Optimization of Information Retrieval Algorithm for Digital Library Based on Semantic Search Engine

Author(s):  
Zhen Pan
2014 ◽  
Vol 2014 ◽  
pp. 1-13 ◽  
Author(s):  
Xi Chen ◽  
Huajun Chen ◽  
Xuan Bi ◽  
Peiqin Gu ◽  
Jiaoyan Chen ◽  
...  

Understanding the functional mechanisms of the complex biological system as a whole is drawing more and more attention in global health care management. Traditional Chinese Medicine (TCM), essentially different from Western Medicine (WM), is gaining increasing attention due to its emphasis on individual wellness and natural herbal medicine, which satisfies the goal of integrative medicine. However, with the explosive growth of biomedical data on the Web, biomedical researchers are now confronted with the problem of large-scale data analysis and data query. Besides that, biomedical data also has a wide coverage which usually comes from multiple heterogeneous data sources and has different taxonomies, making it hard to integrate and query the big biomedical data. Embedded with domain knowledge from different disciplines all regarding human biological systems, the heterogeneous data repositories are implicitly connected by human expert knowledge. Traditional search engines cannot provide accurate and comprehensive search results for the semantically associated knowledge since they only support keywords-based searches. In this paper, we present BioTCM-SE, a semantic search engine for the information retrieval of modern biology and TCM, which provides biologists with a comprehensive and accurate associated knowledge query platform to greatly facilitate the implicit knowledge discovery between WM and TCM.


Author(s):  
Li Sheng ◽  
Zheng Kaihong ◽  
Yang Jinfeng ◽  
Wang Xin ◽  
Zeng Lukun ◽  
...  

Author(s):  
Lisa Langnickel ◽  
Roman Baum ◽  
Johannes Darms ◽  
Sumit Madan ◽  
Juliane Fluck

During the current COVID-19 pandemic, the rapid availability of profound information is crucial in order to derive information about diagnosis, disease trajectory, treatment or to adapt the rules of conduct in public. The increased importance of preprints for COVID-19 research initiated the design of the preprint search engine preVIEW. Conceptually, it is a lightweight semantic search engine focusing on easy inclusion of specialized COVID-19 textual collections and provides a user friendly web interface for semantic information retrieval. In order to support semantic search functionality, we integrated a text mining workflow for indexing with relevant terminologies. Currently, diseases, human genes and SARS-CoV-2 proteins are annotated, and more will be added in future. The system integrates collections from several different preprint servers that are used in the biomedical domain to publish non-peer-reviewed work, thereby enabling one central access point for the users. In addition, our service offers facet searching, export functionality and an API access. COVID-19 preVIEW is publicly available at https://preview.zbmed.de.


Author(s):  
Ricardo Usbeck ◽  
Michael Röder ◽  
Peter Haase ◽  
Artem Kozlov ◽  
Muhammad Saleem ◽  
...  

Author(s):  
Omar Shehab ◽  
Ali Hussein Saleh Zolait

In this paper, the authors propose a Semantic Search Engine, which retrieves software components precisely and uses techniques to store these components in a database, such as ontology technology. The engine uses semantic query language to retrieve these components semantically. The authors use an exploratory study where the proposed method is mapped between object-oriented concepts and web ontology language. A qualitative survey and interview techniques were used to collect data. The findings after implementing this research are a set of guidelines, a model, and a prototype to describe the semantic search engine system. The guidelines provided help software developers and companies reduce the cost, time, and risks of software development.


Sign in / Sign up

Export Citation Format

Share Document