Semantic Annotation of Biomedical Literature Using Google

Sætre, Rune; Tveit, Amund; Steigedal, Tonje S.; Lægreid, Astrid

doi:10.1007/11424857_36

Cited by 8 publications

(3 citation statements)

References 21 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Google is one of the most popular large-scale hypertextual web search engine in terms of audience reach and time spent searching, due to its ability to crawl and index the web in a very efficient way (Brin and Page 1998). Moreover promising biology-related results from previous work using the Internet Search Engine Google for semantic annotation of biomedical literature (Saetre et al 2005a) and annotation of protein interactions (Saetre et al 2005b) has been reported. In this work we used a specialized flavour of Google (currently in its beta version), Google Scholar that provides a way to search for scholarly literature across many disciplines and sources: peer-reviewed papers, theses, books, abstracts and articles.…”

Section: Introductionmentioning

confidence: 95%

A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies

et al. 2006

View full text Add to dashboard Cite

Traditionally housekeeping genes have been employed as endogenous reference (internal control) genes for normalization in gene expression studies. Since the utilization of single housekeepers cannot assure an unbiased result, new normalization methods involving multiple housekeeping genes and normalizing using their mean expression have been recently proposed. Moreover, since a gold standard gene suitable for every experimental condition does not exist, it is also necessary to validate the expression stability of every putative control gene on the specific requirements of the planned experiment. As a consequence, finding a good set of reference genes is for sure a non-trivial problem requiring quite a lot of lab-based experimental testing. In this work we identified novel candidate barley reference genes suitable for normalization in gene expression studies. An advanced web search approach aimed to collect, from publicly available web resources, the most interesting information regarding the expression profiling of candidate housekeepers on a specific experimental basis has been set up and applied, as an example, on stress conditions. A complementary lab-based analysis has been carried out to verify the expression profile of the selected genes in different tissues and during heat shock response. This combined dry/wet approach can be applied to any species and physiological condition of interest and can be considered very helpful to identify putative reference genes to be shortlisted every time a new experimental design has to be set up.

show abstract

Section: Introductionmentioning

confidence: 95%

A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies

et al. 2006

View full text Add to dashboard Cite

show abstract

“…Compared with PANKOW, Alchymoogle (Saetre et al 2005) works in the more specific domain of biomedicine, which uses 275 different semantic tags, much more than the 59 concepts for general phrases in the ontology used by PANKOW. Similarly, Alchymoogle utilizes Google for semantic annotation (extracting is-a relations specifically).…”

Section: Frequency Statistics-basedmentioning

confidence: 99%

Unsupervised Approaches for Textual Semantic Annotation, A Survey

Liao

Zhao

2019

ACM Comput. Surv.

View full text Add to dashboard Cite

Semantic annotation is a crucial part of achieving the vision of the Semantic Web and has long been a research topic among various communities. The most challenging problem in reaching the Semantic Web’s real potential is the gap between a large amount of unlabeled existing/new data and the limited annotation capability available. To resolve this problem, numerous works have been carried out to increase the degree of automation of semantic annotation from manual to semi-automatic to fully automatic. The richness of these works has been well-investigated by numerous surveys focusing on different aspects of the problem. However, a comprehensive survey targeting unsupervised approaches for semantic annotation is still missing and is urgently needed. To better understand the state-of-the-art of semantic annotation in the textual domain adopting unsupervised approaches, this article investigates existing literature and presents a survey to answer three research questions: (1) To what extent can semantic annotation be performed in a fully automatic manner by using an unsupervised way? (2) What kind of unsupervised approaches for semantic annotation already exist in literature? (3) What characteristics and relationships do these approaches have? In contrast to existing surveys, this article helps the reader get an insight into the state-of-art of semantic annotation using unsupervised approaches. While examining the literature, this article also addresses the inconsistency in the terminology used in the literature to describe the various semantic annotation tools’ degree of automation and provides more consistent terminology. Based on this, a uniform summary of the degree of automation of the many semantic annotation tools that were previously investigated can now be presented.

show abstract

“…NLTK has been used successfully as a teaching tool, as an individual study tool, and as a platform for prototyping and building research systems (Liddy and McCracken, 2005;Saetre et al, 2005).…”

Section: Introductionmentioning

confidence: 99%

NLTK

Bird¹

2006

Proceedings of the COLING/ACL on Interactive Presentation Sessions -

733

View full text Add to dashboard Cite

The Natural Language Toolkit is a suite of program modules, data sets and tutorials supporting research and teaching in computational linguistics and natural language processing. NLTK is written in Python and distributed under the GPL open source license. Over the past year the toolkit has been rewritten, simplifying many linguistic data structures and taking advantage of recent enhancements in the Python language. This paper reports on the simplified toolkit and explains how it is used in teaching NLP.

show abstract

Semantic Annotation of Biomedical Literature Using Google

Cited by 8 publications

References 21 publications

A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies

A combined strategy of “in silico” transcriptome analysis and web search engine optimization allows an agile identification of reference genes suitable for normalization in gene expression studies

Unsupervised Approaches for Textual Semantic Annotation, A Survey

NLTK

Contact Info

Product

Resources

About