KE4WoTChallengeWWW2018

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Knowledge Extraction for the Web of Things (KE4WoT) Challenge: Co-located with The Web Conference 2018 (WWW 2018): [1]

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 generated by things/devices using the benefits of Web technologies. Data is exploited by WoT applications to monitor healthcare or even control home automation devices. The purpose of the Knowledge Extraction for the Web of Things (KE4WoT) challenge is to automatically extract the relevant knowledge from already designed smart WoT applications in various applicative domains. Those applications design and release Knowledge Bases (e.g., datasets and/or models) on the web.

Program and Slides

  • Challenge Date: Friday 27 April 2018 (1:40pm-2:20pm)

Where: Lyon, France

Co-located with The Web Conference 2018 (WWW 2018): https://www2018.thewebconf.org/

  • Slides:

  • Poster (Task 1.1): Toward a Semantic Web of Vehicles:

Semantic Web of Things Project from Poliba, Italy

  • Demo (Task 2.1): Neural Machine Translation Approach for Named Entity Recognition:

Philips Kokoh PRASETYO, School of Information Systems Singapore Management University

Demo here

Flyer: KE4WoT Challenge at WWW 2018

FlyerWWW2018ChallengeKE4WoT.png

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 [2]. 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 the following standards. Those models should be used to design interoperable smart web-based WoT applications:

  • W3C Semantic Sensor Networks (SSN) is the first initiative to address interoperability issues to describe sensor networks through an ontology since devices are required to build WoT applications. A new version of the ontology [3] has been recently released and became a W3C recommendation in October 2017. It is a joint contribution with the Open Geospatial Consortium (OGC) standard, extending and improving the SSN ontology published in 2011.
  • W3C Web of Things (WoT) Interest Group is designing a vocabulary to describe interactions between objects through the Web, a potential implementation is the WoT ontology [4]. At the current date of writing, WoT ontology is not aligned with W3C SSN ontologies. A healthcare scenario has been designed "Remote health monitoring system" [5] among several use cases.
  • OneM2M, an international standard for Machine-to-Machine (M2M) with the development of the OneM2M ontology [6]. It extends the European ETSI M2M standard. At the current date of writing, OneM2M is not aligned with W3C SSN. The MyOntoSens ontology, based on SSN V1 is being standardized as a Technical Specification (TS) within the SmartBAN (Body Area Networks) Technical Committee of the ETSI standardization body [Nachabe et al. 2015]. This ontology is relevant to build health applications based on smart devices.
  • Smart Appliances REFerence (SAREF) [7], is a European standard supported by ETSI M2M and SmartM2M. It mainly covers the smart building applicative domain. The SAREF ontology has been designed re-using SSN and oneM2M according to [Moreira et al. 2017].
  • Schema.org is a well-known schema catalog to structure data on Web pages to describe the location, person, etc. The IoT Schema.org extension [8] is planned; nothing concrete has been developed yet, but discussions are ongoing.
  • Haystack [9] is a project aiming at standardizing semantic data models and web services. For instance, the Haystack Tagging Ontology which employs SSN V1 ontology has been developed [10] [Charpenay et al. 2015].


It would be interesting to have methodologies enabling answering such questions:

  • What are the sensors designed within the models (e.g. Body Thermometer)?
  • What are the logical rules (IF THEN ELSE) designed within the models (e.g., if body temperature greater than 38 Degree Celsius than fever) relevant to interpret sensor data?
  • What is the applicative domain within this model (e.g., healthcare) useful when the ontology covers several domains (e.g., Ambient Assisted Living combines smart homes and healthcare domains).

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 demonstrate that the complementary knowledge is constantly redesigned in different communities [11].

This research challenge could be solved with knowledge extraction technologies. However, most of the existing extraction techniques are frequently applied to text from document and social networks. The main novelty of this challenge would be to apply web-based extraction techniques to models employed to structure data. Indeed, data can be considered as the new oil, what it is still neglected is the reuse of the models used to structure and/or linking data (e.g., Linked Data) to ease the knowledge extraction from data.

In this challenge, we suggest focussing on the healthcare domain with health ontologies to build domain-specific WoT applications and for challenge evaluation purpose. Ideally, the challenge proposal with designed solutions could be applied to any other applicative domains.

Important Dates

Did you miss the deadline or this conference? You can still contact us to stay updated on this topic: fill in the interest form here).

Challenge Paper Submission (Extended Dealine): 10 February 2018

Challenge papers acceptance notification: 14 February 2018

Challenge test data published: 14 February 2018

Camera Ready of authors’ papers: 1 March 2018

Challenge Date: Friday 27 April 2018 (1:40pm-2:20pm)

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 here

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 1.1: Extracting the most popular terms and properties (Challenge Task 1: Exploiting the Web of Things Knowledge Base)

The LOV4IoTontology catalog is referencing almost 400 WoT research projects in various areas such as home automation, smart cities, smart agriculture, healthcare, etc. More information can be found within the LOV4IoT project http://lov4iot.appspot.com/. In the same way, other ontology catalogs 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 catalog dedicated to the healthcare domain.

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 the LOV4IoT ontology catalog (e.g., 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] [12]
 Identifying potentially important concepts and relations in an ontology [Wu et al. 2008] [13]

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.

Evaluation: For evaluation purpose, we will choose some health ontologies referenced within the set of health ontologies mentioned above.

  Criteria 1: What are the sensors/devices described within the ontologies?
  Criteria 2: What are the concepts and or properties which can be linked to other ontologies (e.g., Patient could be linked to foaf:Person)?
 
  Check here our evaluation tables to show the most common concepts within IoT ontologies


Genericity: To test the genericity of your algorithm, you can play with additional datasets:

  LOV4IoT Tutorial to get IoT ontologies: http://lov4iot.appspot.com/?p=queryIoTOntologiesWS (using a web service or a dump of ontologies)
  LOV4IoT Tutorial to get city ontologies: http://lov4iot.appspot.com/?p=queryCityOntologiesWS (using a web service or a dump of ontologies)
  LOV4IoT Tutorial to get Web of Things (WoT) ontologies: http://lov4iot.appspot.com/?p=queryWoTOntologiesWS (using a web service or a dump of ontologies)

Challenge Task 1.2: Ontology Matching algorithms and software (Challenge Task 1: Exploiting the Web of Things Knowledge Base)

Definition: Instead of using the OAEI benchmark, ontology matching experts could apply their software and or algorithms on the LOV4IoT benchmark to align WoT-related ontologies.

Input: A set of ontologies from LOV4IoT ontology catalog: 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, M3, M3-lite, FIESTA-IoT, OneM2M, Hypercat, Haystack, IoT-lite, SAREF, OpenIoT, W3c WoT, Vital.
 
 http://lov4iot.appspot.com/?p=OntologyAlignmentKE4WoTChallengeWWW2018

Evaluation:

 Criteria 1: Do the set of ontologies can be loaded with the ontology matching tool?
 Criteria 2: Do the set of ontologies can be compiled with the ontology matching tool? 
 Criteria 3: Explain the difficulties encountered with the ontologies (e.g., lack of labels, ontology cannot be loaded, ontology does not compile)? 
 Criteria 4: What are the lessons learnt when applying those ontologies with any ontology matching tools? 
 Criteria 5: Do you get better results compared to a specific ontology matching tool (will be announced later)?  
 Check here our suggestions and evaluation tables to align IoT ontologies

Impact: Ontology matching experts would observe that ontologies referenced are not structured in the same way (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 2.1: Named Entity Recognition in Healthcare Unstructured Text

Task Flows
Figure: A Diagrammatic Representation of Two Task Flows

Definition: Named entity recognition (NER) is considered as an important natural language processing task. The identified entities can be used as input for other information enrichment activities like relation extraction, event identification, or question answering. Most of the NER systems developed have targeted structured text in non-healthcare domain. Healthcare text are pretty complex in nature in regards to the context in which the medical entities are used. Healthcare domain is a rich and unexplored area for natural language processing researchers. In this challenge, we propel the idea of NER system development over health-care domain specific unstructured text obtained from Twitter. We categorise the entities as disease entity, a severe form of the disease, trigger entity, procedure/treatment/device identification. We provide the definition of these entity types as follows:

  • Disease Entity: It is the name of the disease that is being explicitly stated in the text. Identification of this entity enables discovery of etiological factors of the disease and its historical information.
  • Severity Entity ( a severe form of disease entity): It is a disease entity that of etiological origin from a relatively mild disease entity.
  • Trigger Entity: It is a disease entity/substance/environmental condition that caused/provoked the disease entity identified in the text. For example, weather, measure cough rate, respiration patterns, heartbeat, temperature and other body data.
  • Location Entity: Words listed under human anatomy are location entity. For instance, bones, muscles, nose, lungs, etc.
  • Procedure/Treatment/Device: These are entities that define a procedure, treatment or device used by the patient or clinician as an act to cure the disease entity stated in the text. For example, an inhaler is a device to cure asthma.
  • Control : It is a dichotomous variable whose value is given “yes” when the tweet talks about disease control, reduction in severity or reduced frequency of asthmatic attacks. This category is created for supporting the question answering task.

Examples:

A. Input Tweet Text: I coughed in front of some stoners at the gas station and a dude goes bro pass that ... I have asthma and pneumonia in lungs.

Output: (The ICD10 codes written in brackets are for references)

  • Disease Entity : asthma(J45 in ICD10) and pneumonia (J851 in ICD10) and cough (R05 in ICD10)
  • Severity Entity (severity of disease) : pneumonia is worsened form of asthma
  • Trigger Entity (Findings/Triggers) : Missing
  • Location Entity : lungs
  • Procedure/Treatment/Devices : Measure, Smart inhaler, Clinical procedure has been stated : Missing
  • Control (binary variable) : yes/no : no

B.Input Tweet Text: Day 3: Anxiety issues cause me to get an asthma attack every time I try to put words on the page. I don't have a bronchodilator.

Output:

  • Disease Entity : asthma(J45 in ICD10) and Anxiety(J419 in ICD10)
  • Severity Entity (severity of disease) : Asthma is associated with Anxiety
  • Trigger Entity (Findings/Triggers) : Anxiety
  • Location Entity : Missing
  • Procedure/Treatment/Devices : Measure, Smart inhaler, Clinical procedure has been stated : bronchodilator
  • Control (binary variable) : yes/no : yes

C. Input Tweet Text: @RiahGsMommy My daughter has asthma too! I'm doing research to see if using a sleep sensor can help. Check out ou https://t.co/32iLMR0ua9

Output:

  • Disease Entity: asthma(J45 in ICD10)
  • Severity Entity (severity of disease)  : Missing
  • Trigger Entity (Findings/Triggers): Missing
  • Location Entity: Missing
  • Procedure/Treatment/Devices : Sleep Sensor
  • Control (binary variable) : (yes/no) : yes

D. Input Tweet Text: Was dying all day of nasal congestion and my asthma but just took some Flonase(made my mom go buy me some) and so far its working

Output:

  • Disease Entity : Asthma and nasal congestion (R0981 in iCD10)
  • Severity Entity (severity of disease)  :
  • Trigger Entity (Findings/Triggers): Missing
  • Location Entity : Nasal
  • Procedure/Treatment/Devices : Flonase
  • Control (binary variable) : (yes/no) : yes

E. Input Tweet Text: "Still picking EKG sensors of my body ! Told you I was sick ! Pneumonia . I know , why didn't i go to the Doctor ? https://t.co/qixp2pO78u"

Output:

  • Disease Entity : Pneumonia
  • Severity Entity (severity of disease) : Missing
  • Trigger Entity (Findings/Triggers): Missing
  • Location Entity : Missing
  • Procedure/Treatment/Devices : EKG Sensors
  • Control (binary variable) : (yes/no) : no


F. Input Tweet Text: Smog caused asthma problem in humans so save yourself from smog

Output:

  • Disease Entity : Asthma
  • Severity Entity (severity of disease)  : Missing
  • Trigger Entity (Findings/Triggers): Smog
  • Location Entity : Missing
  • Procedure/Treatment/Devices : Missing
  • Control (binary variable) : (yes/no) : yes

G. Input Tweet Text: Cair is a smart air quality sensor that monitors the air in your home, ideal for asthma and allergy sufferers https://t.co/vjJ62oC3rN

Output:

  • Disease Entity : asthma, allergy
  • Severity Entity (severity of disease)  : Missing
  • Trigger Entity (Findings/Triggers): Cair
  • Location Entity : Missing
  • Procedure/Treatment/Devices : Air quality sensor
  • Control (binary variable) : (yes/no) : yes

H. Input Tweet Text: Maybe drinking antihistamine with a bronchodilator was a mistakemaybe not

Output:

  • Disease Entity : Missing
  • Severity Entity (severity of disease)  : Missing
  • Trigger Entity (Findings/Triggers): Missing
  • Location Entity: Missing
  • Procedure/Treatment/Devices : antihistamine, bronchodilator
  • Control (binary variable): (yes/no) : no

I. Input Tweet Text: Since taking asthma meds, my Fitbit shows my heartbeat at >100 even during my nap! I feel like I can hear my heart in my head 😑 #amidying

Output:

  • Disease Entity : asthma
  • Severity Entity (severity of disease)  : Missing
  • Trigger Entity (Findings/Triggers): asthma meds
  • Location Entity: heart
  • Procedure/Treatment/Devices : Fitbit
  • Control (binary variable) : (yes/no) : no

Input: A domain-specific corpus of social media text (e.g., tweets).

Output: Named Entities using Statistical methods and knowledge bases/ontologies.

Submittion and Evaluation: Create a system and submit the output results. Our system will evaluate using following metrics: Image: 500 pixels

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 (NLP) Experts

Challenge Task 2.2: Q/A System (Challenge Task 2: Creating a System for extracting named entities using Healthcare Knowledge Sources and a Q/A System over it)

Definition: In this Question Answering task, the participant will be required to provide a response to a natural language question along with the relevant tweet ID and the model that does the answering. In order to complete this task, the participant has to leverage their Named Entity Recognition (NER) module developed in Task 2.1. Furthermore, one can utilize some existing knowledge sources (e.g. SNOMED, Dbpedia, etc.) to enhance the efficiency of their model. As a part of this task, the participant will have access to 25 natural language questions on which they can create their model. The submitted results will be evaluated with the model submitted as a part of this task.

Input for this task: A natural language question and the NER module created by the participant in Task 2.1.

Output for this task: Responses to the natural language question and the system that does the answering.

Impact: Question Answering (Q/A) 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 Q/A 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 Q/A paradigm for gleaning relevant insights using some state-of-the-art approaches in the domain of Q/A. 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).

An Illustrative Example for this Question Answering Task along with a naive procedure

  • Input Question: Does dupilumab control asthma ?
  • Answer: Yes
  • Relevant Tweet: Patients with severely uncontrolled asthma derive the most benefit from dupilumab

Evaluation : Image:200 pixels

Naive Procedure:

  • Tokenization: Split the input into tokens. Since the input is a "Question", one can generate tokens of the question.
  • POS Tagging: Part of speech tagging is one of the fundamental procedure for identifying Nouns, Verbs, Adjectives, Adverb etc. in the sentence. Once they are identified, this procedure tags tokens with POS tags.
  • Regular Expression Parser: After tagging, one can define a regular expression for identifying the required n-gram from the text. For example : {<NNP.?>*<VB.?>*<NN>}
  • After Parsing, one can identify the candidate entities and relations. These ground truth entities and relations can be validated against publicly available knowledge sources (for example SNOMED, Dbpedia, etc.) to transform candidates into proper entities and relations.
  • Once the entities and relations are identified, the tweets exhibiting these entities and relation pairs are extracted. After the extraction of question-related tweets, one can identify the responses to the question. This entire pipeline of Question answering can be converted into a learning model for the healthcare related tweets.

An alternative approach for the completion of this task:

The participant is allowed to store their results in Task 2.1 in an RDF format and can convert the natural language question to a SPARQL form for generating the output of this task. Some helpful pointers for this task:
Quepy (http://quepy.machinalis.com), RDFlib (https://github.com/RDFLib/rdflib).

Instructions for Participants

  • Link to Dataset

Link to the Dataset for the WWW-2018 KE4WoT Challenge Task 2.1 and Task 2.2

  • Link to Annotated Dataset  : [14]

Fetch the dataset :
git clone https://github.com/gyrard/KE4WoT_Challenge_WWW2018

  • Submission Guideline  :
    • Please mail your submission to : ke4wotchallenge.www2018@knoesis.org
    • Format of the submission:
      • Subject : KE4WoTChallenge WWW-2018
      • Body of the Message : Team and Member Names
      • Attachment format : Zipped File with name of the team. For example: <Team-Name>.zip . Inside the file there should be two sub-folder for Task 2.1 and Task 2.2.
  • Please feel free to ask any kind of questions by sending an email to any of the following email-address:
    manas@knoesis.org, amelie@knoesis.org, swati@knoesis.org

Organisation

The challenge co-chairs:


Amelie Gyrard

Homepage: http://wiki.knoesis.org/index.php/AmelieGyrard

Institute: Ohio Center of Excellence in Knowledge-enabled Computing (Kno.e.sis), Wright State University, USA / Univ Lyon, MINES Saint-Etienne, CNRS, Laboratoire Hubert Curien, Saint-Etienne, France

Dr. Amelie Gyrard is post-doc researcher at Kno.e.sis - Ohio Center of Excellence in Knowledge-enabled Computing, Ohio, USA. Previously, she was working at MINES Saint-Etienne, France, working within the Connected Intelligence - Knowledge Representation and Reasoning team. She was also 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 has co-organized tutorials, workshops, and hackathons on the Semantic Web of Things related topics at ISWC 2016, ISWC 2017 and WWW 2017. Her research interests are 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". An entire workflow to semantically annotate heterogeneous IoT data, a necessary step to reason on data to infer high-level abstractions and querying enriched data. This framework hides the complexity of using semantic web technologies to the developers by providing a package with the domain knowledge required to produce the entire workflow. Moreover, it shows the importance to combine heterogeneous applicative domains. She already co-organized tutorials, workshops on the Semantic Web of Things related topics. 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.


Cite this KE4WoT Challenge

Knowledge Extraction for the Web of Things (KE4WoT): WWW 2018 Challenge Summary [Gyrard et al. WWW 2018]

inproceedings{Gyrard:2018:KEW:3184558.3192305,
author = {Gyrard, Amelie and Gaur, Manas and Padhee, Swati and Sheth, Amit and Juganaru-Mathieu, Mihaela},
title = {Knowledge Extraction for the Web of Things (KE4WoT): WWW 2018 Challenge Summary},
booktitle = {Companion Proceedings of the The Web Conference 2018},
series = {WWW '18},
year = {2018},
isbn = {978-1-4503-5640-4},
location = {Lyon, France},
pages = {1935--1936},
numpages = {2},
url = {https://doi.org/10.1145/3184558.3192305},
doi = {10.1145/3184558.3192305},
acmid = {3192305},
publisher = {International World Wide Web Conferences Steering Committee},
address = {Republic and Canton of Geneva, Switzerland},
keywords = {internet of things (iot), knowledge extraction, ontologies, web of things (wot)},
} 


Programme Committee (PC)

List of technical programme committe members already agreed:

  • Raúl García-Castro, Universidad Politécnica de Madrid, Spain
  • Oscar Corcho, Universidad Politécnica de Madrid, Spain
  • María Poveda-Villalón, Universidad Politécnica de Madrid, Spain
  • Kerry Taylor, ANU College of Engineering and Computer Science, Australia
  • Raphaël Troncy, Eurecom, France
  • Xiang Su, University of Oulu, Finland
  • Maria Maleshkova, KIT, Germany
  • Victor Charpenay, Siemens, Germany
  • Luca Costabello, Accenture Labs, Ireland
  • John P. McCrae, Insight Center for Data Analytics, Ireland
  • Achille Zappa, Insight Center for Data Analytics, Ireland
  • Danh Le Phuoc, Technical University of Berlin, Germany
  • Bianca Pereira, Insight Center for Data Analytics, Ireland
  • Payam Barnaghi, University of Surrey, UK
  • Marcin Paprzycki, Systems Research Institute, Polish Academy of Sciences, Poland
  • Soumya Kanti Datta, Eurecom, France
  • Lionel Médini, University of Lyon, France
  • Pankesh Patel, Fraunhofer, USA
  • Parisa Ghodous, University of Lyon 1, France
  • Pavel Shvaiko, Informatica Trentina S.p.A., Italy
  • Pankesh Patel, Fraunhofer, USA
  • Julien Plu, Eurecom, France
  • Ghislain Atemezing, Mondeca, France
  • Cory Henson, Bosh, USA

Acknowledgments

  • This work is partially funded by a bilateral research convention with ENGIE Research & Development and the National French ANR 14-CE24-0029 OpenSensingCity project [15], Hazards SEES NSF Award EAR 1520870, and KHealth NIH 1 R01 HD087132-01.
  • Special thanks to Raphael Troncy for his fruitful feedback to improve the challenge description and tasks, etc.

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