINE requires the use of intelligent data mining methods for information extraction and summarization. Existing biomedical text-mining tools have limited capabilities for inferring topological and network relationships between biomedical terms. Very often too much is returned during summarization leading to information overload.ResultsWe present herein SEACOIN 2.0, an interactive knowledge discovery and hypothesis generation tool for biomedical literature.SEACOIN generates k-ary relational networks of biomedical terms using a novel term ranking scheme to facilitate efficient information retrieval, summarization, and visual data exploration. Summarization is presented via multiple dynamic visualization panels. We evaluate the system performance in information retrieval and features extraction using the BioCreative 2013 Track 3 learning corpus. An average F-measure of 94% was achieved for document retrieval and an average precision of 88% was achieved for identification of top co-occurrence terms. The system allows interactive mining of complex implicit and explicit relationships among biomedical entities (genes, chemicals, diseases/disorders, mutations, etc.) and provides a framework for hypothesis generation. It also improves our understanding of various biological processes and disease [email protected]

A method of query expansion based on topic models and user profile for search in folksonomy

2021 ◽  
pp. 1-11
Author(s):  
Zhinan Gou ◽  
Yan Li
Keyword(s):  
Information Retrieval ◽  
Query Expansion ◽  
Information Overload ◽  
Topic Model ◽  
User Profile ◽  
Expansion Method ◽  
Collaborative Tagging ◽  
Search Query ◽  
Tagging System ◽  
The Web

With the development of the web 2.0 communities, information retrieval has been widely applied based on the collaborative tagging system. However, a user issues a query that is often a brief query with only one or two keywords, which leads to a series of problems like inaccurate query words, information overload and information disorientation. The query expansion addresses this issue by reformulating each search query with additional words. By analyzing the limitation of existing query expansion methods in folksonomy, this paper proposes a novel query expansion method, based on user profile and topic model, for search in folksonomy. In detail, topic model is constructed by variational antoencoder with Word2Vec firstly. Then, query expansion is conducted by user profile and topic model. Finally, the proposed method is evaluated by a real dataset. Evaluation results show that the proposed method outperforms the baseline methods.

A Data-Driven Text Mining and Semantic Network Analysis for Design Information Retrieval

2017 ◽  
Vol 139 (11) ◽  
Author(s):  
Feng Shi ◽  
Liuqing Chen ◽  
Ji Han ◽  
Peter Childs
Keyword(s):  
Information Retrieval ◽  
Network Analysis ◽  
Text Mining ◽  
Engineering Design ◽  
Large Scale ◽  
Semantic Network ◽  
Document Retrieval ◽  
Design Information ◽  
Improve Design ◽  
Correlation Degree

With the advent of the big-data era, massive information stored in electronic and digital forms on the internet become valuable resources for knowledge discovery in engineering design. Traditional document retrieval method based on document indexing focuses on retrieving individual documents related to the query, but is incapable of discovering the various associations between individual knowledge concepts. Ontology-based technologies, which can extract the inherent relationships between concepts by using advanced text mining tools, can be applied to improve design information retrieval in the large-scale unstructured textual data environment. However, few of the public available ontology database stands on a design and engineering perspective to establish the relations between knowledge concepts. This paper develops a “WordNet” focusing on design and engineering associations by integrating the text mining approaches to construct an unsupervised learning ontology network. Subsequent probability and velocity network analysis are applied with different statistical behaviors to evaluate the correlation degree between concepts for design information retrieval. The validation results show that the probability and velocity analysis on our constructed ontology network can help recognize the high related complex design and engineering associations between elements. Finally, an engineering design case study demonstrates the use of our constructed semantic network in real-world project for design relations retrieval.

scholarly journals Thesaurofacet - the information retrieval language of the 21st century

2016 ◽  
pp. 73-81
Author(s):  
Eduard Sukiasyan
Keyword(s):  
Information Retrieval ◽  
Retrieval Systems ◽  
Efficient Information ◽  
Retrieval Language

The term of “thesaurofest” is scrutinized; the characteristics of the system, the technologies of efficient information retrieval are analyzed. Prospects for retrieval systems modernization are discussed.

Web Semantics for Personalized Information Retrieval

2017 ◽  
pp. 166-186 ◽  
Author(s):  
Aarti Singh ◽  
Anu Sharma
Keyword(s):  
Information Retrieval ◽  
Semantic Web ◽  
Web Search ◽  
Information Overload ◽  
Software Agents ◽  
Web Personalization ◽  
Comprehensive Review ◽  
Web Technologies ◽  
Intelligent Interface

This chapter explores the synergy between Semantic Web (SW) technologies and Web Personalization (WP) for demonstrating an intelligent interface for Personalized Information Retrieval (PIR) on web. Benefits of adding semantics to WP through ontologies and Software Agents (SA) has already been realized. These approaches are expected to prove useful in handling the information overload problem encountered in web search. A brief introduction to PIR process is given, followed by description of SW, ontologies and SA. A comprehensive review of existing web technologies for PIR has been presented. Although, a huge contribution by various researchers has been seen and analyzed but still there exist some gap areas where the benefits of these technologies are still to be realized in future personalized web search.

Genetic-Fuzzy Programming Based Linkage Rule Miner (GFPLR-Miner) for Entity Linking in Semantic Web

2021 ◽  
pp. 447-481
Author(s):  
Amit Singh ◽  
Aditi Sharan
Keyword(s):  
Fuzzy Logic ◽  
Information Retrieval ◽  
Semantic Web ◽  
Real World ◽  
Fuzzy Programming ◽  
Data Sources ◽  
Fuzzy Approach ◽  
Entity Linking ◽  
Efficient Information ◽  
Domain Independent

This article describes how semantic web data sources follow linked data principles to facilitate efficient information retrieval and knowledge sharing. These data sources may provide complementary, overlapping or contradicting information. In order to integrate these data sources, the authors perform entity linking. Entity linking is an important task of identifying and linking entities across data sources that refer to the same real-world entities. In this work, they have proposed a genetic fuzzy approach to learn linkage rules for entity linking. This method is domain independent, automatic and scalable. Their approach uses fuzzy logic to adapt mutation and crossover rates of genetic programming to ensure guided convergence. The authors' experimental evaluation demonstrates that our approach is competitive and make significant improvements over state of the art methods.

Engines of Order

2020 ◽  
Author(s):  
Bernhard Rieder
Keyword(s):  
Information Overload ◽  
Document Retrieval ◽  
Specific Situation ◽  
Practical Application ◽  
Online Environments

The chapter discusses central terms like ‘information’ and ‘order’, and it proposes the concept of ‘engine’ to point toward the infrastructural embeddings that have allowed techniques initially conceived for document retrieval to become pervasive mediators in online environments. While this book constitutes a humanistic exploration of technical substances rather than their practical application, the chapter pays tribute to the fact that the techniques under scrutiny have become prevalent in a specific situation, in this world and not another. To this end, the chapter discusses three critical trends: computerization, information overload, and social diversification.

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