Abstract
Scholarly publications of biodiversity literature contain a vast amount of information in human readable format. The detailed morphological descriptions in these publications contain rich information that can be extracted to facilitate analysis and computational biology research. However, the idiosyncrasies of morphological descriptions still pose a number of challenges to machines. In this work, we demonstrate the use of two different approaches to resolve meronym (i.e. part-of) relations between anatomical parts and their anchor organs, including a syntactic rule-based approach and a SVM-based (support vector machine) method. Both methods made use of domain ontologies. We compared the two approaches with two other baseline methods and the evaluation results show the syntactic methods (92.1% F1 score) outperformed the SVM methods (80.7% F1 score) and the part-of ontologies were valuable knowledge sources for the task. It is notable that the mistakes made by the two approaches rarely overlapped. Additional tests will be conducted on the development version of the Explorer of Taxon Concepts toolkit before we make the functionality publicly available. Meanwhile, we will further investigate and leverage the complementary nature of the two approaches to further drive down the error rate, as in practical application, even a 1% error rate could lead to hundreds of errors.
Highlights
IntroductionUsing the large volumes of information contained in biodiversity literature, we aim to provide scientists with rich computable data so they can build a more complete tree of life, predict the causal genes for wider ranges of diseases/conditions, derive better models of the decline of species populations and improve climate change predictions
We demonstrate the use of two different approaches to resolve meronym relations between anatomical parts and their anchor organs, including a syntactic rule-based approach and a Support vector machine (SVM)-based method
Using the large volumes of information contained in biodiversity literature, we aim to provide scientists with rich computable data so they can build a more complete tree of life, predict the causal genes for wider ranges of diseases/conditions, derive better models of the decline of species populations and improve climate change predictions
Summary
Using the large volumes of information contained in biodiversity literature, we aim to provide scientists with rich computable data so they can build a more complete tree of life, predict the causal genes for wider ranges of diseases/conditions, derive better models of the decline of species populations and improve climate change predictions. Biodiversity literature contains various descriptive information on extinct and extant taxa, including habitat, distribution, phenology, ecology, physiology and morphology. We are primarily concerned with making the information contained in morphological descriptions more accessible to machines. This work involves linking a structure/organ (e.g. leaf or stem) with its properties (e.g. colour, shape, orientation etc., often called “characters” in systematics biology), as described in the literature (Dececchi et al 2015). We refer to such terms as non-specific structure (NSS) terms. When a NSS is linked to its parent organ, as in leaf apex, it represents an independent entity and we say it is resolved. Extracting character information about such structures requires the machine to bridge the non-specific structures with their anchor organs. Associating apex acuminate with its appropriate organ, leaf, produces a complete and accurate character leaf apex accuminate
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