Information Extraction Technology Research Articles

Remote sensing for the sea ice effect monitoring in petroleum development: Take the Nanpu sea area as an example

Abstract Sea ice monitoring is one of the important guarantees for marine operation and transport. The space borne remote sensing techniques have the advantages of wide area surveillance, multi-temporal resolution, prompt acquisition, and high precision. They have become one of the key sea ice monitoring techniques. The new Chinese satellite HJ-1A/B CCD images were used in the sea ice monitoring of the Nanpu sea area in winter, during the time from Nov. 29, 2008 to Feb. 21, 2009. A fast processing work flow was established based on the object-oriented information extraction technology. It was used to map the change of the ice area during the period. According to the ice area change, the ice sea distribution, and the historical meteorological information, the ice period was divided into: initial ice (early Dec., 2008 to the end of Dec., 2008), abounding ice (from the end of Dec., 2008 to the end of Jan., 2009), ice thawing (from the end of Jan., 2009 to the end of Feb., 2009). The work contributes to the safe construction of the artificial islands in the Nanpu-4 structure during the icebound season. The study shows that the remote sensing technology is a practical and effective technical means for the sea ice security monitoring of oil fields' exploration, development and engineering construction in beaches.

Prioritization of Domain-Specific Web Information Extraction

It is often desirable to extract structured information from raw web pages for better information browsing, query answering, and pattern mining. many such Information Extraction (IE) technologies are costly and applying them at the web-scale is impractical. In this paper, we propose a novel prioritization approach where candidate pages from the corpus are ordered according to their expected contribution to the extraction results and those with higher estimated potential are extracted earlier. Systems employing this approach can stop the extraction process at any time when the resource gets scarce (i.e., not all pages in the corpus can be processed), without worrying about wasting extraction effort on unimportant pages. More specifically, we define a novel notion to measure the value of extraction results and design various mechanisms for estimating a candidate page’s contribution to this value. We further design and build the Extraction Prioritization (EP) system with efficient scoring and scheduling algorithms, and experimentally demonstrate that EP significantly outperforms the naive approach and is more flexible than the classifier approach.

Analysis and construction of pathogenicity island regulatory pathways in Salmonella enterica serovar Typhi

SummarySignal transduction through protein-protein interactions and protein modifications are the main mechanisms controlling many biological processes. Here we described the implementation of MedScan information extraction technology and Pathway Studio software (Ariadne Genomics Inc.) to create a Salmonella specific molecular interaction database. Using the database, we have constructed several signal transduction pathways in Salmonella enterica serovar Typhi which causes Typhoid Fever, a major health threat especially in developing countries. S. Typhi has several pathogenicity islands that control rapid switching between different phenotypes including adhesion and colonization, invasion, intracellular survival, proliferation, and biofilm formation in response to environmental changes. Understanding of the detailed mechanism for S. Typhi survival in host cells is necessary for development of efficient detection and treatment of this pathogen. The constructed pathways were validated using publically available gene expression microarray data for Salmonella.

A Method of Web Information Automatic Extraction Based on XML

With the increasingly high-speed of the internet as well as the increase in the amount of data it contains, users are finding it more and more difficult to gain useful information from the web. How to extract accurate information from the Web efficiently has become an urgent problem. Web information extraction technology has emerged to solve this kind of problem. The method of Web information auto-extraction based on XML is designed through standardizing the HTML document using data translation algorism, forming an extracting rule base by learning the XPath expression of samples, and using extraction rule base to realize auto-extraction of pages of same kind. The results show that this approach should lead to a higher recall ratio and precision ratio, and the result should have a self-description, making it convenient for founding data extraction system of each domain.

Text-mining of PubMed abstracts by natural language processing to create a public knowledge base on molecular mechanisms of bacterial enteropathogens.

BackgroundThe Enteropathogen Resource Integration Center (ERIC; ) has a goal of providing bioinformatics support for the scientific community researching enteropathogenic bacteria such as Escherichia coli and Salmonella spp. Rapid and accurate identification of experimental conclusions from the scientific literature is critical to support research in this field. Natural Language Processing (NLP), and in particular Information Extraction (IE) technology, can be a significant aid to this process.DescriptionWe have trained a powerful, state-of-the-art IE technology on a corpus of abstracts from the microbial literature in PubMed to automatically identify and categorize biologically relevant entities and predicative relations. These relations include: Genes/Gene Products and their Roles; Gene Mutations and the resulting Phenotypes; and Organisms and their associated Pathogenicity. Evaluations on blind datasets show an F-measure average of greater than 90% for entities (genes, operons, etc.) and over 70% for relations (gene/gene product to role, etc). This IE capability, combined with text indexing and relational database technologies, constitute the core of our recently deployed text mining application.ConclusionOur Text Mining application is available online on the ERIC website . The information retrieval interface displays a list of recently published enteropathogen literature abstracts, and also provides a search interface to execute custom queries by keyword, date range, etc. Upon selection, processed abstracts and the entities and relations extracted from them are retrieved from a relational database and marked up to highlight the entities and relations. The abstract also provides links from extracted genes and gene products to the ERIC Annotations database, thus providing access to comprehensive genomic annotations and adding value to both the text-mining and annotations systems.

Study on shallow groundwater information extraction technology based on multi-spectral data and spatial data

Aimed at solving the difficulties, such as low efficiency and limited exploration range encountered in finding groundwater with the traditional methods, a new method was presented by using remote sensing technology in this paper. Based on multi-spectral data (ETM data) and spatial data (SRTM data), a forecasting model was built to produce a probability rating map for finding shallow groundwater in the arid and semi-arid areas. According to investigations, a conclusion is drawn that the results of the model are satisfied, which have been testified by the later geophysical exploration and drilling. Thus, the model can serve as a guide for finding groundwater in the arid and semi-arid regions.

Concept recognition for extracting protein interaction relations from biomedical text

Background:Reliable information extraction applications have been a long sought goal of the biomedical text mining community, a goal that if reached would provide valuable tools to benchside biologists in their increasingly difficult task of assimilating the knowledge contained in the biomedical literature. We present an integrated approach to concept recognition in biomedical text. Concept recognition provides key information that has been largely missing from previous biomedical information extraction efforts, namely direct links to well defined knowledge resources that explicitly cement the concept's semantics. The BioCreative II tasks discussed in this special issue have provided a unique opportunity to demonstrate the effectiveness of concept recognition in the field of biomedical language processing.Results:Through the modular construction of a protein interaction relation extraction system, we present several use cases of concept recognition in biomedical text, and relate these use cases to potential uses by the benchside biologist.Conclusion:Current information extraction technologies are approaching performance standards at which concept recognition can begin to deliver high quality data to the benchside biologist. Our system is available as part of the BioCreative Meta-Server project and on the internet .

Classification of Urban Land Based on Object-oriented Information Extraction Technology

Object-oriented information extraction technology compared with the pixel-based classification method is suitable for classification of high resolution remotely sensed images.Object-oriented image analysis has two key technologies,multi-scale image segmentation and classification technologies based segmentation.Urban area of Huairou was selected as study area,and the purpose is to extract information from QuickBird image using above approach.The conclusions are: ①pepper and salt noises are discarded;② 84.82% overall accuracy is achieved while only 73.87% is achieved with traditional pixel-based method.Furthermore,precision of each kind of object information was also improved,particularly for grass,roads and building shadows.

Adaptive information extraction

The growing availability of online textual sources and the potential number of applications of knowledge acquisition from textual data has lead to an increase in Information Extraction (IE) research. Some examples of these applications are the generation of data bases from documents, as well as the acquisition of knowledge useful for emerging technologies like question answering, information integration, and others related to text mining. However, one of the main drawbacks of the application of IE refers to its intrinsic domain dependence. For the sake of reducing the high cost of manually adapting IE applications to new domains, experiments with different Machine Learning (ML) techniques have been carried out by the research community. This survey describes and compares the main approaches to IE and the different ML techniques used to achieve Adaptive IE technology.

A Collaborative Biomedical Research System

The convergence of need between improved clinical care and post genomics research presents a unique challenge to restructuring information flow so that it benefits both without compromising patient safety or confidentiality. The CLEF project aims to link-up heath care with bioinformatics to build a collaborative research platform that enables a more effective biomedical research. In that, it addresses various barriers and issues, including privacy both by policy and by technical means, towards establishing its eventual system. It makes extensive use of language technology for information extraction and presentation, and its shared repository is based around coherent chronicles of patients' histories that go beyond traditional health record structure. It makes use of a collaborative research workbench that encompasses several technologies and uses many tools providing a rich platform for clinical researcher.

The Technology of Web Information Extraction and Its Application in the TBT Early-Warning System

The Technology of Web Information Extraction and Its Application in the TBT Early-Warning System

Mining knowledge from text using information extraction

An important approach to text mining involves the use of natural-language information extraction. Information extraction (IE) distills structured data or knowledge from unstructured text by identifying references to named entities as well as stated relationships between such entities. IE systems can be used to directly extricate abstract knowledge from a text corpus, or to extract concrete data from a set of documents which can then be further analyzed with traditional data-mining techniques to discover more general patterns. We discuss methods and implemented systems for both of these approaches and summarize results on mining real text corpora of biomedical abstracts, job announcements, and product descriptions. We also discuss challenges that arise when employing current information extraction technology to discover knowledge in text.

The BioLink SIG workshop at ISMB2004

The Special Interest Group (SIG) on Text Mining (or BioLINK — Biological Literature, Information and Knowledge; http://www.pdg.cnb.uam.es/BioLINK/) was created to address the need for communication and interchange of ideas in the field of text mining and information extraction applied to biology and biomedicine. Information extraction (IE) is an outgrowth of work in automated natural language processing, which began in the 1950s with work on transformational grammar by Zellig Harris [5,6] and later Noam Chomsky [3,4]. Information extraction technology made rapid progress starting in the late 1980s, thanks to a series of conferences focused on evaluation of IE: the Message Understanding Conferences [1]. There is also a long history of research on applications in medicine. Applications to the medical field focus on two distinct sub-problems: improved access to the medical literature and extraction of information from patient records. Despite these successes in other fields, natural language processing (NLP) techniques were not introduced in biology until the late 1990s. Even today, there are two distinct groups: on the one hand, researchers with a background in computer science, and on the other hand, their colleagues with a background in the life sciences, with only limited interaction between the two groups. To improve this situation, the BioLINK group holds regular open meetings to bring together researchers developing text data mining tools and related language processing methods to manage the information explosion in the biomedical field. They include invited and contributed papers, with a focus on developing shared infrastructure (tools, corpora, ontologies) and challenge evaluations, in the style of the KDD Challenge Cups [2]. This year, the BioLINK SIG meeting focused on resources and tools for text mining, with special emphasis on the evaluation of these tools. Speakers from the following areas were invited:

Information Extraction Using Concept Node Analysis of Brain Radiology Reports Summarization

OBJECTIVE: Electronic Medical Record contains the majority of clinical data in unstructured text. The information in the textual document can be stored in conceptual format and used to support clinical care by text summarization technique. In this study, we present Information Extraction(IE) using Concept Node(CN) which is extraction rule in case frame from brain radiology reports in SNUH(Seoul National University Hospital) for summarization. METHOD: Following steps are performed: design conceptual model to define semantic entities as extraction templates of brain radiology report, build CN dictionary based on statistical syntactic pattern and development of parser to extract relevant information based on defined templates. RESULTS: The three evaluation results shows that 19% precision improvement after post processing supplemental specified complex verb construction and 19.24~21.25% accurate semantic effectiveness with extracting additional Korean noun. The average of precision is 85.18%, average of recall is 93.71% and F-measure is 0.89. CONCLUSION: Our approach has advantageous elements for different language at the same sentence. We expect this IE technology can summarize vast amount radiology texts material for clinical decision support system effectively and hope this study helps the evolution of clinical data representation in Korean medical records and its integration into the EMR in the future.

Combining information extraction with genetic algorithms for text mining

An evolutionary approach that combines information extraction technology and genetic algorithms can produce a new, integrated model for text mining. Text mining discovers unseen patterns in textual databases. We've brought together the benefits of GAs for data mining and IE technology to propose a new approach for high-level knowledge discovery. Unlike previous KDT approaches, our model doesn't rely on external resources or conceptual descriptions. Instead, it performs the discovery using only information from the original corpus of text documents and from training data computed from them. The GA that produces the hypotheses is strongly guided by semantic constraints, which means that several specifically defined metrics evaluate the quality and plausibility.

ISMB 2003 Text Mining SIG meeting report

The third meeting of the Special Interest Group (SIG) for Text Mining was held in conjunction with ISMB in Australia this year, following the 2001 meeting in Copenhagen and the 2002 meeting in Edmonton. The Text Mining SIG has been organized by the BioLINK group (http://www.pdg.cnb.uam.es/BioLINK), with its main contributors Lynette Hirschman (MITRE, Bedford, MA, USA) and Alfonso Valencia (CNB, Madrid, Spain), together with this year’s local organizers Christian Blaschke (CNB), Marc Light (University of Iowa, USA) and Alexander Yeh (MITRE). The SIG’s main goal has been to foster communication in text mining and information extraction applied to biology and biomedicine. To this end, the BioLINK group holds regular open meetings to bring together researchers from the field to interchange ideas and share them with a wider community interested in the latest developments. In the past two meetings, the Text Mining SIG has included reports from related SIGs (e.g. BioOntologies and BioPathways). Information extraction (IE) is an outgrowth of work in automated natural language processing, which began in the 1950s with work on transformational grammar by Zellig Harris and later Noam Chomsky. Information extraction technology made rapid progress starting in the late 1980s, thanks to a series of conferences focused on evaluation of IE: the Message Understanding Conferences (MUCs). There has also been a long history of research on applications in medicine. Applications to the medical field focus on two distinct sub-problems: (a) improved access to the medical literature; and (b) extraction of information from patient records. Despite the successes in other fields, Natural Language Processing (NLP) techniques were not introduced into biology until the late 1990s. The field has been dominated by two, not necessarily convergent, approaches: (a) application-orientated, where simple methods are used (possibly too simple) to address ‘real’ biological problems; and (b) tool-orientated, where complex, state-of-the-art NLP methods are used to address problems that are not always relevant to biologists. During the SIG meeting, it became apparent that three major bottlenecks hinder current development:

Opinion Classification Through Information Extraction

In this paper we present an application of information extraction technologies to data mining. The system has the goal of producing reliable reports on opinions that customers express about companies and/or products. Information extraction is regarded as an enabling technology by which structured databases can be populated from unstructured texts available on the web. The approach can be considered as an alternative to standard text mining techniques: rather than applying data mining algorithms on textual inputs, we propose to apply syntactic and semantic processing in order to disclose structured information which abstrac~s completely from linear order of words and language dependent constmctions.

Can bibliographic pointers for known biological data be found automatically? Protein interactions as a case study

The Dictionary of Interacting Proteins (DIP) (Xenarios et al., 2000) is a large repository of protein interactions: its March 2000 release included 2379 protein pairs whose interactions have been detected by experimental methods. Even if many of these correspond to poorly characterized proteins, the result of massive yeast two-hybrid screenings, as many as 851 correspond to interactions detected using direct biochemical methods. We used information retrieval technology to search automatically for sentences in Medline abstracts that support these 851 DIP interactions. Surprisingly, we found correspondence between DIP protein pairs and Medline sentences describing their interactions in only 30% of the cases. This low coverage has interesting consequences regarding the quality of annotations (references) introduced in the database and the limitations of the application of information extraction (IE) technology to Molecular Biology. It is clear that the limitation of analyzing abstracts rather than full papers and the lack of standard protein names are difficulties of considerably more importance than the limitations of the IE methodology employed. A positive finding is the capacity of the IE system to identify new relations between proteins, even in a set of proteins previously characterized by human experts. These identifications are made with a considerable degree of precision. This is, to our knowledge, the first large scale assessment of IE capacity to detect previously known interactions: we thus propose the use of the DIP data set as a biological reference to benchmark IE systems.

The Need for Accurate Alignment in Natural Language System Evaluation

As evaluations of computational linguistics technology progress toward higher-level interpretation tasks, the problem of determining alignments between system responses and answer key entries may become less straightforward. We present an extensive analysis of the alignment procedure used in the MUC-6 evaluation of information extraction technology, which reveals effects that interfere with the stated goals of the evaluation. These effects are shown to be pervasive enough that they have the potential to adversely impact the technology development process. These results argue strongly for the use of accurate alignment criteria in natural language evaluations, and for maintaining the independence of alignment criteria and mechanisms used to calculate scores.

Bioinformatics applications of information extraction from scientific journal articles

Information extraction technology, as defined and developed through the US Defense Advanced Research Projects Agency (DARPA) Message Understanding Conferences (MUCs), has proved successful at extracting information primarily from newswire texts and primarily in domains concerned with human activity. In this paper, the application of this technology to the extraction of information from scientific journal papers in the area of molecular biology is considered. In particular, it is described how an information extraction designed to participate in the MUC exercises has been modified for two bioinformatics applications: one concerned with enzyme and metabolic pathways; the other with protein structure. Progress to date provides convincing grounds for believing that information extraction techniques will deliver novel and effective ways for scientists to make use of the core literature which defines their disciplines.