Data patterns in multiple botanical descriptions: Implications for automatic processing of legacy data

Lydon, Susannah J.; Wood, Mary McGee; Huxley, R.; Sutton, David

doi:10.1017/s1477200003001129

Cited by 18 publications

(21 citation statements)

References 7 publications

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“…Information Organization patterns. Lydon, et al (2003) reported an analysis of the information in different paper-based botanical descriptions. Five species descriptions of the genus Ranuculus L. from six different English language floras were compared.…”

Section: Text Mining In Taxonomic Literaturementioning

confidence: 99%

“…Lydon et al (2003) argues, "techniques by merging the results from several descriptions which is more complete than that in any one source, are likely to provide a way forward in dealing with the vast amounts of overlapping botanical legacy data." Our research focuses on merging the information together to improve accessibility.…”

Section: Text Mining In Taxonomic Literaturementioning

confidence: 99%

“…Integrating information from different sources for the purpose of providing more complete information about the same object becomes more important. Due to the challenges of the non-uniform nature of taxonomic literature (Lydon, et al, 2003), information fusion has only recently become the focus of research with a few pilot projects dealing with multiple documents/sources having been conducted, e.g., Wang & Pan, 2006 developed an ontology-based Information Extraction system known as MultiFlora. Their results show that retrieval performance significantly improved by summing over six botanical descriptions.…”

Section: Domain Knowledge: Green and Blue Are Different Colorsmentioning

confidence: 99%

“…Lydon et al (2003) found that there are significant amounts of information (around 55%) that can only be found in different sources or levels.…”

Section: Cdr Relationships Distributionmentioning

confidence: 99%

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Information fusion in taxonomic descriptions

Wei

2013

Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing

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Providing a single access point to an information system from multiple sources is helpful in many fields. As a case study, this research investigates the potential of applying information fusion techniques in biodiversity area since researchers in this domain desperately need information from different sources to support decision making on tasks like biological identification. Furthermore, there are massive collections in this area and the descriptive materials on the same species (object) are scattered in different places. It is not easy to manually collect information to form a broader and integrated one.As one of the most important descriptive materials in this field, floras are selected as the target of this research. This research tests a hypothesis concerning the organization of text and the constancy of fact-based information in text. It is observed that individual descriptions may not contain sufficient information to differentiate the target species from others, and different information sources might contain not only overlap information but also complementary information that is helpful. We also observe non-trivial complementary information could also be from different-level descriptions [family, genus, or species level] from the same source. By using the sample dataset from Flora of North America (FNA) and Flora of China (FOC), we found that about 50% information could only be found in single source and another 25% complementary information could be identified by fusion. And the most importantly, confliction information could only be detected by direct comparison.iii The question is how could we fuse the records in an automatic or semi-automatic manner, so that each resulting record provides a broader while non-redundant description of each species? The proposed system demonstrates the feasibility with currently available techniques. The prototype system contains 4 modules: Text segmentation and Taxonomic Name Identification, Organ-level and Sub-organ level Information Extraction, Relationship Identification, and Information fusion. By using the sample descriptions from Flora of North America and Flora of China, we demonstrate that the method gain promising fusion result based on Cross-Description Relationships. With the evaluation results, we identified the key factors contribute to the performance of fusion. Some methods that might lead to further improvement on fusion performances are discussed.This study also demonstrates that to a certain extent, this fusion approach is generalizable. The generalizability of this fusion approach is a challenging problem due to the typical domain-and task-oriented nature of the fusion methods. We identified the challenges while applying the approach to different data set. ivTo Leo, Zhi, Mom and Dad v

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Section: Text Mining In Taxonomic Literaturementioning

confidence: 99%

Section: Text Mining In Taxonomic Literaturementioning

confidence: 99%

Section: Domain Knowledge: Green and Blue Are Different Colorsmentioning

confidence: 99%

“…Lydon et al (2003) found that there are significant amounts of information (around 55%) that can only be found in different sources or levels.…”

Section: Cdr Relationships Distributionmentioning

confidence: 99%

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Information fusion in taxonomic descriptions

Wei

2013

Proceedings of the 2013 International Workshop on Mining Unstructured Big Data Using Natural Language Processing

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“…Lydon and colleagues found that only 9% of information was exactly the same in six sources, over 55% of information was from a single source, and around 1% of information contradicted information from another source. Besides the large variation, these findings also suggest that descriptions from different collections are mostly complementary to one another.As Lydon et al (2003) concluded, any automatic markup software program must take the variation into account to avoid an overly-tailored system that works only on one or a few description collections. In other words, it is highly desirable for a system to be easily portable to a different description collection.…”

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confidence: 99%

Converting Taxonomic Descriptions to New Digital Formats

Cui¹

2008

Biodiv. Inf.

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Abstract. The majority of taxonomic descriptions are currently in print format. The majority of digital descriptions are in a format, such as DOC, HTML, or PDF, for human readers. These formats do not convey rich semantics in taxonomic descriptions for computer-aided processing. Newer digital formats, such as XML and RDF, accommodate semantic annotations that allow a computer to process the rich semantics on human's behalf, opening up opportunities for a wide range of innovative usages of taxonomic descriptions, including searching in more precise and flexible ways, integrating morphological, genomic, georeference, or other information, automatically generating taxonomic keys, and knowledge mining and visualizing taxonomic data etc. This paper reports our experience with the development of an automated semantic markup system named MARTT and discusses challenging issues involved. To address these challenging issues, a number of utilities were implemented to make MARTT a more operable system. The utilities can be used to speed up the preparation of training examples for MARTT, to facilitate the creation of more comprehensive annotation schemas, and to predict system performance on a new collection of descriptions. MARTT has been tested on several plant and alga taxonomic publications including Flora of China, Flora of North America, and Flora of North Central Texas.Key words. Digital formats, morphological descriptions, semantic markup, supervised machine learning, system evaluation , taxonomic descriptions, unsupervised machine learning, XML.Taxonomic descriptions of living organisms are a major information resource used by systematists and evolutionary biologists. The majority of such information is in a print or digital format for human readers.On-going and planned digitalization projects such as those initiated by the Global Biodiversity Information Facility (GBIF, 2007) and the Biodiversity Heritage Library (BHL, 2007) will likely increase the volumes of taxonomic descriptions in legacy formats (e.g., DOC, HTML, or PDF). These documents will have to be converted to a new digital format such as XML or RDF to allow for any innovative usages beyond keyword-based search. Due to the scale of the problem, automated means for the conversion must be sought.Large volumes of taxonomic descriptions, print or digital, have been produced over the past two hundred years. While descriptions created by trained taxonomists are of high quality and provide consistent information in general, there is not a well-defined and well-accepted standard to regulate the content of a description. A manual comparison among the descriptions of five plant species, found in six well-known floras, revealed surprisingly large variations in terms of description content and style (Lydon et al, 2003). Lydon and colleagues found that only 9% of information was exactly the same in six sources, over 55% of information was from a single source, and around 1% of information contradicted information from another source. Besides the large variation,...

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The reusability of induced knowledge for the automatic semantic markup of taxonomic descriptions

Cui¹,

Heidorn

2006

J. Am. Soc. Inf. Sci.

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To automatically convert legacy data of taxonomic descriptions into extensible markup language (XML) format, the authors designed a machine-learning-based approach. In this project three corpora of taxonomic descriptions were selected to prove the hypothesis that domain knowledge and conventions automatically induced from some semistructured corpora (i.e., base corpora) are useful to improve the markup performance of other less-structured, quite different corpora (i.e., evaluation corpora). The "structuredness" of the three corpora was carefully measured. Basing on the structuredness measures, two of the corpora were used as the base corpora and one as the evaluation corpus. Three series of experiments were carried out with the MARTT (markuper of taxonomic treatments) system the authors developed to evaluate the effectiveness of different methods of using the n-gram semantic class association rules, the element relative position probabilities, and a combination of the two types of knowledge mined from the automatically marked-up base corpora. The experimental results showed that the induced knowledge from the base corpora was more reliable than that learned from the training examples alone, and that the n-gram semantic class association rules were effective in improving the markup performance, especially on the elements with sparse training examples. The authors also identify a number of challenges for any automatic markup system using taxonomic descriptions.

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Data patterns in multiple botanical descriptions: Implications for automatic processing of legacy data

Cited by 18 publications

References 7 publications

Information fusion in taxonomic descriptions

Information fusion in taxonomic descriptions

Converting Taxonomic Descriptions to New Digital Formats

The reusability of induced knowledge for the automatic semantic markup of taxonomic descriptions

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