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https://ceur-ws.org/Vol-3337/smart-preface.pdf
Preface - SMART 2022
SMART 2022 was the third edition of SMART, the subtasks for Question An-
swering over Knowledge Graph, which is part of the ISWC 2022 Semantic Web
Challenge. It was co-located with the 21st International Semantic Web Con-
ference (ISWC 2022)1 . The first edition SMART2020 [1] was in ISWC 2020
and the second edition SMART2020 [2]. Given a question in natural lan-
guage, the task of the SMART challenge is, to predict the answer type, en-
tities and relations using a target ontology. In the current (third) edition, the
SMART challenge had three tracks i.e. answer type prediction, entity link-
ing and relation prediction. These three tasks are based on two popular KBs,
one using the DBpedia ontology and the other using Wikidata. This volume
contains peer-reviewed system description papers of all the systems that par-
ticipated in the challenge. More details about the challenge can be found at
https://smart-task.github.io/2022/.
Program and Keynote
This year’s edition had two talks and one keynote. First Philipp Christmann
spoke on behalf of his co-authors Rishiraj Saha Roy, Gerhard Weikum about
’Entity and Relation Linking using CLOCQ’ Second the paper ’Contribution
to SMART task 2022: Answer Type Prediction, Relation Linking and Entity
Linking’ by Azanzi Jiomekong, Vadel Tsague, Brice Foko, Uriel Melie, Gaoussou
Camara was presented.
SMART also invited Dennis Diefenbach to give a keynote. He is the CEO
of The QA Company. Dennis holds a Ph.D. from the University of Lyon. He
talked about the ’Challenges of Domain Specific Question Answering Systems’.
We would like to thank all presenters again.
Challenge Description
This challenge is focused on answer type prediction, entity linking and relation
prediction, which play an important role in KGQA (Question Answering over
Knowledge Graphs) systems.
Answer Type Prediction Given a natural language question, the task is
to produce a ranked list of answer types of a given target ontology. Previ-
ous such answer type classifications in literature are performed as a short-text
classification task using a set of coarse-grained types, for instance, either six
types [3, 4, 5, 6] or 50 types [7] with TREC QA task2 . We propose a more
granular answer type classification using popular Semantic Web ontologies such
as DBpedia and Wikidata.
1 https://iswc2022.semanticweb.org/
2 https://trec.nist.gov/data/qamain.html
1
Relation Prediction Given a natural language question, the task is to iden-
tify the relation and link to the relations in KG. Depending on the number of
relations in the KG, the number of relation types to be linked varies.
Entity Linking Given a natural language question, the task is to identify the
entity mentions and link to the entities in KG.
Table 1, Table 2 and Table 3 illustrate some examples. The participating
systems can be either supervised (training data is provided) or unsupervised.
The systems can utilize a wide range of approaches; from rule-based to neural
approaches.
Table 1: Example questions and answer types.
Answer Type
Question
DBpedia Wikidata
Give me all actors starring in dbo:Actor wd:Q33999
movies directed by and star-
ring William Shatner.
Which programming lan- dbo:ProgrammingLanguage wd:Q9143
guages were influenced by
Perl?
Who is the heaviest player of dbo:BasketballPlayer wd:Q3665646
the Chicago Bulls?
How many employees does xsd:integer xsd:integer
Google have?
Table 2: Example questions and relation types.
Relation Type
Question
DBpedia Wikidata
Which languages were in- dbo:influencedBy wdt:P737
fluenced by Perl?
Give me all actors star- dbo:starring, dbo:director wdt:P161, wdt:P57
ring in movies directed
by and starring William
Shatner.
How many employees dbo:numberOfEmployees wdt:P1128
does IBM have?
Organisation
In this section, we list the people who organised and contributed to the success
of this event.
2
Table 3: Example questions and entity linking.
Entity Linking
Question
DBpedia Wikidata
Which languages were influenced by dbr:Perl wd:Q42478
Perl?
Give me all actors starring in movies dbr:William_Shatner wd:Q16297
directed by and starring William
Shatner.
How many employees does IBM dbr:IBM wd:Q37156
have?
Challenge Chairs
• Nandana Mihindukulasooriya (IBM Research AI)
• Mohnish Dubey (Philips Research, Netherlands)
• Alfio Gliozzo (IBM Research AI)
• Jens Lehmann (Amazon)
• Axel-Cyrille Ngonga Ngomo (Universität Paderborn)
• Ricardo Usbeck (University of Hamburg)
• Gaetano Rossiello (IBM Research AI)
• Uttam Kumar (University of Bonn)
• Debayan Banerjee (Universität Hamburg)
Challenge Programme Committee Members
The challenge program committee helped to peer-review the eight system pa-
pers. Each paper received 2 or 3 reviews from the program committee members
and authors took those feedback into account when preparing the camera-ready
versions. The organizers would like to thank them for their valuable time.
• Ahmad Alobaid (Universidad Politécnica de Madrid)
• Debayan Banerjee (Universität Hamburg)
• Mohnish Dubey (Philips Research, Netherlands)
• Longquan Jiang (University of Hamburg)
• Marcos Martinez-Galindo (IBM Research)
• Nandana Mihindukulasooriya (IBM Research AI)
• Cedric Möller (University of Hamburg)
• Ricardo Usbeck (University of Hamburg)
3
Acknowledgements
We would like to thank the ISWC Semantic Web Challenge chairs, Daniele
DellAglio, Catia Pesquita and the whole ISWC organising committee for their
invaluable support to make this event a success. We would also like to thank
the challenge participants for their interest, quality of work, and informative
presentations during the event which made it attractive to the ISWC audience.
References
[1] Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens
Lehmann, Axel-Cyrille Ngonga Ngomo, and Ricardo Usbeck. SeMantic An-
sweR Type prediction task (SMART) at ISWC 2020 Semantic Web Chal-
lenge. CoRR/arXiv, abs/2012.00555, 2020.
[2] Nandana Mihindukulasooriya, Mohnish Dubey, Alfio Gliozzo, Jens
Lehmann, Axel-Cyrille Ngonga Ngomo, Ricardo Usbeck, Gaetano Rossiello,
and Uttam Kumar. Semantic Answer Type and Relation Prediction Task
(SMART 2021). CoRR/arXiv, abs/2112.07606, 2022.
[3] Han Zhao, Zhengdong Lu, and Pascal Poupart. Self-adaptive hierarchical
sentence model. In Twenty-Fourth International Joint Conference on Arti-
ficial Intelligence, 2015.
[4] Chunting Zhou, Chonglin Sun, Zhiyuan Liu, and Francis Lau. A C-LSTM
neural network for text classification. arXiv preprint arXiv:1511.08630, 2015.
[5] Yoon Kim. Convolutional neural networks for sentence classification. In Pro-
ceedings of the 2014 Conference on Empirical Methods in Natural Language
Processing (EMNLP 2014), 2014.
[6] Nal Kalchbrenner, Edward Grefenstette, and Phil Blunsom. A convolutional
neural network for modelling sentences. In Proceedings of the 52nd Annual
Meeting of the Association for Computational Linguistics (Volume 1: Long
Papers), pages 655–665, Baltimore, Maryland, June 2014. Association for
Computational Linguistics.
[7] Xin Li and Dan Roth. Learning Question Classifiers: the Role of Semantic
Information. Natural Language Engineering, 12(3):229–249, 2006.
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