=Paper=
{{Paper
|id=Vol-3063/om2021_poster4
|storemode=property
|title=Bootstrapping supervised product taxonomy mapping with hierarchical path translations for the regulatory intelligence domain
|pdfUrl=https://ceur-ws.org/Vol-3063/om2021_poster4.pdf
|volume=Vol-3063
|authors=Alfredo Maldonado,Spencer Sharpe,Paul ter Horst
|dblpUrl=https://dblp.org/rec/conf/semweb/MaldonadoSH21
}}
==Bootstrapping supervised product taxonomy mapping with hierarchical path translations for the regulatory intelligence domain==
https://ceur-ws.org/Vol-3063/om2021_poster4.pdf
Bootstrapping Supervised Product Taxonomy Mapping
with Hierarchical Path Translations for the Regulatory
Intelligence Domain
Alfredo Maldonado1 , Spencer Sharpe2, and Paul ter Horst3
1,3 UL, Dublin, Ireland
2 UL, Laramie, WY, United States
{Alfredo.Maldonado, Spencer.Sharpe, Paul.TerHorst}@ul.com
1 Introduction
Regulatory Intelligence (RI) help manufacturers and retailers understand their com-
pliance requirements in the markets they intend to serve. Automatic RI relies on
matching product taxonomies with regulations. However, retailers use an abundance
of different product taxonomies. This work addresses this problem by investigating
automatic entity alignment between arbitrary vendor-specific taxonomies to the GS1’s
Global Product Classification (GPC) 1.
Taxonomy mapping usually takes place at the beginning of the onboarding process.
This means that no historic alignments are available, limiting the applicability of su-
pervised mapping methods, unless we have a reliable seeding method to use in lieu of
historic alignments. We describe such a seeding approach and use it to train a super-
vised neural mapping system. The seeding approach is inspired in neural machine
translation. Crucial in the approach is the hierarchical classification [1–3], where a
hierarchical class is represented as a sequence of node IDs. In the GPC product tax-
onomy, for example, a text label “powered stationary exercise bicycle” has a node ID
of 10005815 – Cycles (Powered), and a full taxonomic classification in the branch
[71000000, 71010000, 71010800, 10005815]. We train a sequence-to-sequence archi-
tecture with attention [4] on examples of this sort, mapping the text label to the se-
quence of node IDs. Training data was acquired from GPC and a small set of product
names (~1,200) manually labelled with brick codes. The resulting model allows us to
predict GPC mappings for a target taxonomy. The only requirement is that both tax-
onomies are expressed in the same human language. However, equivalent labels in
the two taxonomies need not be textually identical due to word embeddings.
In the second stage, the seeding from the seq2seq model is used as training data for
Deep Graph Matching Consensus (DGMC) [5], which learns mappings in two steps:
first, it learns links between two graphs via localised node embeddings. Then, it re-
fines these initial correspondences via neighbourhood consensus.
1 https://www.gs1.org/standards/gpc
Copyright © 2021 for this paper by its authors. Use permitted under Creative Com-
mons License Attribution 4.0 International (CC BY 4.0).
2
2 Evaluation
Our experiments were run on the WDC Product Categorisation Gold Standard 2, which
links the Google Product Taxonomy to GPC. Our system (Seq2Seq-DGMC in Ta-
ble 1) takes the Google Taxonomy and the GPC Taxonomy as input, along with a set
of seed mappings (predicted by Seq2Seq) in order to train the DGMC model. The
trained model outputs suggested mappings between the two taxonomies. Table 1
compares our system with the supervised alternatives:
• Supervised-DGMC50: DGMC system trained on a 50% sample of the Google-
GPC mappings and evaluated on the other 50%.
• Supervised-DGMC10: DGMC system trained on a 10% of the Google-GPC map-
pings and evaluated on the other 90%.
• Seq2Seq: The raw output of our seeding method.
Table 1. Experiment Results on different system configurations using the “Hits at 1” (H@1)
and “Hits at 10” (H@10) metrics commonly used for ranking systems.
System H@1 H@10 System H@1 H@10
Supervised-DGMC50 .47 .78 Seq2Seq .26 N/A
Supervised-DGMC10 .31 .68 Seq2Seq-DGMC .31 .63
The performance of our automatically seeded Seq2Seq-DGMC taxonomy mapping is
comparable to that of the manually seeded Supervised-DGMC10. Depending on the
sizes of the taxonomies to map, this result translates to potentially significant time
savings. As further work, we will evaluate this method with more vendor-specific
product taxonomies. We will also directly measure time saved in manually correcting
automatic mappings. We also wish to explore additional taxonomy matching models.
References
1. Yang, Z., Liu, G.: Hierarchical Sequence-to-Sequence Model for Multi-Label
Text Classification. IEEE Access. 7, 153012–153020 (2019).
2. Umaashankar, V., Shanmugam S., G.: Multi-Label Multi-Class Hierarchical
Classification using Convolutional Seq2Seq. In: KONVENS (2019).
3. Hasson, I., Novgorodov, S., Fuchs, G., Acriche, Y.: Category Recognition in
E-Commerce using Sequence-to-Sequence Hierarchical Classification. In:
ACM International Conference on Web Search and Data Mining (2021).
4. Dzmitry Bahdana, Bahdanau, D., Cho, K., Bengio, Y.: Neural Machine
Translation By Jointly Learning To Align and Translate. In: ICLR (2015).
5. Fey, M., Lenssen, J.E., Morris, C., Masci, J., Kriege, N.M.: Deep Graph
Matching Consensus. In: ICLR (2020).
2 http://webdatacommons.org/structureddata/2014-12/products/gs.html#toc4