CfP: Semantic Web Challenge (ISWC2020) - Mining the Web of HTML-embedded Product Data

Dear Colleagues,

 

you are cordially invited to participate in the Semantic Web Challenge:
Mining the Web of HTML-embedded Product Data  collocated with ISWC 2020.
Please also kindly forward the following CfP to your professional network.

 

Call for Participation:

Mining the Web of HTML-embedded Product Data

(co-located with ISWC2020)

 

1. Overview

The Semantic Web Challenge on Mining the Web of HTML-embedded Product Data
is co-located with the 19th International Semantic Web Conference (
<https://iswc2020.semanticweb.org/> https://iswc2020.semanticweb.org/, 2-6
Nov 2020 at Athens, Greece). The challenge organises two shared tasks
related to product data mining on the Web: (1) product matching and (2)
product classification. This event is organised by The University of
Sheffield, The University of Mannheim and Amazon, and is open to anyone.
Systems successfully beating the baseline of the respective task, will be
invited to write a paper describing their method and system and present the
method as a poster (and potentially also a short talk) at the ISWC2020
conference. Winners of each task will be awarded 500 euro as prize (partly
sponsored by Peak Indicators,  <https://www.peakindicators.com/>
https://www.peakindicators.com/).

 

2. Challenge website

For details of the challenge please visit
<https://ir-ischool-uos.github.io/mwpd/>
https://ir-ischool-uos.github.io/mwpd/

 

3. Important dates

02 March 2020: Google support group open. Please join the group at
<https://groups.google.com/forum/#!forum/mwpd2020>
https://groups.google.com/forum/#!forum/mwpd2020 if you wish to take part in
this event

16 March 2020: Release of the training and validation sets

01 June 2020: Release of the test set (without ground truth) 

15 June 2020: Submission of system output 

08 July 2020: Publication of system results and notification of acceptance
for presentation

 

4. Task and dataset brief

The challenge organises two tasks, product matching and product
categorisation.

 

i) Product Matching deals with identifying product offers on different
websites that refer to the same real-world product (e.g., the same iPhone X
model offered using different names/offer titles as well as different
descriptions on various websites). A multi-million product offer corpus
(16M) containing product offer clusters is released for the generation of
training data. A validation set containing 1.1K offer pairs and a test set
of 600 offer pairs will also be released. The goal of this task is to
classify if the offer pairs in these datasets are match (i.e., referring to
the same product) or non-match.

 

ii) Product classification deals with assigning predefined product category
labels (which can be multiple levels) to product instances (e.g., iPhone X
is a 'SmartPhone', and also 'Electronics'). A training dataset containing
10K product offers, a validation set of 3K product offers and a test set of
3K product offers will be released. Each dataset contains product offers
with their metadata (e.g., name, description, URL) and three classification
labels each corresponding to a level in the GS1 Global Product
Classification taxonomy. The goal is to classify these product offers into
the pre-defined category labels. 

 

All datasets are built based on structured data that was extracted from the
Common Crawl ( <https://commoncrawl.org/> https://commoncrawl.org/) by the
Web Data Commons project ( <http://webdatacommons.org/>
http://webdatacommons.org/). 

 

5. Resources and tools

The challenge will also release utility code (in Python) for processing the
above datasets and scoring the system outputs. In addition, the following
language resources for product-related data mining tasks:

.         A text corpus of 150 million product offer descriptions

.         Word embeddings trained on the above corpus

 


6. Organizing committee


.         Dr Ziqi Zhang (Information School, The University of Sheffield)

.         Prof. Christian Bizer (Institute of Computer Science and Business
Informatics, The Mannheim University)

.         Dr Haiping Lu (Department of Computer Science, The University of
Sheffield)

.         Dr Jun Ma (Amazon Inc. Seattle, US)

.         Prof. Paul Clough (Information School, The University of Sheffield
& Peak Indicators)

.         Ms Anna Primpeli (Institute of Computer Science and Business
Informatics, The Mannheim University)

.         Mr Ralph Peeters (Institute of Computer Science and Business
Informatics, The Mannheim University)

.         Mr. Abdulkareem Alqusair (Information School, The University of
Sheffield)

7. Contact

To contact the organising committee please use the Google discussion group
<https://groups.google.com/forum/#!forum/mwpd2020>
https://groups.google.com/forum/#!forum/mwpd2020