Semantic Web Challenges

SMART: SeMantic AnsweR Type prediction task


Mohnish Dubey
Alfio Gliozzo
Jens Lehmann
Nandana Mihindukulasooriya
Axel-Cyrille Ngonga Ngomo
Muhammad Saleem
Ricardo Usbeck

Question Answering is a popular task in the field of Natural Language Processing and Information Retrieval, in which, the goal is to answer a natural language question (going beyond the document retrieval). Question or answer type classification plays a key role in question answering. The questions can be generally classified based on Wh-terms (Who, What, When, Where, Which, Whom, Whose, Why). Similarly, the answer type classification is determining the type of the expected answer based on the query. Such answer type classifications in literature are performed as a short-text classification task using a set of coarse-grained types, for instance, either 6 or 50 types with TREC QA task. A granular answer type classification is possible with popular Semantic Web ontologies such as DBepdia (~760 classes) and Wikidata (~50K classes). In this challenge, the task is to predict the answer type using a target ontology given a question in natural language.

SemTab: Semantic Web Challenge on Tabular Data to Knowledge Graph Matching


Kavitha Srinivas
Ernesto Jimenez-Ruiz
Oktie Hassanzadeh
Jiaoyan Chen
Vasilis Efthymiou

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap.

Mining the Web of HTML-embedded Product Data


Ziqi Zhang
Christian Bizer
Haiping Lu
Jun Ma
Paul Clough
Anna Primpeli
Ralph Peeters

The Semantic Web Challenge on Mining the Web of HTML-embedded Product Data is co-located with the 19th International Semantic Web Conference. The challenge organises two shared tasks related to product data mining on the Web: (1) product matching and (2) product classification. These are of core importance to the development of product integration services on the Web and product knowledge graph research. 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,

Call For Papers!

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