=Paper=
{{Paper
|id=Vol-3293/paper48
|storemode=property
|title=Data-driven Update of AGROVOC Using Agricultural Text Corpora
|pdfUrl=https://ceur-ws.org/Vol-3293/paper48.pdf
|volume=Vol-3293
|authors=Hercules Panoutsopoulos,Christopher Brewster
|dblpUrl=https://dblp.org/rec/conf/haicta/PanoutsopoulosB22
}}
==Data-driven Update of AGROVOC Using Agricultural Text Corpora==
https://ceur-ws.org/Vol-3293/paper48.pdf
Data-driven Update of AGROVOC Using Agricultural Text
Corpora
Hercules Panoutsopoulos 1 and Christopher Brewster 1,2
1
Maastricht University, Institute of Data Science, Paul-Henri Spaaklaan 1 (PHS1), Maastricht, 6229 EN, The
Netherlands
2
TNO, Data Science Group, Kampweg 55, Soesterberg, 3769 DE, The Netherlands
Abstract
AGROVOC is a well-known multilingual controlled vocabulary covering the fields of
agriculture, forestry, fisheries, and food. It is used for dataset annotation, indexing of literature,
and automated text tagging, and its effective use depends on its continuous update. Currently,
updates are done manually by a dispersed community of editors. In this paper, we present work
towards automated update recommendations using large corpora of agricultural text (such as
the AGRIS database). The work is based on the extraction of agricultural concept mentions
from text through the deployment of custom trained Named Entity Recognition models and the
exploitation of Graph Neural Networks to recommend concept and relation additions towards
predicting future AGROVOC states. The research questions and methodology are presented
together with the results of an initial experiment. The next steps and future research directions
are outlined. This work forms part of a PhD research on monitoring and predicting changes in
knowledge graphs utilising textual data.
Keywords 1
AGROVOC, knowledge graph, update, Named Entity Recognition, Graph Neural Networks
1. Motivation
AGROVOC is a multilingual, structured vocabulary of more than 40K agricultural concepts, concept
definitions and relations, and concept labels. It is structured as a directed acyclic graph using the SKOS
standard2 and represents associations between concepts by means of hierarchical and non-hierarchical
relations. Utilising semantic web technology standards, AGROVOC provides knowledge organisation
affordances enabling data retrieval. It allows standardised indexing via the unambiguous identification
of resources, thus making search operations more efficient [1]. AGROVOC is curated by FAO experts
in collaboration with editors from affiliated organisations. However, the pace at which new information
and data become available, through the various kinds of publications, poses challenges to keeping it up
to date. Advances in Natural Language Processing and Machine Learning hold the promise of providing
technological support to the manual work involved in AGROVOC’s maintenance and curation. In this
context, the aim of this paper is to present work on the provision of automated recommendations for
AGROVOC updates based on agricultural text corpora (such as the abstracts in the AGRIS database3).
The goal is to identify concepts absent in AGROVOC but present in text to recommend for addition to
an updated vocabulary version. Such recommendations include identifying where in the graph the new
concepts should be added also specifying links to existing concepts. This work will eventually lead to
methods for predicting future AGROVOC states based on the computation of diachronic changes.
Proceedings of HAICTA 2022, September 22–25, 2022, Athens, Greece
EMAIL: herculespanoutsopoulos@gmail.com (A. 1); christopher.brewster@maastrichtuniversity.nl (A. 2)
ORCID: 0000-0002-8060-9750 (A. 1); 0000-0001-6594-9178 (A. 2)
©️ 2022 Copyright for this paper by its authors.
Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
CEUR Workshop Proceedings (CEUR-WS.org)
2
https://www.w3.org/2004/02/skos/
3
https://agris.fao.org/agris-search/index.do
260
2. Background and Related Work
There is a growing body of research on the development of knowledge graphs utilising unstructured
or structured data sources (cf. [2] for a review of literature on automated knowledge graph construction).
However, less research has been undertaken on automated knowledge graph update [3]. A method based
on the combined use of Relational Graph Convolutional Networks (R-GCNs) [4], capturing an entity’s
context in a graph with bi-directional Gated Recurrent Units (bi-GRUs), having the capacity to identify
the context of a word’s appearance in text, is proposed in [5]. In that work, graph update is approached
as a task of adding or deleting relations, assuming fixed sets of entities and relation types, to codify the
information in the text. Fundamentally, research on automated graph update methods has taken the form
of link prediction (e.g., [4, 6, 7, 8]). However, in such a context, important aspects, such as new concept
addition, are overlooked. Apart from that, there is also interest in temporal node and graph embeddings
[9, 10, 11]. Within this context, there has been work in time-aware relational Graph Neural Networks
(GNNs) predicting new relations based on diachronic changes in the graph [12].
3. Description of Proposed Research
AGROVOC provides affordances for annotation of agricultural data, information retrieval, literature
indexing, and automated text tagging [13]. Given the pace at which new information becomes available,
it is important to timely capture domain developments, taking these from food- and agriculture-related
publications, and integrate them into AGROVOC, to ensure an up-to-date knowledge representation
enabling accurate resource identification. AGROVOC has grown over the years following changes in
the domain as shown in Figures 1 and 24.
Figure 1: Number of concepts in AGROVOC per year
The number of concepts in AGROVOC (Figure 1) has increased over time, which is to be expected
given the developments in the fields of food and agriculture. However, changes in the number of relation
types (Figure 2) have not followed a similar pattern, with the observed drops in the recorded numbers
requiring further explanation. To acquire further insights into how AGROVOC is updated, the creation
dates and temporal distribution of concept occurrences in literature (abstracts from the AGRIS database)
were computed for a random sample of concepts from the 2022 AGROVOC version (Table 1).
4
Figures 1 and 2 have been created using data from SPARQL queries submitted to the AGROVOC versions from 2013 and 2022. The queries
are available in the paper’s GitHub repository.
261
Figure 2: Number of relation types in AGROVOC per year
Table 1
Dates of addition of a sample of concepts in AGROVOC and numbers of their occurrence in literature
before and after their addition to AGROVOC
Date of addition Occurrences in literature Occurrences in literature
Concept
to AGROVOC before creation after creation
c_5903 2011 422 119
c_59e0f842 2019 1668 34
c_25740 2011 143 29
c_27140 2011 125 19
c_786c0cff 2019 2843 464
c_33193 1990 0 494
c_62e403a1 2019 209 327
c_41ce07e7 2017 231 307
Despite the small sample size, it is evident that in many cases the number of concept occurrences in
literature before their addition to AGROVOC is greater than the number of their occurrences after being
added to AGROVOC. It can be concluded that the addition of new concepts to AGROVOC is not based
on their frequency of occurrences in literature. This is further supported by the temporal distribution of
new concept additions illustrated in Figure 35. A high peak in the number of concepts added in 2011 is
observed (26,667 concepts) with the average number of concept additions per year being much lower
before 2011 (≅ 66 concepts) and after 2011 (≅ 800 concepts). Based on these findings and considering
the rapid pace of advances in agriculture, we propose that manual updates appear to not be sufficient
for the timely capture and representation of new knowledge.
The proposed PhD research aims to develop, test, and evaluate methods recommending automated
AGROVOC updates based on text. This forms part of a broader effort on the monitoring and predicting
of changes in knowledge graphs utilising textual data. To this end, we have posed the following research
questions:
1. How can we extract agricultural concepts from text, absent in AGROVOC, and identify which
ones to propose as new concepts to be integrated into AGROVOC?
2. Given a new concept to be integrated into AGROVOC what existing relations need also to be
added to link the new concept to existing concepts?
5
The code used to obtain the data shown in Table 1 and Figure 3 is available in the paper’s GitHub repository.
262
Figure 3: Temporal distribution of new concept additions in AGROVOC
4. Research Methodology and Experiments
The research methodology, depicted in Figure 4, has two phases: (i) Extraction of novel agricultural
concepts from text; and (ii) Generation of recommendations for automated AGROVOC updates. Each
phase involves the implementation of an experiment. The experiments are described below.
Figure 4: PhD research methodology
263
Extraction of mentions of novel agricultural concepts from text: The focus is on the development
of an agricultural term extraction tool to identify mentions of novel concepts (not seen in AGROVOC)
in the corpus of texts. Given a version of AGROVOC available at a time point t and a corpus spanning
across a time frame t+Δt, the goal is to identify new concept mentions and recommend them to be added
to the vocabulary. The term extraction tool is based on off-the-shelf Named Entity Recognition (NER)
models. Abstracts of AGRIS publications are used as the tool’s training, validation, and test datasets.
An initial version of the tool was built based on the spaCy library’s Tok2Vec6 and NER7 components,
using their default architectures (spacy.Tok2Vec.v2 and spacy.TransitionBasedParser.v2 respectively)
and the language models shipped with spaCy (en_core_web_sm and en_core_web_lg). Training was
made on a set of 617 AGRIS abstracts annotated manually with labels of agricultural concepts appearing
in them. Table 2 lists the best precision, recall and F1-score achieved in the initial experiment and the
tool configurations giving those results. The results reveal the challenges related to the classification of
a string as an agricultural term, when manually annotating text with agricultural terms, which has a high
degree of vagueness, and hence subjectivity, leaving room for different interpretations by humans and
impacting performance. Optimisation of the term extraction tool based on the use of transformer-based
architectures and agriculture-related vocabularies and ontologies to unambiguously annotate text is
currently in progress.
Table 2
Best precision, recall, and F1-score and configurations of the term extraction tool giving those results
Model configuration
Precision Recall F1-score
(language model - batch size - learning rate)
“en_core_web_lg” - 128 - 0.01 50.73% 47.34% 48.97%
“en_core_web_sm” - 64 - 0.0001 46.08% 54.52% 49.95%
“en_core_web_sm” - 64 - 0.0001 50.70% 52.96% 51.81%
Generation of recommendations for automated AGROVOC updates: This experiment focuses
on the generation of automated updates of AGROVOC drawing upon recommendations for adding new
concepts and relations (from a set of existing relation types) to link the new conceps to concepts already
in AGROVOC. The method will be based on Deep Neural Network-based Natural Language Processing
(DNN-based NLP), capturing the context of agricultural concept mentions in text, and Graph Neural
Networks (GNNs) capable of capturing a concept’s context in the graph, thereby allowing to identify
where in the graph the new concept should be added and how it should be linked to existing concepts.
The available AGROVOC versions will be used as ground truth to evaluate the method’s performance.
5. Discussion
AGROVOC is an agriculture-related graph knowledge representation structure that can be used in
various application scenarios. To facilitate an accurate identification of resources, based on its use, it is
important to keep AGROVOC up to date. However, the rate at which new information and data become
available together with the issues emerging from the AGROVOC’s update methods currently in practice
(appearing not to follow the pace of domain developments as made evident from the relevant literature)
necessitate the adoption of automated update solutions based on means of technological support. In this
context, this paper has presented a PhD research on automated AGROVOC updates based on the
extraction of novel concept mentions from text. Further work is currently in progress related to the
development of the tool for extracting agricultural terms from text towards improving its performance.
To this end, domain ontologies and vocabularies are intended to be used to annotate text automatically
and unambiguously for obtaining the tool’s training, validation, and test datasets. Moreover, drawing
upon transformer-based architectures will help to get better performance results. Future research will
6
https://spacy.io/api/tok2vec
7
https://spacy.io/api/entityrecognizer
264
be concerned with the deployment of time aware GNNs predicting future states of AGROVOC solely
based on the computation of changes that have diachronically occurred in it.
6. Acknowledgements
The authors would like to thank FAO’s support facility for providing previous AGROVOC versions.
This work has been partly supported by the H2020 EUREKA project, contract number 862790.
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