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<!DOCTYPE html PUBLIC "-//W3C//DTD XHTML 1.0 Transitional//EN" "http://www.w3.org/TR/xhtml1/DTD/xhtml1-transitional.dtd"> <html xmlns="http://www.w3.org/1999/xhtml" xml:lang="en" lang="en"> <head> <meta http-equiv="content-type" content="text/html; charset=UTF-8" /> <meta name="viewport" content="initial-scale=1.0, user-scalable=no" /> <meta name="description" content="Ontologies are essential tools for organizing information on taxonomy, ecology, and inter-species relationships, helping to standardize ecological data and facilitate integration of large datasets. Combining ontologies with advanced Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and Relation Extraction (RE), has greatly improved the discovery of insights from unstructured scientific texts, particularly in biodiversity (Gabud et al. 2023, Abdelmageed et al. 2022, Hearst 1992).This study combines ontologies and NLP to analyze complex trophic interactions among animal species (Gabud et al. 2023), using a dataset (National Biodiversity Institute of Costa Rica (INBio) 2015) containing species descriptions in English and Spanish. We applied TaxoNERD to identify taxonomic entities (Le Guillarme and Thuiller 2021) and we fine-tuned the Large Language Model Meta AI (LLaMA 2 7B) to extract feeding interactions and predator-prey relationships (CheeKean 2023), due to its effectiveness in handling complex language patterns and its adaptability to diverse scientific domains.Our results (Fig. 1) showed a recall of 0.73 and a precision of 0.68, indicating that the model effectively identifies feeding relationships in most cases. However, the lower precision suggests that the model may still capture some unrelated interactions, highlighting an area for improvement to reduce false positives and increase accuracy (Touvron et al. 2023). Previous studies also emphasize the need for further refinement of relation extraction models to enhance accuracy (Mora-Cross et al. 2023). The structured dataset offers valuable insights into species’ diets and roles, contributing to biodiversity research and conservation efforts (Mora-Cross et al. 2023, Touvron et al. 2023).Moreover, this research highlights the potential of integrating AI-driven tools with ontological frameworks to manage and analyze biodiversity data at scale (Abdelmageed et al. 2022). By transforming unstructured text into structured data, we make ecological information more accessible, supporting better decision-making in conservation strategies (Abdelmageed et al. 2022, Hearst 1992). This approach scales well with the growing volume of biodiversity data, offering a more efficient and accurate method for analyzing species interactions, which are crucial for ecosystem management and endangered species protection (Gabud et al. 2023)."/> <meta name="keywords" content="biodiversity, ontologies, Named Entity Recognition (NER), Relation Extraction (RE), LLaMA2-7b, feeding relationships"/> <meta name="author" content="Fabricio Rios Montero, Ervin Rodríguez, Maria Mora Cross"/> <meta name="distribution" content="global"/> <meta name="robots" content="index, follow, all"/> <meta property="og:image" content="https://biss.pensoft.net//img/7TVeXpoqfNYT89tyrm3ifrTeG9Wv8P676JSQp%2FH2pj9hhtoybol4GF7LEbj3fxHT5Fo8esHsttwAZJVlYxveclqaWK47d9IQa44hjS%2FAFhD1dDA%3D.jpg"/> <meta property="og:title" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta property="og:description" content="Ontologies are essential tools for organizing information on taxonomy, ecology, and inter-species relationships, helping to standardize ecological data and facilitate integration of large datasets. Combining ontologies with advanced Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and Relation Extraction (RE), has greatly improved the discovery of insights from unstructured scientific texts, particularly in biodiversity (Gabud et al. 2023, Abdelmageed et al. 2022, Hearst 1992).This study combines ontologies and NLP to analyze complex trophic interactions among animal species (Gabud et al. 2023), using a dataset (National Biodiversity Institute of Costa Rica (INBio) 2015) containing species descriptions in English and Spanish. We applied TaxoNERD to identify taxonomic entities (Le Guillarme and Thuiller 2021) and we fine-tuned the Large Language Model Meta AI (LLaMA 2 7B) to extract feeding interactions and predator-prey relationships (CheeKean 2023), due to its effectiveness in handling complex language patterns and its adaptability to diverse scientific domains.Our results (Fig. 1) showed a recall of 0.73 and a precision of 0.68, indicating that the model effectively identifies feeding relationships in most cases. However, the lower precision suggests that the model may still capture some unrelated interactions, highlighting an area for improvement to reduce false positives and increase accuracy (Touvron et al. 2023). Previous studies also emphasize the need for further refinement of relation extraction models to enhance accuracy (Mora-Cross et al. 2023). The structured dataset offers valuable insights into species’ diets and roles, contributing to biodiversity research and conservation efforts (Mora-Cross et al. 2023, Touvron et al. 2023).Moreover, this research highlights the potential of integrating AI-driven tools with ontological frameworks to manage and analyze biodiversity data at scale (Abdelmageed et al. 2022). By transforming unstructured text into structured data, we make ecological information more accessible, supporting better decision-making in conservation strategies (Abdelmageed et al. 2022, Hearst 1992). This approach scales well with the growing volume of biodiversity data, offering a more efficient and accurate method for analyzing species interactions, which are crucial for ecosystem management and endangered species protection (Gabud et al. 2023)."/> <meta property="fb:app_id" content="1129013080805726"/> <meta property="og:url" content="https://biss.pensoft.net/article/142382/"/> <meta property="og:site_name" content="Biodiversity Information Science and Standards"/> <meta property="og:type" content="article"/> <meta name="twitter:card" content="summary"/> <meta name="twitter:site" content="@BISS_Journal"/> <meta name="twitter:title" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta name="twitter:description" content="Ontologies are essential tools for organizing information on taxonomy, ecology, and inter-species relationships, helping to standardize ecological data and facilitate integration of large datasets. Combining ontologies with advanced Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and Relation Extraction (RE), has greatly improved the discovery of insights from unstructured scientific texts, particularly in biodiversity (Gabud et al. 2023, Abdelmageed et al. 2022, Hearst 1992).This study combines ontologies and NLP to analyze complex trophic interactions among animal species (Gabud et al. 2023), using a dataset (National Biodiversity Institute of Costa Rica (INBio) 2015) containing species descriptions in English and Spanish. We applied TaxoNERD to identify taxonomic entities (Le Guillarme and Thuiller 2021) and we fine-tuned the Large Language Model Meta AI (LLaMA 2 7B) to extract feeding interactions and predator-prey relationships (CheeKean 2023), due to its effectiveness in handling complex language patterns and its adaptability to diverse scientific domains.Our results (Fig. 1) showed a recall of 0.73 and a precision of 0.68, indicating that the model effectively identifies feeding relationships in most cases. However, the lower precision suggests that the model may still capture some unrelated interactions, highlighting an area for improvement to reduce false positives and increase accuracy (Touvron et al. 2023). Previous studies also emphasize the need for further refinement of relation extraction models to enhance accuracy (Mora-Cross et al. 2023). The structured dataset offers valuable insights into species’ diets and roles, contributing to biodiversity research and conservation efforts (Mora-Cross et al. 2023, Touvron et al. 2023).Moreover, this research highlights the potential of integrating AI-driven tools with ontological frameworks to manage and analyze biodiversity data at scale (Abdelmageed et al. 2022). By transforming unstructured text into structured data, we make ecological information more accessible, supporting better decision-making in conservation strategies (Abdelmageed et al. 2022, Hearst 1992). This approach scales well with the growing volume of biodiversity data, offering a more efficient and accurate method for analyzing species interactions, which are crucial for ecosystem management and endangered species protection (Gabud et al. 2023)."/> <meta name="twitter:image" content="https://biss.pensoft.net//img/7TVeXpoqfNYT89tyrm3ifrTeG9Wv8P676JSQp%2FH2pj9hhtoybol4GF7LEbj3fxHT5Fo8esHsttwAZJVlYxveclqaWK47d9IQa44hjS%2FAFhD1dDA%3D.jpg"/> <meta name="twitter:url" content="https://biss.pensoft.net/article/142382/"/> <meta name="citation_journal_title" content="Biodiversity Information Science and Standards"/> <meta name="citation_publisher" content="Pensoft Publishers"/> <meta name="citation_title" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta name="citation_abstract" content="Ontologies are essential tools for organizing information on taxonomy, ecology, and inter-species relationships, helping to standardize ecological data and facilitate integration of large datasets. Combining ontologies with advanced Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and Relation Extraction (RE), has greatly improved the discovery of insights from unstructured scientific texts, particularly in biodiversity (Gabud et al. 2023, Abdelmageed et al. 2022, Hearst 1992).This study combines ontologies and NLP to analyze complex trophic interactions among animal species (Gabud et al. 2023), using a dataset (National Biodiversity Institute of Costa Rica (INBio) 2015) containing species descriptions in English and Spanish. We applied TaxoNERD to identify taxonomic entities (Le Guillarme and Thuiller 2021) and we fine-tuned the Large Language Model Meta AI (LLaMA 2 7B) to extract feeding interactions and predator-prey relationships (CheeKean 2023), due to its effectiveness in handling complex language patterns and its adaptability to diverse scientific domains.Our results (Fig. 1) showed a recall of 0.73 and a precision of 0.68, indicating that the model effectively identifies feeding relationships in most cases. However, the lower precision suggests that the model may still capture some unrelated interactions, highlighting an area for improvement to reduce false positives and increase accuracy (Touvron et al. 2023). Previous studies also emphasize the need for further refinement of relation extraction models to enhance accuracy (Mora-Cross et al. 2023). The structured dataset offers valuable insights into species’ diets and roles, contributing to biodiversity research and conservation efforts (Mora-Cross et al. 2023, Touvron et al. 2023).Moreover, this research highlights the potential of integrating AI-driven tools with ontological frameworks to manage and analyze biodiversity data at scale (Abdelmageed et al. 2022). By transforming unstructured text into structured data, we make ecological information more accessible, supporting better decision-making in conservation strategies (Abdelmageed et al. 2022, Hearst 1992). This approach scales well with the growing volume of biodiversity data, offering a more efficient and accurate method for analyzing species interactions, which are crucial for ecosystem management and endangered species protection (Gabud et al. 2023)."/> <meta name="citation_volume" content="8"/> <meta name="citation_issue" content=""/> <meta name="citation_firstpage" content="e142382"/> <meta name="citation_lastpage" content=""/> <meta name="citation_doi" content="10.3897/biss.8.142382"/> <meta name="citation_issn" content="2535-0897"/> <meta name="citation_publication_date" content="2024/11/22"/> <meta name="citation_article_type" content="article-journal"/> <meta name="citation_pdf_url" content="https://biss.pensoft.net/article/142382/download/pdf/"/> <meta name="citation_xml_url" content="https://biss.pensoft.net/article/142382/download/xml/"/> <meta name="dc.title" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta name="dc.type" content="Conference Abstract"/> <meta name="dc.source" content="Biodiversity Information Science and Standards 8: e142382"/> <meta name="dc.date" content="2024/11/22"/> <meta name="dc.identifier" content="doi:10.3897/biss.8.142382"/> <meta name="dc.publisher" content="Pensoft Publishers"/> <meta name="dc.rights" content=""/> <meta name="dc.format" content="text/html"/> <meta name="dc.language" content="en"/> <meta name="prism.publicationName" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta name="prism.issn" content="2535-0897"/> <meta name="prism.publicationDate" content="2024/11/22"/> <meta name="prism.volume" content="8"/> <meta name="prism:issueIdentifier " content=""/> <meta name="prism.doi" content="10.3897/biss.8.142382"/> <meta name="prism.section" content="Conference Abstract"/> <meta name="prism.startingPage" content="e142382"/> <meta name="prism.endingPage" content=""/> <meta name="prism.pageRange" content="e142382"/> <meta name="prism.copyright" content="2024 Fabricio Rios Montero, Ervin Rodríguez, Maria Mora Cross"/> <meta name="prism.rightsAgent" content="biss@pensoft.net"/> <meta name="eprints.title" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta name="eprints.type" content="Conference Abstract"/> <meta name="eprints.datestamp" content="2024/11/22"/> <meta name="eprints.ispublished" content="pub"/> <meta name="eprints.date" content="2024"/> <meta name="eprints.date_type" content="published"/> <meta name="eprints.publication" content="Pensoft Publishers"/> <meta name="eprints.volume" content="8"/> <meta name="eprints.number" content=""/> <meta name="eprints.pagerange" content="e142382"/> <meta itemprop="name" content="Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B"/> <meta name="dc.creator" content="Fabricio Rios Montero"/> <meta name="dc.creator" content="Ervin Rodríguez"/> <meta name="dc.creator" content="Maria Mora Cross"/> <meta name="dc.contributor" content="Fabricio Rios Montero"/> <meta name="dc.contributor" content="Ervin Rodríguez"/> <meta name="dc.contributor" content="Maria Mora Cross"/> <meta name="citation_author" content="Fabricio Rios Montero"/> <meta name="citation_author" content="Ervin Rodríguez"/> <meta name="citation_author" content="Maria Mora Cross"/> <meta name="eprints.creators_name" content="Fabricio Rios Montero"/> <meta name="eprints.creators_name" content="Ervin Rodríguez"/> <meta name="eprints.creators_name" content="Maria Mora Cross"/> <title>Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B</title> <base href="https://biss.pensoft.net/" /> <link rel="SHORTCUT ICON" href="/i/ico/biss.ico" /> <link type="text/css" rel="stylesheet" href="/lib/css/langs/en.css?v=1723472591" media="all" title="default" /> <script type="text/javascript" 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Combining ontologies with advanced Natural Language Processing (NLP) techniques, such as Named Entity Recognition (NER) and Relation Extraction (RE), has greatly improved the discovery of insights from unstructured scientific texts, particularly in biodiversity (Gabud et al. 2023, Abdelmageed et al. 2022, Hearst 1992).This study combines ontologies and NLP to analyze complex trophic interactions among animal species (Gabud et al. 2023), using a dataset (National Biodiversity Institute of Costa Rica (INBio) 2015) containing species descriptions in English and Spanish. We applied TaxoNERD to identify taxonomic entities (Le Guillarme and Thuiller 2021) and we fine-tuned the Large Language Model Meta AI (LLaMA 2 7B) to extract feeding interactions and predator-prey relationships (CheeKean 2023), due to its effectiveness in handling complex language patterns and its adaptability to diverse scientific domains.Our results (Fig. 1) showed a recall of 0.73 and a precision of 0.68, indicating that the model effectively identifies feeding relationships in most cases. However, the lower precision suggests that the model may still capture some unrelated interactions, highlighting an area for improvement to reduce false positives and increase accuracy (Touvron et al. 2023). Previous studies also emphasize the need for further refinement of relation extraction models to enhance accuracy (Mora-Cross et al. 2023). The structured dataset offers valuable insights into species\u2019 diets and roles, contributing to biodiversity research and conservation efforts (Mora-Cross et al. 2023, Touvron et al. 2023).Moreover, this research highlights the potential of integrating AI-driven tools with ontological frameworks to manage and analyze biodiversity data at scale (Abdelmageed et al. 2022). By transforming unstructured text into structured data, we make ecological information more accessible, supporting better decision-making in conservation strategies (Abdelmageed et al. 2022, Hearst 1992). This approach scales well with the growing volume of biodiversity data, offering a more efficient and accurate method for analyzing species interactions, which are crucial for ecosystem management and endangered species protection (Gabud et al. 2023).", "image": "https://biss.pensoft.net//img/7TVeXpoqfNYT89tyrm3ifrTeG9Wv8P676JSQp/H2pj9hhtoybol4GF7LEbj3fxHT5Fo8esHsttwAZJVlYxveclqaWK47d9IQa44hjS/AFhD1dDA=.jpg", "identifier": { "@type": "PropertyValue", "propertyID": "https://registry.identifiers.org/registry/doi", "url": "https://doi.org/10.3897/biss.8.142382", "value": "10.3897/biss.8.142382" }, "sameAs": "https://doi.org/10.3897/biss.8.142382", "offers": { "@type": "Offer", "price": "3.90", "priceCurrency": "EUR", "availability": "https://schema.org/InStock", "description": "Order Printed version" }, "associatedMedia": [ { "@type": "DataDownload", "contentUrl": "https://biss.pensoft.net/article/142382/download/pdf", "description": "Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B PDF download" } ], "publisher": { "@identifier": "https://oceanexpert.org/institution/24685", "@type": "Organization", "name": "Pensoft Publishers", "logo": { "@type": "ImageObject", "url": "https://pensoft.net/new_images/pensoft_logo.svg" } }, "citation": [ { "@type": "CreativeWork", "citation": "Abdelmageed N, Löffler F, Feddoul L, Algergawy A, Samuel S, Gaikwad J, Kazem A, König-Ries B, et al. 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Proceedings of the 14th conference on Computational linguistics - 2 https://doi.org/10.3115/992133.992154" }, { "@type": "CreativeWork", "citation": "Le Guillarme N, Thuiller W, et al. (2021) TaxoNERD: Deep neural models for the recognition of taxonomic entities in the ecological and evolutionary literature. Methods in Ecology and Evolution 13 (3): 625‑641. https://doi.org/10.1111/2041-210x.13778" }, { "@type": "CreativeWork", "citation": "Mora-Cross M, Ulate W, Retana Chacón B, Biarreta Portillo M, Castro Ramírez JD, Chavarria Madriz J, et al. (2023) Structuring Information from Plant Morphological Descriptions using Open Information Extraction. Biodiversity Information Science and Standards 7 https://doi.org/10.3897/biss.7.113055" }, { "@type": "CreativeWork", "citation": "National Biodiversity Institute of Costa Rica (INBio) (2015) Atta: Species Records from Costa Rica Documented between 1999 and 2015 [Dataset]. National Biodiversity Institute of Costa Rica. URL: https://docs.google.com/spreadsheets/d/1EVIjVQYE7gw5m4uGPWrn6rXIgcnXLkqr/edit?usp=sharing&ouid=112437040868151967020&rtpof=true&sd=true" }, { "@type": "CreativeWork", "citation": "Touvron H, Bojanowski P, Caron M, Cord M, El-Nouby A, Grave E, Izacard G, Joulin A, Synnaeve G, Verbeek J, Jégou H, et al. (2023) ResMLP: Feedforward Networks for Image Classification With Data-Efficient Training. IEEE Transactions on Pattern Analysis and Machine Intelligence 45 (4): 5314‑5321. https://doi.org/10.1109/tpami.2022.3206148" } ], "creditText": [ "Rios Montero FJ, Rodr\u00edguez E, Mora Cross M (2024) Relation Extraction From Unstructured Species Descriptions Using TaxonNERD and LLaMA 2 7B. Biodiversity Information Science and Standards 8: e142382. https://doi.org/10.3897/biss.8.142382" ], "articleSection": [ "Artificial intelligence", "Computer applications", "Computer & Information sciences", "Costa Rica" ], "author": [ { "@type": "Person", "name": "Fabricio Rios Montero" }, { "@type": "Person", "name": "Ervin Rodr\u00edguez" }, { "@type": "Person", "name": "Maria Mora Cross" } ], "mainEntityOfPage": { "@type": "WebPage", "@id": "https://biss.pensoft.net/article/142382" } }</script> <script type="text/javascript">SetArticleId(142382);</script> <script type="text/javascript">SetArticlePreviewLang('en', 'en');</script>  <div class="Main-Content"> <div class="P-Article-Content-Wrapper"> <div id="article-head" class="no-annotations"> <div class="row middle-xs between-xs m0"> <div> <div class="PaperType bra">Conference Abstract</div> <div id="article-id"> Biodiversity Information Science and Standards 8: e142382 <br/> <a class="article-id-doi" href="https://doi.org/10.3897/biss.8.142382">https://doi.org/10.3897/biss.8.142382</a> (22 Nov 2024) </div> </div> <div class="col-lg-4 col-md end-xs" style="padding: 0px;"> <div class="P-Article-Type-Holder"> <div class="P-Article-Previous-Versions"> <div class="P-Article-Versions-Title">Other versions:</div> </div> </div> </div> <div style="float:right" id="info_menu-id" onClick="showInfoBar();" class="icon-mobile-menu"></div> </div> </div> <div id="article-preview"> <iframe onload="onIframeLoadHandler();" data-showpdf="" src="article_preview.php?id=142382" enable-annotation="" id="articleIframe" name="articleIframe" style="height: 1px;"></iframe> <footer class="footer"> <div class=""> <div class="row top-xs between-xs f_content" id="footer-accordion"> <div class="col-md col-sm col-xs-6"> <div class="footer_menuholder"> <div class="about_foot footer-accordion-toggle">ABOUT PENSOFT</div> <ul> <li><a href="https://pensoft.net/about#Company-profile">Company Profile</a></li> <li><a href="https://pensoft.net/about#Innovations-tools">Innovations & Tools</a></li> <li><a href="https://pensoft.net/about#Open-access-policy">Open Access & APC Policies</a></li> <li><a href="https://pensoft.net/terms">Terms of Use</a></li> <li><a href="https://pensoft.net/editorial-policies">Editorial Policies</a></li> </ul> </div> </div> <div class="col-md col-sm col-xs-6"> <div class="footer_menuholder padding_right"> <div class="products_foot footer-accordion-toggle">PRODUCTS</a></div> <ul> <li><a href="https://pensoft.net/browse-journals">Journals</a></li> <li><a href="https://ab.pensoft.net/">Advanced Books</a></li> <li><a href="https://books.pensoft.net/">Conventional Books</a></li> <li><a href="https://aca.pensoft.net/">Conference Abstracts</a></li> <li><a href="https://ap.pensoft.net/">Conference Proceedings</a></li> </ul> </div> </div> <div class="col-md col-sm col-xs-6"> <div class="footer_menuholder padding_right"> <div class="products_foot footer-accordion-toggle">SERVICES</a></div> <ul> <li><a href="https://pensoft.net/services-authors">For Authors</a></li> <li><a href="https://pensoft.net/services-journals">For Journals</a></li> <li><a href="https://pensoft.net/services-books">For Books</a></li> <li><a href="https://pensoft.net/services-conferences">For Conferences</a></li> <li><a href="https://pensoft.net/for-organisations">For Organisations</a></li> </ul> </div> </div> <div class="col-md col-sm col-xs-6 footer_menu_projects"> <div class="footer_menuholder"> <div class="other_foot footer-accordion-toggle">PROJECTS</div> <ul> <li><a href="https://pensoft.net/projects">Research Projects</a></li> <li><a href="https://pensoft.net/services-projects">Services</a></li> <li><a href="https://pensoft.net/partners">Partners</a></li> <li class="hide"><a href="https://pensoft.net/media">Brochure</a></li> </ul> </div> </div> <div class="col-md col-sm col-xs-6"> <div class="footer_menuholder"> <div class="other_foot footer-accordion-toggle">ARPHA</div> <ul> <li><a href="https://arphahub.com/about/platform">ARPHA Publishing Platform</a></li> <li><a href="https://arphahub.com/manual">ARPHA Manual</a></li> <li><a href="https://arphahub.com/about/pricing_plans">ARPHA Pricing</a></li> <li><a href="https://pensoft.net/web-services">Web Services</a></li> </ul> </div> </div> <div class="col-md col-sm col-xs-6"> <div class="footer_menuholder"> <div class="contact_foot footer-accordion-toggle">GET IN TOUCH</div> <ul> <li><a href="https://pensoft.net/contacts">Contacts</a></li> <li><a href="javascript:void(0);" onclick="redirectAndOpenModal('publish', 'https://pensoft.net/'); 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