=Paper=
{{Paper
|id=Vol-3890/paper-46
|storemode=property
|title=Vision for modular taxonomy production at Elsevier: The VOICE project
|pdfUrl=https://ceur-ws.org/Vol-3890/paper-46.pdf
|volume=Vol-3890
}}
==Vision for modular taxonomy production at Elsevier: The VOICE project==
https://ceur-ws.org/Vol-3890/paper-46.pdf
Vision for modular taxonomy production at Elsevier:
The VOICE project
Wytze J. Vlietstra1,∗ , Matthias Albus1 , Nick Drummond2 , Simon Jupp2 and
George Georghiou1
1
Elsevier B.V., Radarweg 29, Amsterdam, Noord-Holland, 1043 NX Netherlands
2
SciBite Limited, BioData Innovation Centre, Wellcome Genome Campus Hinxton, Cambridge CB10 1DR, United
Kingdom
Abstract
Elsevier aims to streamline taxonomy production by creating a shared infrastructure supported by
automation. In this presentation we will explain the components of this infrastructure, which include a
candidate pool for incoming candidate terms. Here candidates are enriched with tools such as a synonym
suggestion classifier, a term categorization classifier, an ambiguity scorer, and a hierarchical relationship
suggestor. In the future, we want to move to a domain-based architecture, in which pre-built branches
for specific scientific domains are maintained, and the taxonomy compiler, which chooses from these
”modules” to create taxonomies for specific products.
Keywords
Taxonomies, Taxonomy production, Taxonomy tooling
1. Background
Taxonomies drive many of Elsevier’s products. They both support searching through our
scientific literature corpora, as well as extracting knowledge from publications and patents
with NLP techniques. Taxonomies group synonymous terms together to represent concepts,
potentially further enriching them with commonly used identifiers such as UniProt identifiers.
Concepts within taxonomies are hierarchically organized, allowing some flexibility of what the
hierarchical relationship represents exactly.
For each product, Elsevier currently develops and maintains a separate taxonomy, supported
by a dedicated team of subject matter experts (SMEs). Up until now, these teams have worked
in a siloed manner, reusing relatively little taxonomy data curated by other teams, and each
developing their own set of tools. As a result, taxonomy production processes were poorly
supported by automation, leading to many tasks being performed manually by SMEs.
To improve taxonomy production and reuse of their contents, the VOICE project (Vision for
Ontological Interoperability & Content Enhancement) project was started. Based on an analysis
of all processes around the production of taxonomies, four objectives were defined: Maintaining
the high quality of our taxonomies, creating a shared taxonomy production pipeline supported
by state-of-the-art automation, improve reuse of existing curated taxonomy data, and ensuring
their FAIR compliance.
SSWAT4HCLS 2024: The 15th International Conference on Semantic Web Applications and Tools for Health Care
and Life Sciences, February 26–29, 2024, Leiden, The Netherlands
*Corresponding author.
w.vlietstra@elsevier.com (W. J. Vlietstra)
0000-0002-1096-8563 (W. J. Vlietstra)
© 2024 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))
CEUR
ceur-ws.org
Workshop ISSN 1613-0073
Proceedings
Figure 1: Schematic overview of our vision for taxonomy production. It consists of three processes:
1) Gathering and enriching candidate terms from all the various in a candidate pool. The output of the
candidate pool are so-called proto-concepts. 2) Placing proto-concepts within a taxonomy for further
editorial processing. 3) Compiling taxonomies based on pre-specified rules to create a product-taxonomy.
Please note that the figure describes processes, which may be supported by the same system.
2. Candidate pool & services
Our initial focus was on the part of the taxonomy production process where we estimated most
could be gained: developing a shared infrastructure and set of services for processing candidate
terms. This infrastructure consists of a so-called candidate pool, shown in Figure 1, which is
a triple store that stores each candidate term supplied to us, along with several services that
enrich these candidate terms. Storing candidate terms and their enrichments in a candidate
pool enables memorization of previous assessments of candidate terms, thereby allowing for
quick comparisons of new candidates with existing data and eliminating the need for their
repeated assessment. The candidate term enrichment services enable normalizing terms to the
lexical variant preferred by specific taxonomies, counting their frequencies in our literature
corpora, categorizing them to different scientific domains to efficiently assign them to the SME
specialized in that domain, and clustering them with their synonymous terms. Additional
services, such as hierarchy suggestion and ambiguity scoring are currently on our roadmap. The
output of the candidate pool are so-called proto-concepts, which are collections of synonymous
terms, which ideally also contain a suggestion on where they should be placed within a specific
taxonomy in the taxonomy management system.
3. Domain-oriented taxonomy architecture
To improve the reuse of existing taxonomy data, we aim to move from a product-oriented
architecture for our taxonomies to a domain oriented one. In a domain-oriented architecture,
each scientific domain would be represented by a single pre-built taxonomy branch. Product
taxonomies would then be able to select their required subset (i.e. concepts and labels) from
these pre-built branches, combining them with selections from other pre-built branches covering
other domains. The result would then be an equivalent product taxonomy as currently is being
produced but eliminating duplicated taxonomy curation efforts. To support different needs
of different product taxonomies, such a domain-based architecture would require a number
of advanced features. For example, to support different granularities of concepts, concepts
would need to be specified at their maximum granularity by default (e.g. different brand
names of drugs are not considered to be synonymous to each other). Coarser granularities of
concepts could then be achieved by “rolling up” child concepts to a pre-defined parent. Other
modifications would include filtering out specific subsets of labels, using different preferred
labels, and automatically adding qualifiers to terms that occur in multiple scientific domains
and will therefore be ambiguous. Many of these features would be supported by what we refer
to as a taxonomy compiler, shown in Figure 1, which would perform these operations based on
pre-specified rules, which sometimes require flags to be assigned to concepts or labels.
4. Outlook
Ultimately, the VOICE project should lead to comprehensive and up to date taxonomies, the
quality of which is guaranteed by Elsevier SMEs, which are produced with such an efficient
process that we can be highly responsive to new use cases or customer requests. Although
there remain to be open questions around e.g. the feasibility of the domain-based architecture
of taxonomy data, we believe our achievements up until now have put us firmly on the path to
reaching these goals.