=Paper=
{{Paper
|id=Vol-3797/paper23
|storemode=property
|title=
Application of Artificial Intelligence to Knowledge Discovery for the Maintenance and Conservation of Botanical Gardens
|pdfUrl=https://ceur-ws.org/Vol-3797/paper23.pdf
|volume=Vol-3797
|authors=Claudia Aguilar-Rajme
|dblpUrl=https://dblp.org/rec/conf/sepln/Aguilar-Rajme24
}}
==
Application of Artificial Intelligence to Knowledge Discovery for the Maintenance and Conservation of Botanical Gardens
==
https://ceur-ws.org/Vol-3797/paper23.pdf
Application of Artificial Intelligence to Knowledge
Discovery for the Maintenance and Conservation of
Botanical Gardens
Claudia Aguilar-Rajme1,2
1
GPLSI research group, University of Alicante, Spain
2
Department of Computer Science, Technical Sciences Faculty, Agricultural University of Havana, Cuba
Abstract
Botanical gardens play a key role in the conservation of biodiversity and generate large amounts of data related
to the management and maintenance of plants on a daily basis. In this sense, the Network of Botanical Gardens
in Cuba stands out, made up of 12 institutions that, among other things, have in common that they manage these
data by dividing them into three main registers: Introduction Register, Living Plant Register and Herbarium.
The diversity of formats and the heterogeneity of the information make it difficult to manage by the Center’s
specialists, as well as to make it accessible to researchers and students in search of references. In this context,
there is an opportunity to use artificial intelligence and natural language processing techniques to facilitate
access to the implicit and explicit information contained in these records. To this end, it is proposed to evaluate
the results of their use in order to finally obtain a product capable of using the extracted knowledge to provide
recommendations and guidelines for the management and conservation of plants.
Keywords
Natural Language Processing, Information Extraction, Botany, Gardens
1. Justification of the proposed research
According to Smith and Harvey-Brown [1], the most widely accepted definition of botanic gardens is
that expressed by Jackson in 1999 [2], who stated that they are "institutions containing documented
collections of living plants for the purposes of scientific research, conservation, exhibition and education".
Among the main functions of these centers are :
• proper documentation of collections, including wild origin,
• monitoring of collected individuals,
• communication and information to and from other Gardens, other institutions, and the public;
and
• promotion of conservation through extension and environmental education activities [3].
These tasks involve the management of large amounts of data, which then become sources of frequent
consultation for researchers, students, and the general public. The work of botanic gardens is essential
to the preservation of the planet’s biodiversity, and this is one of the reasons why botanic garden
institutions exist all over the world.
In Cuba, there is a network of botanical gardens made up of 12 institutions, including the Botanical
Garden of Cienfuegos, the oldest in the country, and the National Botanical Garden of Havana, the
largest in the Cuban territory. In these institutions, as in the rest of the network, large amounts of data
related to the processes that take place in them are generated daily. Of these data, those related to the
management and maintenance of the plants are mainly divided into 3 groups, the first of which is the
introduction record, where the information about the plant is stored when it arrives at the garden, by
whatever means, and is kept there for a period of time after which its destination within the institution
Doctoral Symposium on Natural Language Processing, 26 September 2024, Valladolid, Spain.
$ claudia.aguilarrajme@gmail.com (C. Aguilar-Rajme)
0000-0002-7447-4458 (C. Aguilar-Rajme)
© 2024 Copyright for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
is decided. Those specimens that are selected to become part of the collections are transferred to the
Register of Living Plants, where they are tracked for the rest of their lives in the Garden. The third
main repository of the Botanical Garden is the Herbarium, defined by the RAE as "a collection of dried
and classified plants used as material for the study of botany". [4]. In addition to all this information
generated within the institutions, various bibliographic sources are constantly consulted in search of
information on the identifying characteristics of the plants, both from the physical point of view for
their correct identification in nature, as well as the best practices in the management and conservation
of specimens and the different uses that can be given to each variety.
All this results in a large amount of information, all related to botany and the plant species present
in the garden, but in different formats and very heterogeneous, which makes it difficult to handle by
the specialists of the center and even more difficult for researchers and students who are looking for
references for consultation and research. This is where artificial intelligence and the various techniques
available for handling large amounts of information come into play, since the use of models and
algorithms can facilitate access to the implicit and explicit information contained in these records.
2. Background and related work
Natural Language Processing (NLP) is a branch of artificial intelligence that focuses on the interaction
between computers and human language. In the last decade, NLP techniques have experienced signifi-
cant growth and are being applied in a variety of fields, including botany and plant data management.
These applications include information extraction (IE) from scientific texts, databases, and other relevant
sources to facilitate the integration and analysis of large amounts of data related to plant species. In
this research, we will focus on the task of Named Entity Recognition (NER), as it is a fundamental step
to identify species names, morphological characteristics, geographic locations, and other key concepts
in botanical texts.
There are several techniques for extracting information from texts, one of which is the use of rule-
based models. These models rely on specific dictionaries and linguistic rules to identify entities. For
example, projects such as FloraQuest [5] and Planteome [6] have used dictionaries of botanical terms
to perform NER. These approaches are highly accurate in specific domains, but lack scalability and
flexibility. Another technique used for information extraction is deep learning based models. In recent
years, models such as Recurrent Neural Networks (RNN), especially LSTM (Long Short-Term Memory)
architectures, and transformer-based models such as BERT (Bidirectional Encoder Representations from
Transformers) have demonstrated superior performance in NER tasks. An example of the use of large
language models can be found at [7], where they are being tested for efficiency of different language
models for the identification of single and multi-word names of flowers and plants. The analysis was
done for both Spanish and English, with a significantly smaller dataset in Spanish, but still obtaining
relevant results. Thirteen models were tested for English and four for Spanish, demonstrating in both
cases the superiority of the discriminative models over the generative ones in the task to be evaluated.
For English the model with the best results was BERT-LARGE-CASED and for Spanish BERT-BASE-
MULTILINGUAL-CASED. A similar study [8] was also carried out for the identification of plant names,
but in this case metaphorical names, not textual ones, and the superiority of the discriminative models
over the generative ones was maintained.
Despite significant progress, information extraction in botany presents unique challenges. The
diversity and complexity of the language used in botanical texts, the ambiguity of scientific and common
names, and the scarcity of annotated datasets limit the effectiveness of traditional approaches.
3. Description of the proposed research
The main objective of this research is the identification and application of NLP techniques that contribute
to the extraction of knowledge from different scientific sources available in Spanish in the botanical
field.
A number of specific objectives are planned to achieve the main objective:
1. Determine the state of the art of information extraction in the field of botany.
2. Identify reliable data sources in Spanish in the field of botany.
3. Select NLP techniques for information extraction.
4. Design a knowledge structure where the knowledge will be stored.
5. Apply the identified techniques to the data to extract and store the information.
6. Implement a recommendation system based on the extracted knowledge.
At the end of the thesis, it is expected to have identified and evaluated NLP techniques that allow
obtaining efficient results in the discovery of knowledge in the field of botany in Spanish. This knowledge
will later be used for a better management and conservation of the plant specimens within the collections
of each garden, through a product that allows users to make queries about certain elements of the plants
and provide recommendations for their conservation and maintenance.
4. Methodology
The methodology proposed to achieve the objectives set in the research is based on the fulfillment of
tasks planned throughout the years of the doctoral program.
We will start with a search and reading of the state of the art to know the techniques currently used
to extract information from unstructured texts in the field of botany or similar domains and that can be
applied to it.
Then, since there are data from different sources (described in the next section), it is necessary to
organize, integrate and standardize them to achieve a correct curation process and that the resulting
dataset is as complete as possible and of great value in the field of botany. For this purpose, it will be
necessary to validate the sources with experts in this field of knowledge and, once the sources to be
used have been confirmed, to apply web scraping techniques to obtain the data available online.
The next step would be to process this data to extract and represent the knowledge it contains, for
which we can use Named Entity Recognition (NER) techniques and semantic representation of the data.
We will do this by following the next steps:
1. Text pre-processing: This stage removes unwanted words or characters, performs tokenisation
and text normalisation, such as converting everything to lower case.
2. Grammatical tagging: This step involves tagging each word in the text with its appropriate
grammatical category, using techniques such as POS (part-of-speech) tagging or dependency
analysis. We should also explore ways of working with untagged text, as most of the data sources
we have identified are untagged.
3. Entity identification: In this phase, we search for and identify named entities in the text. We will
do this using machine learning models, rule systems and a combination of both to see which
gives us better results.
4. Entity classification: Once entities have been identified, they are assigned a specific category or
classification. For example, entities can be classified as plant name, life cycle, medicinal use, etc.
5. Feature extraction: Relevant features are extracted from the identified entities for storage, analysis
and processing.
6. Validation and refinement: This is where the results of the analysis of the named entities are
evaluated and adjustments or refinements are made where necessary. This involves verifying the
accuracy and quality of the identified and classified entities.
This will allow us to build a knowledge structure that we can then use to support decision making.
In this way, we will be able to build a recommendation system based on questions and answers that
can interact with users and provide them with information and recommendations on various areas
of botany and agriculture. This can be very useful for the staff of the botanical garden, as well as for
students and researchers, or even for the general public, since it will be a tool to accompany the design,
creation and maintenance of a garden, with a scientific basis on the different plant species, soil types,
seasons of the year and any other knowledge that we are able to integrate into the knowledge base
from the data sources we are working with.
5. Data available
The data initially available came from the institutions of the Network of Botanical Gardens of Cuba,
mostly records of the specimens present in each center, as an inventory or control. There were no records
with textual descriptions of the plants, nor of the norms to be followed for the care and conservation
of the different types of plants, nor of the uses that can be given to them, which is the information
that a system intended to help the user in the process of creating and maintaining gardens would need.
However, this information is available in several online sources that are consulted daily by professionals
and that have scientific validity, which guarantees the quality of the knowledge extracted from them.
The sources identified in Spanish are:
• Revista del Jardín Botánico Nacional (https://revistas.uh.cu/rjbn): aims to disseminate the results
of Cuban and foreign scientific work in the field of botany and mycology, and publishes original
articles and short communications in Spanish and English. It has 28 volumes available online and
is published annually with high international visibility.
• Records of adventitious plants in the province of Alicante [9]: This is a reference handbook with
the necessary information to design ecological gardens with adventitious plants. It contains a
total of 45 plant files with scientific and common names of the species, life cycle, as well as textual
descriptions of their physical characteristics (leaves, stems, flowers, fruits), agronomic needs and
possible uses (Figure 1a).
• Flora Ibérica (http://www.floraiberica.es/): a website that collects definitions and synthesizes
current knowledge about the vascular plants that grow spontaneously in the Iberian Peninsula
and the Balearic Islands. Its aim is to facilitate the identification of the plants, and for this purpose
it has a .pdf file for each species that contains the correct scientific name and its synonyms, a
description that highlights the morphological peculiarities, the habitat in which it can be found,
its geographical distribution in the world, its flowering period, its chromosome number, etc.
• Virtual Herbarium of the Western Mediterranean (http://www.floraiberica.es/): contains informa-
tion and an extensive gallery of images of the vascular plants of the countries of the Western
Mediterranean. It is structured in tabs or pages for each plant species treated, the main reason for
each tab are the images of the plants, but also a brief information in text form about it (which is
why it is a valuable source of data in this research) (Figure 1b).
• Spanish Invasive Alien Species Catalog - Plants (https://www.miteco.gob.es/es/biodiversidad/
temas/conservacion-de-especies/especies-exoticas-invasoras/ce_eei_flora.html): official website
of the Spanish government, where the list of species considered invasive in the territory can be
found, and for each of them can be obtained a .pdf file with textual descriptions of the physical
appearance of the plant, the main distinguishing characteristics compared to other similar plants,
the ecological and health impact and the main routes of entry (Figure 1c).
All of these sources have been reviewed by experts in the field of botany and have been found to be a
reliable source of reference in this area. They are therefore considered suitable for the purposes of the
research and web scraping techniques will be used to extract the information they contain to create the
dataset from which the information extraction will be carried out.
(a) Records of adventitious plants in the province of Ali-
cante
(b) Virtual Herbarium of the Western Mediterranean
(c) Spanish Invasive Alien Species Catalog - Plants
Figure 1: Data Source Examples
6. Specific Issues of Research to be Discussed
In this section, after explaining the proposed research, some questions are posed for discussion:
Q1. Ambiguity in the data: Since we have different sources of data and the same species may
appear in more than one of them, it is possible that we will come across cases where it is necessary to
disambiguate the information for one or more characteristics; in these cases, how should this issue be
approached? would it be wise to choose one of the different options found or to reflect all of them with
the appropriate reference?
Q2. Evaluation: What metrics would be most appropriate to evaluate the results of the information
extraction task?
The conclusions of the debate generated by the questions presented, as well as other aspects that
may emerge, would be of great value for the development of research.
References
[1] P. P. Smith, Y. Harvey-Brown, BGCI Technical Review: Defining the botanic garden, and how to
measure performance and success, volume 2 of kkhui, jhhghhgj ed., Botanic Gardens Conservation
International, hjhbjh, 2017. Uygfyuu.
[2] P. S. W. Jackson, Experimentation on a large scale-an analysis of the holdings and resources of
botanic gardens, Botanic Gardens Conservation News (1999).
[3] W. IUCN-BGCS, The botanic gardens conservation strategy, IUCN-BGCS, WWF Gland, Swit-zerland
(1989).
[4] R. A. ESPAÑOLA, Diccionario de la lengua española, 23.ª ed. URL: https://dle.rae.es.
[5] N. C. B. Garden, Floraquest (????).
[6] L. Cooper, J. Elser, M.-A. Laporte, E. Arnaud, P. Jaiswal, Planteome 2024 update: Reference
ontologies and knowledgebase for plant biology, Nucleic Acids Research 52 (2024). doi:10.1093/
nar/gkad1028.
[7] D. Premasiri, A. Haddad, T. Ranasinghe, R. Mitkov, Deep learning methods for identification of
multiword flower and plant names, Proceedings of the 14th International Conference on Recent
Advances in Natural Language Processing (2023).
[8] T. Ranasinghe, R. Mitkov, A. Haddad, D. Premasiri, Métodos de aprendizaje profundo para la
extracción de nombres metafóricos de flores y plantas, Sociedad Española para el Procesamiento
del Lenguaje Natural, Section: Procesamiento del lenguaje natural (2023).
[9] J. A. Mateu Brotons, Fichas de plantas adventicias de la provincia de Alicante para su uso en el
diseño de jardines ecológicos, Master’s thesis, Universidad Miguel Hernandez de Elche Escuela
Politécnica Superior de Orihuela, 2018.