KE4WoTChallenge_WWW2018: Knowledge Extraction for the Web of Things (KE4WoT) Challenge, co-located with The Web Conference 2018 (WWW 2018) Centre de Congrès, Lyon Lyon, France, April 23-27, 2018 |
Conference website | http://wiki.knoesis.org/index.php/KE4WoTChallengeWWW2018 |
Submission link | https://easychair.org/conferences/?conf=ke4wotchallengewww2018 |
Abstract registration deadline | January 12, 2018 |
Submission deadline | January 12, 2018 |
Short Description of the KE4WoT Challenge
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 in standardization in designing models to represent devices and produced data as demonstrated by standards (e.g., oneM2M, W3C SSN, W3C WoT, SmartM2M, ETSI M2M). 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 designed by standards, and/or ontology-based WoT research projects already applied to numerous domains. It will demonstrate that the complementary knowledge is constantly redesigned in different communities and would encourage semantic interoperability among WoT projects and KBs.
Full description of the challenge: http://wiki.knoesis.org/index.php/KE4WoTChallengeWWW2018
Important Dates
Challenge Paper Submission: 12 January 2018
Challenge papers acceptance notification: 14 February 2018
Challenge test data published: 14 February 2018
Camera Ready of authors’ papers: Will appear
Challenge – proclamation of winners:: During the conference 23-27 April 2018
Where: Lyon, France
Co-located with The Web Conference 2018 (WWW 2018): https://www2018.thewebconf.org/
Submission Guidelines
Please express your interest in the KE4WoT Challenge and which tasks by filling this form: https://docs.google.com/forms/d/e/1FAIpQLSeXBx7Y1QZ01FCnJbiLqiOpNJroS3cCNdR49XESukKc41S7_Q/viewform?usp=sf_link
Submission Web page on Easy Chair: https://easychair.org/conferences/?conf=ke4wotchallengewww2018
The paper submission will be maximum 6 pages and should follow the ACM format (see WWW 2018 template).
Challenge Task 1: Exploiting the Web of Things Knowledge Base
To open the challenge to a larger audience and complementary web communities, we have designed following tasks, some of them can be isolated:
The LOV4IoT is an ontology catalogue referencing almost 380 WoT research projects in various areas such as home automation, smart cities, smart agriculture, healthcare, etc. More information can be found the LOV4IoT project http://lov4iot.appspot.com/. In the same way, other ontology catalogues can be employed (e.g., Ready4SmartCities, LOV, OpenSensingCity); we are suggesting the LOV4IoT dataset since the organizers can help for any requests during the challenge process. BioPortal is another ontology catalogue dedicated to the healthcare domain.
This task can be split into two subtasks that can be addressed by different communities:
- Task 1.1: Extracting the most popular terms and properties
Definition: Loading a set of WoT-related ontologies and extract the most popular/important terms and properties. An example would be to query all ontologies from the healthcare domain, by analyzing most popular terms, we expect the results would display Body Temperature, Blood Pressure, etc. It would be interesting to have algorithms answering such questions: (1) What are the sensors designed within the ontology (e.g. Body Thermometer)?, (2) What are the logical rules (IF THEN ELSE) designed within the ontology (e.g., if body temperature greater than 38 Degree Celcius than fever)? What is the applicative domain within this ontology (e.g., healthcare) useful when the ontology covers several domains (e.g., Ambient Assisted Living combines smart homes and healthcare domains).
Input: A set of ontologies from LOV4IoT ontology catalogue: health ontologies.
LOV4IoT Tutorial to get health ontologies: http://lov4iot.appspot.com/?p=queryHealthOntologiesWS (using a web service or a dump of ontologies)
Output: For each ontology, finding the most 20 relevant concepts and properties.
Suggestion:
OntoKhoj: a semantic web portal for ontology searching, ranking and classification [Patel et al. 2003]
Identifying potentially important concepts and relations in an ontology [Wu et al. 2008]
Impact: Such algorithms would demonstrate the most relevant concepts and properties in a set of domains. Hopefully, the algorithm will be generic enough to be applied to any domains. Such algorithms could be relevant to assist to create iot.schema.org for instance.
Audience: WoT/IoT and healthcare communities who want to discover and study already designed models, any developers and/or data scientists willing to make statistics, Knowledge Extraction Experts.
- Task 1.2: Ontology Matching algorithms and software.
Definition: Instead of using the OAEI benchmark, ontology matching experts could apply their algorithms on the LOV4IoT benchmark to align WoT-related ontologies.
Input: A set of ontologies from LOV4IoT ontology catalogue: IoT ontologies.
LOV4IoT Tutorial to get IoT ontologies: http://lov4iot.appspot.com/?p=queryIoTOntologiesWS (using a web service or a dump of ontologies)
Output: Aligning ontologies to highlight common concepts and properties
Suggestion:
Focus on the following ontologies: W3C SSN V1, W3C SSN V2, W3C SOSA, M3, M3-lite, FIESTA-IoT, OneM2M, Hypercat, Haystack, IoT-lite, SAREF.
Impact: Ontology matching experts would observe that ontologies referenced are not structured in the same way for instance (perhaps no labels or comments are provided within the ontology which is a huge problem since most of the methods are using this hypothesis). This would lead to the design of new ontology matching tools relevant to WoT.
Audience: Ontology Matching Experts
Challenge Task 2: Creating a System for extracting named entities using ontologies and a Q/A System over it
To open the challenge to a larger audience and complementary web communities, we have designed following tasks, some of them can be isolated:
- Task 2.1: Extracting named entities using ontologies
Definition: Create a system and submit the output results. Our system [9] will compare the results and generate the F-score.
Input: A domain-specific corpus of social media text (e.g., tweets).
Output: Named Entities using Statistical methods and knowledge bases/ontologies.
Impact: LOV4IoT is a valuable resource combining semantic-based projects relevant to IoT. This task motivates the participating team to develop an NLP system over this inter-linked IoT vocabulary in order to identify named entities and linked entities across various semantic related projects. This task will be leveraged by a question answering system for providing better insights to user queries.
Audience: Natural Language Processing Experts
- Task 2.2: Q/A System
Definition: We will use the entity recognition Task: Extracting named entities using ontologies together with relationship extraction task to generate Triples of the data. These triples will be stored in RDF format (linked data). We will create a Q/A system in terms of SPARQL queries. For instance, we will run a bunch of say 20-30 SPARQL queries and generate a result set. The participants will also run the same set 20-30 SPARQL queries over their RDF linked data and generate the results. Now they have to submit their results to ours and we will report the f-score of the task.
Input: Their own result of Task: Extracting named entities using ontologies and queries provided by us.
Output: RDF Triples and the results from the given queries.
Impact: Question Answering (QA) system have been an apogee of research in deep learning and linked open data. This system provides the user an interactional framework showing the expressive potential to query linked open data while keeping the complexity in the back-end. There has been little research in developing a QA system over an IoT linked open data. This is because there is less influence of IoT within semantic web community. LOV4IoT provides a heterogeneous IoT data lake over which the participants have to create QA paradigm for gleaning relevant insights using some state-of-the-art approaches in the domain of QA. This challenge will create an impact on the community of semantic web of things by providing a system that leverages natural language processing and IoT-RDF data. These challenges aim to promote deep innovations in IoT related research leading to improve human's life.
Audience: Natural Language Processing (NLP) Experts, Semantic Web Experts (RDF, RDFS, OWL, SPARQL)
Venue
The conference is colocated with The Web Conference (WWW 2018), 23-24 April 2018, Lyon, France.
Organizers
The challenge co-chairs:
Amelie Gyrard
Homepage: http://sensormeasurement.appspot.com/?p=AmelieGyrard
Institute: Univ Lyon, MINES Saint-Etienne, CNRS, Laboratoire Hubert Curien, Saint-Etienne, France
Amelie Gyrard is a post-doc researcher at Ecole des Mines de Saint-Etienne, France, working within the Connected Intelligence - Knowledge Representation and Reasoning team. Previously, she was a post-doc at Insight Center for Data Analytics, National University of Galway and actively working in the scientific development and coordination of the FIESTA-IoT (Federated Interoperable Semantic IoT/Cloud Testbeds and Applications) EU H2020 project. She already co-organized tutorials, workshops and hackathons on the Semantic Web of Things related topics. Her research interests are on Software engineering for Semantic Web of Things and Internet of Things (IoT), semantic web best practices and methodologies, ontology engineering, reasoning and interoperability of IoT data. She holds a Ph.D. from Eurecom since 2015 where she designed and implemented the Machine-to-Machine Measurement (M3) framework. The title of her dissertation is ``Designing Cross-Domain Semantic Web of Things Applications. Her work is published in conferences, journals and book chapters. She also disseminated her work in standardizations such as ETSI M2M, oneM2M, and W3C Web of Things. She is also a reviewer for IoT, Semantic Web related journals and conferences.
Mihaela Juganaru-Mathieu
Homepage: https://www.linkedin.com/in/mihaela-juganaru-4893635/
Institute: Univ Lyon, MINES Saint-Etienne, CNRS, Laboratoire Hubert Curien, Saint-Etienne, France
Mihaela Juganaru-Mathieu is Associate Professor in computer science and data science at Ecole des Mines de Saint Etienne, France, Department of Computer Science and Intelligent Systems. Her actually research concerns text mining and topic modeling. She participated at various Text Mining challenges at CLEF competitions in author identification (PAN) and mining huge structured text (INEX). She is also responsible of the interdisciplinary Specialization "Big Data".
Manas Gaur
Homepage: http://knoesis.org/people/manas
Institute: Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, USA
Manas Gaur is a Ph.D. student at the Kno.e.sis Ohio Center of Excellence in Knowledge-enabled Computing at Wright State University in Dayton, Ohio, within Kno.e.sis-Knowledge Graph Development and Social and Physical Sensing Enabled Decision Support Team. His research lies at the intersection of deep learning, text mining and knowledge graph to solve challenges in Biomedical and Clinical Natural Language Text. He had been a Data Science for Social Good Fellow at the University of Chicago working on improving healthcare outcomes of the patients using semantics and machine learning. As a researcher in Kno.e.sis, he utilized clinical and biomedical information in UMLS, BKR, and PubMed to create a healthcare knowledge base which can help in classifying musculoskeletal diseases and can be leveraged for analyzing forums and twitter data. He has actively participated in various North America Hackathons such as SteelHacks (University of Pittsburgh), HackDuke (Duke University) and BoilerMake (Purdue). Previously, he completed his graduate studies in Computer Science from Delhi College of Engineering, India. Where he organized and co-organized various hackathons and tutorials on machine learning, data mining, and natural language processing. His work has been published in conferences, book chapters, and journals.
Swati Padhee
Homepage: http://knoesis.org/resources/researchers/swati
Institute: Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, USA
Swati Padhee is a Graduate Research Assistant at Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis). Her current research work involves semantic web, knowledge representations, dynamically evolving knowledge graphs, ontologies, information extraction, natural language processing, and machine learning. The publicly available knowledge bases do not capture the changing dynamics of the events occurring in the real world. They also lack many domain-specific relationships between the entities. Swati is working on solving such issues of capturing the dynamics of domain-specific raw data with time. She is also working on knowledge graph-based similarity measures for enhancing semantic analysis of social data clustering. Prior to joining Kno.e.sis, she holds a Masters in Electrical Engineering from India. Her thesis involved predictive analysis to prevent electrical hazards using background knowledge for power systems. She has organized Robotics competitions and research seminars during her study in India.
Amit Sheth
Homepage: http://knoesis.org/amit/
Institute: Kno.e.sis, Wright State University, USA
Amit Sheth is an educator, researcher, and entrepreneur. He is the LexisNexis Ohio Eminent Scholar, an IEEE Fellow, and the executive director of Kno.e.sis—the Ohio Center of Excellence in Knowledge-enabled Computing. Kno.e.sis' faculty and researchers are computer scientists, cognitive scientists, biomedical researchers, and clinicians. It has the largest US academic research group in the area of Semantic Web and maintains a very high publication impact. Prof. Sheth is one of the 100 top computer sciences based on publication impact (h-index = 95). He has founded three companies by licensing his university research outcomes. He has organized over 75 international events (as General/Organization Committee/Steering Committee/Program Chair) and given over 35 tutorials. Examples of his relevant activities includes (a) initiating and co-chairing W3C Semantic Sensor Networking group, whose outcomes serve as the defacto international standard, and (b) serving as the IoT department editor for IEEE Intelligent Systems.