The document discusses the adaptation of lexicography techniques to evaluate nomenclature usage in biodiversity literature, focusing on the ambiguity between biological taxonomies and scientific nomenclature. It details methods used for corpus analysis, including the application of word sketches and graph transformations to identify hierarchical relations and common name disambiguation. The research aims to enhance understanding of naming conventions and proposes next steps for further evaluation against existing ontologies.

1. USING LEXICOGRAPHY TO
CHARACTERISE RELATIONS
BETWEEN SPECIES MENTIONS
IN THE BIODIVERSITY
LITERATURE
SANDRA YOUNG
UNIVERSITY OF BRIGHTON

2. WHAT DO I MEAN BY SPECIES MENTIONS?

3. BIODIVERSITY
LITERATURE AND
ONTOLOGIES
• Long-standing example of recorded heritage
• Knowledge representation today favours
ontologies
• Suitable approach for biodiversity?

4. BIOLOGICAL TAXONOMIES VS SCIENTIFIC
NOMENCLATURE

5. SCIENTIFIC NOMENCLATURE AMBIGUITY
MELPOMENE

6. PURPOSE OF RESEARCH
• Adapt lexicography techniques to perform an empirical evaluation of
nomenclature use in a specific corpus
• Use results of this to compare with existing ontologies

7. LEXICOGRAPHY
• Corpus-based analysis
• Patterns of language use
in context
• Use to define different
meanings of words
• Fluid and flexible,
multiple meanings the
norm

8. SKETCH ENGINE
– CORPUS
QUERY TOOL

9. WORD SKETCHES
Extraction of
physical
elements from
the earth
Extraction of
data

10. METHODS - CORPUS
• Journal of Ecology of Freshwater Fish
• 3.5 million tokens
• Access through University subscription to Wiley

11. METHODS – ADAPT WORD SKETCHES
CORPUS ANNOTATION
• SCI 1 or 2: Scientific name (as identified through
GNRD an automatic name extraction tool)
• COM: Common species names (perch, trout,
salmon, eel, chub, stickleback, goby, whitefish)
• GENCOLL: general collective terms for species
(insect, species, specie, plant, fish, animal, plant)
• GENPRT: life-stages of species (nymph, parr,
larvae, larva, egg)
SKETCH GRAMMAR ADAPTATION
Original: 1. "(JJ.*|N.*[^Z])" MODIFIER{0,3} NOUN{0,2}
1:NOUN NOT_NOUN
Adapted: 2:SCICOMGEN MODIFIER{0,3} NOUN{0,2}
1:SCICOMGEN NOT_NOUN

12. WORD SKETCH RELATIONS INCLUDED

13. METHODS – TRANSFORMATION INTO GRAPHS

14. RESULTS
• Filtering for frequency
• Filtering for salience

15. FREQUENCY – OVER 20

16. FREQUENCY OVER 10

17. SALIENCE FILTERING
Frequency over 50 Salience over 11

18. CONCLUSIONS
• Hierarchies identified using adapted Word Sketches
• Filtering options differ in relations highlighted
• Disambiguation of common name usage across scientific nomenclature

19. NEXT STEPS
• Identify specific characteristics of graph nodes for evaluation purposes
• Automatic evaluation of this data compared to existing ontologies
• Further research into contrasting qualities and uses of frequency versus
salience filtering
• Test on corpora with known ambiguities relating to nomenclature usage

20. THANK YOU!
ANY QUESTIONS?
Please get in touch:
s.h.young@brighton.ac.uk