Track description
for
Challenges track

Four challenges have been selected for The Web Conference 2018:

Knowledge Extraction for the Web of Things (KE4WoT)

The Web of Things (WoT) is an extension of the Internet of Things (IoT) to ease the access to data using the benefits of Web technologies. Data is generated by things/devices and then exploited by more and more web-based applications to monitor healthcare or even control home automation devices. There is a growing interest within standardization in designing models to represent devices and produced data as demonstrated by the following standards. The purpose of this challenge would be to automatically extract the knowledge (e.g. the most common concepts and properties) in already designed and available Knowledge Bases (e.g., datasets and/or models) released on the Web. We will focus on KBs from standards, and/or ontology-based WoT research projects applied to numerous domains. It will demonstrates that the complementary knowledge is constantly redesigned in different communities.

http://wiki.knoesis.org/index.php/KE4WoTChallengeWWW2018


Learning to Recognise Musical Genre from Audio on the Web

Like never before, the web has become a place for sharing creative work among a global community of artists and art lovers. The Free Music Archive (FMA) is an interactive web library of high-quality music, freely and openly shared by artists, and curated by established audio curators. Recently, Defferrard et al. have published a dataset from this library, the FMA dataset, which is a collection of 917 GiB and 343 days of audio and metadata from 106,574 tracks, arranged in a hierarchical taxonomy of 161 genres. The task of this challenge is to recognize the musical genre, e.g. pop and rock, of a piece of music of which only a recording is available on the FMA. Metadata such as the song title or artist name shall not be used for the prediction. The submitted algorithms should learn to map an audio signal, i.e. a time series, to a genre. The training set is composed of 25,000 clips of 30 seconds, categorized in 16 genres. The categorization is unbalanced with 21 to 7,103 clips per genre, but only one genre per track.

https://www.crowdai.org/challenges/www-2018-challenge-learning-to-recognize-musical-genre


Question Answering Mediated by Visual Clues and Knowledge Graphs

This challenge focuses on the use of Web data to support semantic Visual Question Answering: given a large image collection, find a set of images matching natural language queries. The task will support advancing the state-of-the-art in Visual Question Answering by focusing on semantic representation and reasoning mechanisms. In order to address natural language queries, participating systems will need to integrate graph descriptors extracted from images to use structured and unstructured Web data sources.

https://visual-question-answering-challenge.github.io/


Financial Opinion Mining and Question Answering

The growing maturity of Natural Language Processing (NLP) techniques and resources is drastically changing the landscape of many application domains which are dependent on the analysis of unstructured data at scale. The financial domain, with its dependency on the interpretation of multiple unstructured and structured data sources and with its demand for fast and comprehensive decision making is already emerging as a primary ground for the experimentation of NLP, Web Mining and Information Retrieval (IR) techniques. This challenge focuses on advancing the state-of-the-art of aspect-based sentiment analysis and opinion-based Question Answering for the financial domain.

https://sites.google.com/view/fiqa/home