Abstract
In this paper, scientific species names from images of handwritten species observations are automatically recognised and annotated with semantic concepts, so that they can be used for document retrieval and faceted search. Until now, automated semantic annotation of such named entities was only applied to printed or digital text. We employ a two-step approach. First, word images are classified, identifying elements of scientific species names; Genus, species, author, using (i) visual structural features, (ii) position, and (iii) context. Second, the identified species names are semantically annotated according to the NHC-Ontology, an ontology that describes species observations. Internationalised Resource Identifiers (IRIs) are assigned to the elements so that they can be linked and disambiguated at a later stage by individual researchers. For the identification of scientific species names, we achieve an average F1 score of 0.86. Moreover, we discuss how our method will function in a semi-automated annotation process, with a fruitful dialogue between system and user as the main objective.
| Original language | English |
|---|---|
| Title of host publication | Advances in Information Retrieval |
| Subtitle of host publication | 41st European Conference on IR Research, ECIR 2019, Proceedings, Part I |
| Editors | Leif Azzopardi, Benno Stein, Norbert Fuhr, Philipp Mayr, Claudia Hauff, Djoerd Hiemstra |
| Place of Publication | Cham |
| Publisher | Springer |
| Pages | 667-680 |
| Number of pages | 14 |
| ISBN (Electronic) | 978-3-030-15712-8 |
| ISBN (Print) | 978-3-030-15711-1 |
| DOIs | |
| Publication status | Published - 7 Apr 2019 |
| Event | 41st European Conference on Information Retrieval, ECIR 2019 - Cologne, Germany Duration: 14 Apr 2019 → 18 Apr 2019 Conference number: 41 http://ecir2019.org/ |
Publication series
| Name | Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) |
|---|---|
| Volume | 11437 LNCS |
| ISSN (Print) | 0302-9743 |
| ISSN (Electronic) | 1611-3349 |
Conference
| Conference | 41st European Conference on Information Retrieval, ECIR 2019 |
|---|---|
| Abbreviated title | ECIR 2019 |
| Country | Germany |
| City | Cologne |
| Period | 14/04/19 → 18/04/19 |
| Internet address |
Fingerprint
Keywords
- Deep learning
- Historical biodiversity research
- Ontologies
- Scientific names
- Semantic annotation
- Taxonomy
Cite this
}
Automated semantic annotation of species names in handwritten texts. / Stork, Lise; Weber, Andreas; van den Herik, Jaap; Plaat, Aske; Verbeek, Fons; Wolstencroft, Katherine.
Advances in Information Retrieval: 41st European Conference on IR Research, ECIR 2019, Proceedings, Part I. ed. / Leif Azzopardi; Benno Stein; Norbert Fuhr; Philipp Mayr; Claudia Hauff; Djoerd Hiemstra. Cham : Springer, 2019. p. 667-680 (Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics); Vol. 11437 LNCS).Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › Academic › peer-review
TY - GEN
T1 - Automated semantic annotation of species names in handwritten texts
AU - Stork, Lise
AU - Weber, Andreas
AU - van den Herik, Jaap
AU - Plaat, Aske
AU - Verbeek, Fons
AU - Wolstencroft, Katherine
PY - 2019/4/7
Y1 - 2019/4/7
N2 - In this paper, scientific species names from images of handwritten species observations are automatically recognised and annotated with semantic concepts, so that they can be used for document retrieval and faceted search. Until now, automated semantic annotation of such named entities was only applied to printed or digital text. We employ a two-step approach. First, word images are classified, identifying elements of scientific species names; Genus, species, author, using (i) visual structural features, (ii) position, and (iii) context. Second, the identified species names are semantically annotated according to the NHC-Ontology, an ontology that describes species observations. Internationalised Resource Identifiers (IRIs) are assigned to the elements so that they can be linked and disambiguated at a later stage by individual researchers. For the identification of scientific species names, we achieve an average F1 score of 0.86. Moreover, we discuss how our method will function in a semi-automated annotation process, with a fruitful dialogue between system and user as the main objective.
AB - In this paper, scientific species names from images of handwritten species observations are automatically recognised and annotated with semantic concepts, so that they can be used for document retrieval and faceted search. Until now, automated semantic annotation of such named entities was only applied to printed or digital text. We employ a two-step approach. First, word images are classified, identifying elements of scientific species names; Genus, species, author, using (i) visual structural features, (ii) position, and (iii) context. Second, the identified species names are semantically annotated according to the NHC-Ontology, an ontology that describes species observations. Internationalised Resource Identifiers (IRIs) are assigned to the elements so that they can be linked and disambiguated at a later stage by individual researchers. For the identification of scientific species names, we achieve an average F1 score of 0.86. Moreover, we discuss how our method will function in a semi-automated annotation process, with a fruitful dialogue between system and user as the main objective.
KW - Deep learning
KW - Historical biodiversity research
KW - Ontologies
KW - Scientific names
KW - Semantic annotation
KW - Taxonomy
UR - http://www.scopus.com/inward/record.url?scp=85064860066&partnerID=8YFLogxK
UR - https://rdcu.be/bFzrr
U2 - 10.1007/978-3-030-15712-8_43
DO - 10.1007/978-3-030-15712-8_43
M3 - Conference contribution
SN - 978-3-030-15711-1
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 667
EP - 680
BT - Advances in Information Retrieval
A2 - Azzopardi, Leif
A2 - Stein, Benno
A2 - Fuhr, Norbert
A2 - Mayr, Philipp
A2 - Hauff, Claudia
A2 - Hiemstra, Djoerd
PB - Springer
CY - Cham
ER -