July 2021 - sem-grd - lists.ogf.org

[CFP] IECARTE 2021
by Editor-in- Chief 31 Jul '23

31 Jul '23

*2021 Conference**CALL FOR PAPERS**(Accepted & Presented Papers will be Published by Peer Reviewed Journals and Submitted for SCI and SCOPUS)* 1st International E–Conference on Applied Research Trends in Engineering* - IECARTE 2021**14-15 December 2021**Website**:- * *http://www.ijces.com/1st-iecarte/* <http://www.ijces.com/1st-iecarte/> Greetings.,hope you are safe and doing well. The objective of this International conference is to provide opportunities for the Researchers, Academicians, Industry persons and students to interact and exchange ideas, experience and expertise in the current trend and strategies for applied research problems in engineering. Besides this, participants will also be enlightened about vast avenues, current and emerging technological developments in the field of engineering in this era and its applications, will be thoroughly explored and discussed. Authors can submit the papers in following related areas - Computer Engineering - Electrical Engineering - Electronics Engineering - Civil Engineering - Structural Engineering - Mathematical Engineering - Mechanical Engineering - Ant interdisciplinary domain in view of above *Publication* : All the papers accepted and presented in the conference is going to be published of peer reviewed journals and submitted for possible consideration in SCOPUS and SCI database. Paper Submission link <https://docs.google.com/forms/d/e/1FAIpQLSeecU4_BKi_5i2qBMlGqvuJV4rDRoQv-J4…> *Important Dates * - *Submission Deadline: 1st August 2021* - *Acceptance Notification: 2nd Oct 2021 (On or before)* - *Camera-Ready version Submission Deadline: 5th November 2021* - *Registration Deadline: 30th November 2021* - *Conference Dates:14-15 December 2021* Note: IECARTE is organised in virtual mode because of COVID prolongs. Send your inquiries through Email to *ijces.editor(a)gmail.com* <ijces.editor(a)gmail.com> *With Best Regards,* *Organising Team IECARTE 2021.*

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[CFP] DEADLINE EXTENSION for 1st International Workshop on Ontology Uses and Contribution to Artificial Intelligence @ KR-2021
by Sarra Ben Abbès 30 Jul '21

30 Jul '21

Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Ontology Uses and Contribution to Artificial Intelligence" which will be held online or in Hanoi, Vietnam - November 6-12, 2021. * **** OnUCAI - CALL FOR PAPERS **** * * Ontology Uses and Contribution to Artificial Intelligence* * 1st International Workshop, in conjunction with *KR 2021 <https://kr2021.kbsg.rwth-aachen.de/> * November 6-12, 2021 - Online or in Hanoi, Vietnam* https://sites.google.com/view/onucai-kr2021 ** Important dates ** - *Workshop paper submission due: **July 19, 2021 **September 06, 2021* - *Workshop paper notifications: *September 30, 2021 - *Workshop paper camera-ready versions due: *October 12, 2021 - *Workshop registration deadline: *TBA - *Workshop: *November 06-12, 2021 All deadlines are 23:59 anywhere on earth (UTC-12) ** Workshop description ** An ontology is well known to be the best way to represent knowledge in a domain of interest. It is defined by Gruber as “an explicit specification of a conceptualization”. It allows us to represent explicitly and formally existing entities, their relationships and their constraints in an application domain. This representation is the most suitable and beneficial way to solve many challenging problems related to the information domain (e.g., knowledge representation, knowledge sharing, knowledge reusing, automated reasoning, knowledge capitalizing and ensuring semantic interoperability among heterogeneous systems). Using ontology has many advantages, among them we can cite ontology reusing, reasoning and explanation, commitment and agreement on a domain of discourse, ontology evolution and mapping, etc. As a field of artificial intelligence (AI), ontology aims at representing knowledge based on declarative and symbolic formalization. Combining this symbolic field with computational fields of IA such as Machine Learning (ML), Deep Learning (DL), Probabilistic Graphical Models (PGMs), Computer Vision (CV) and Natural Languages Processing (NLP) is a promising association. Indeed, ontological modeling plays a vital role to help AI reducing the complexity of the studied domain and organizing information inside it. It broadens AI’s scope allowing it to include any data type as it supports unstructured, semi-structured, or structured data format which enables smoother data integration. The ontology also assists AI for interpretation process, learning, enrichment, prediction, semantic disambiguation and discovering of complex inferences. Finally, the ultimate goal of ontologies is the ability to be integrated in a software to make sense of all information. In the last decade, ontologies are increasingly being used to provide background knowledge for several AI domains in different sectors (e.g. energy, transport, health, banking and insurance, etc.). Some of these AI domains are: - Machine learning and deep learning: semantic data selection, semantic data pre-processing, semantic data transformation, semantic data prediction, semantic clustering correction of the outputs, semantic enrichment with ontological concepts, use the semantic structure for promoting distance measure, etc. - Probabilistic Graphical Models: learning PGM (structure or parameters) using ontologies, probabilistic semantic reasoning, semantic causality and probability, etc. - Computer Vision: semantic image processing, semantic image classification, semantic object recognition/classification, etc. - Blockchain: semantic transactions, interoperable blockchain systems, etc. - Natural Language Processing: semantic text mining, semantic text classification, semantic role labelling, semantic machine translation, semantic question answering, ontology based text summarizing, semantic recommendation systems, etc. - Robotics: semantic task composition, task assignment, communication, cooperation and coordination, etc. - Voice-video-speech: semantic voice recognition, semantic speech annotation, etc. - Game Theory: semantic definition of specific games, semantic rules and goals definition, etc. - etc. ** Objective ** This workshop aims at highlighting recent and future advances on the role of ontologies and knowledge graphs in different domains of AI and how it can be used in order to reduce the semantic gap between the data, applications, machine learning process, etc., in order to obtain a semantic-aware approaches. In addition, the goal of this workshop is to bring together an area for experts from industry, science and academia to exchange ideas and discuss results of on-going research in ontologies and AI approaches. We invite the submission of original works that are related -- but are not limited to -- the topics below. ** Topics of interests ** - Ontology for Machine Learning/Deep Learning - Ontology for Probabilistic Graphical Models - Ontology for Federated Machine Learning - Ontology for Smart Contracts - Ontology for Computer Vision - Ontology for Natural Language Processing - Ontology for Robotics and Multi-agent Systems - Ontology for Voice-video-speech - Ontology for Game Theory - and so on. ** Submission * * The workshop is open to submit unpublished work resulting from research that presents original scientific results, methodological aspects, concepts and approaches. All submissions are not anonymous and must be PDF documents written in English and formatted using the following style files: *KR2021_authors_kit <https://www.google.com/url?q=https%3A%2F%2Fkr2021.kbsg.rwth-aachen.de%2Fdow…>* Papers are to be submitted through the workshop's *EasyChair <https://www.google.com/url?q=https%3A%2F%2Feasychair.org%2F&sa=D&sntz=1&usg…>* submission page. We welcome the following types of contributions: - *Full papers* of up to 9 pages, including abstract, figures and appendices (if any), but excluding references and acknowledgements: Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* of up to 4 pages, excluding references and acknowledgements: Ongoing works with relevant preliminary results, opened to discussion. At least one author of each accepted paper must register for the workshop, in order to present the paper. For further instructions, please refer to the *KR 2021 <https://www.google.com/url?q=https%3A%2F%2Fkr2021.kbsg.rwth-aachen.de%2F&sa…>* page. ** Workshop chairs ** - Sarra Ben Abbès, Engie, France - Lynda Temal, Engie, France - Nada Mimouni, CNAM, France - Ahmed Mabrouk, Engie, France - Philippe Calvez, Engie, France ** Program Committee ** - Shridhar Devamane, Physical Design Engineer, Tecsec Technologies, Bangalore, India - Philippe Leray, Professor at University of Nantes - Stefan Fenz, key researcher at SBA Research and Senior Scientist at Vienna University of Technology <https://informatics.tuwien.ac.at/people/stefan-fenz> - Olivier Dameron, Professor at Université de Rennes I, Dyliss team, Irisa / Inria Rennes-Bretagne Atlantique - Aarón Ayllón Benitez, Phd in bioinformatique, ontology leader at BASF digital solutions - François Scharffe, Researcher on Knowledge based AI, New York, United States - Maxime Lefrançois, Associate Professor at Saint Etienne University, France - Pierre Maret, The QA Company & Saint Etienne University, France - Sanju Tiwari, Universidad Autonoma de Tamaulipas, Mexico **Publication* The best papers from this workshop may be included in the supplementary proceedings of KR 2021.

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CfP K-CAP 2021 - Deadline 15 September 2021
by Marieke van Erp 27 Jul '21

27 Jul '21

*apologies for cross-posting* K-CAP 2021 The Eleventh International Conference on Knowledge December 2 - 3, 2021 A virtual conference https://www.k-cap.org/2021/ *Call for Papers* The massive quantity and increasing availability of information on the Web have catalyzed research in a wide range of fields. Today's data-driven world, however, needs more than an abundance of information, and there is a broadening focus on information quality and accuracy. Given the increasing demand for higher quality, accurate information that can be effectively and unambiguously used, the study of precise and scalable knowledge capture is of crucial importance. Knowledge capture involves the extraction of useful knowledge from multiple, potentially heterogeneous sources, as well as its acquisition directly from human experts. The International Conference on Knowledge Capture, K-CAP, is a forum that brings together members of disparate research communities who are interested in efficiently and precisely capturing knowledge from a variety of heterogeneous sources, and in creating representations that can be useful for automated reasoning, analysis, and other forms of machine processing, as well as to support users in knowledge-intensive collaborative tasks. To discuss and advance the goals of scalable and precise knowledge capture, K-CAP 2021 aims to attract researchers from diverse areas of artificial intelligence, including knowledge representation and reasoning, knowledge acquisition, semantic web, intelligent user interfaces for knowledge acquisition and retrieval, query processing and question answering over heterogeneous knowledge bases, novel evaluation paradigms, problem-solving and reasoning, agents, information extraction from structured or unstructured data, machine learning and representation learning, information enrichment and visualization, as well as researchers interested in cyber-infrastructures to foster the publication, retrieval, reuse, and integration of data. *Topics of interest* - Knowledge acquisition - Knowledge authoring - Knowledge extraction - Knowledge management - Knowledge publication - Knowledge capture from multimodal data, e.g., text, audio, visual - Knowledge capture for the Semantic Web and the Web of Linked Data - Knowledge capture and enrichment in specific domains such as earth and life sciences - Knowledge capture and Interoperability with existing Knowledge Graphs (e.g., Wikidata, DBpedia) - Entity linking and resolution using target Knowledge (e.g. Wikidata, DBpedia) - Collaborative and social approaches to knowledge management and acquisition - Crowdsourcing for knowledge capture and refinement - Handling bias in knowledge capture - Knowledge capture from social environments and contexts - Problem-solving knowledge and methods - Knowledge-based markup techniques - Modeling methodologies for ontologies and knowledge graphs - Narrative intelligence - Knowledge capture through storytelling - Provenance and trust issues in knowledge intensive systems - Services and applications that enable or utilize data capture techniques - Semantic search and query enrichment - Knowledge engineering and knowledge governance - Ontology design patterns - Similarity measurement and analogy-based reasoning - Extracting knowledge graphs from unstructured/semi-structured and multimedia data - Summarization techniques for knowledge graphs - Visualization of knowledge graphs and visual query interfaces - Services and API for knowledge graphs - Addressing scalability issues for distributed knowledge graphs - Automated construction, enrichment and cleaning of knowledge graphs and alignment to existing graphs - Hybrid approaches for knowledge capture combining knowledge engineering and machine learning - Data dynamicity, heterogeneity, and decay in knowledge intensive systems *Submissions* The eleventh International Conference on Knowledge Capture, K-CAP 2021, features a full papers track for research papers, as well as tracks for short papers, for visionary ideas, and for demos. - Full papers, which describe original research, can be up to 8 pages long including references. Full papers will appear in the conference proceedings and will be citable as K-CAP 2021 publications; - Short papers, which may describe (possibly preliminary or open-ended) research, applications (academic, industrial or otherwise), and late breaking results, can be up to 4 pages long, including references. Short papers will also appear in the conference proceedings and will be citable as K-CAP 2021 publications; - Visionary papers, which describe outrageous ideas or potentially preliminary, but highly innovative research, can be up to 2 pages long, including references. Visionary papers will be presented as short talks in the conference program; - Demo papers, which exemplify applications or illustrate work presented in other submissions, can be up to 4 pages long, including references. Demos will be presented as short talks in the conference program. Authors of all paper types will present their work in plenary sessions during the conference. Presentation timing for the different types of papers will be announced once the program is crafted. K-CAP is not a double-blind conference, hence authors should list their names and affiliations on the submission. Please use the ACM 2 column SIG Conference Proceedings template for your submission. Submissions in HTML format are welcome, so long as authors of HTML submissions also provide a conversion of their submission to a PDF file that adheres to the required ACM template for proceedings. Papers submitted to the main conference track should not have been published before in an archival venue, or currently be under review in an archival venue. Authors are welcome to submit papers that have been published as a preprint on arXiv, or in a non-archival venue like a workshop. All submissions to K-CAP should be made through EasyChair: https://easychair.org/conferences/?conf=kcap21 This year we will embrace the FAIR principles by collecting structured metadata of the datasets, software, ontologies and methods generated by K-CAP submissions. Authors will be encouraged to add these resources in the EasyChair submission form (together with a brief description). Accepted papers with resources will be highlighted in the main K-CAP conference page. *Important Dates* - Paper submission: September 15, 2021 - Author notification: October 13, 2021 - Camera ready version due: October 25, 2021 - Conference dates: December 2-3, 2021 All deadlines are midnight AoE (Anywhere on Earth).

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[CFP] DEADLINE EXTENSION for 1st International Workshop on Ontology Uses and Contribution to Artificial Intelligence @ KR-2021
by Sarra Ben Abbès 22 Jul '21

22 Jul '21

Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Ontology Uses and Contribution to Artificial Intelligence" which will be held online or in Hanoi, Vietnam - November 6-12, 2021. * **** OnUCAI - CALL FOR PAPERS ***** * Ontology Uses and Contribution to Artificial Intelligence* * 1st International Workshop, in conjunction with *KR 2021 <https://kr2021.kbsg.rwth-aachen.de/> * November 6-12, 2021 - Online or in Hanoi, Vietnam* https://sites.google.com/view/onucai-kr2021 ** Important dates ** - *Workshop paper submission due: **July 19, 2021 **September 06, 2021* - *Workshop paper notifications: *September 30, 2021 - *Workshop paper camera-ready versions due: *October 12, 2021 - *Workshop registration deadline: *TBA - *Workshop: *November 06-12, 2021 All deadlines are 23:59 anywhere on earth (UTC-12) ** Workshop description ** An ontology is well known to be the best way to represent knowledge in a domain of interest. It is defined by Gruber as “an explicit specification of a conceptualization”. It allows us to represent explicitly and formally existing entities, their relationships and their constraints in an application domain. This representation is the most suitable and beneficial way to solve many challenging problems related to the information domain (e.g., knowledge representation, knowledge sharing, knowledge reusing, automated reasoning, knowledge capitalizing and ensuring semantic interoperability among heterogeneous systems). Using ontology has many advantages, among them we can cite ontology reusing, reasoning and explanation, commitment and agreement on a domain of discourse, ontology evolution and mapping, etc. As a field of artificial intelligence (AI), ontology aims at representing knowledge based on declarative and symbolic formalization. Combining this symbolic field with computational fields of IA such as Machine Learning (ML), Deep Learning (DL), Probabilistic Graphical Models (PGMs), Computer Vision (CV) and Natural Languages Processing (NLP) is a promising association. Indeed, ontological modeling plays a vital role to help AI reducing the complexity of the studied domain and organizing information inside it. It broadens AI’s scope allowing it to include any data type as it supports unstructured, semi-structured, or structured data format which enables smoother data integration. The ontology also assists AI for interpretation process, learning, enrichment, prediction, semantic disambiguation and discovering of complex inferences. Finally, the ultimate goal of ontologies is the ability to be integrated in a software to make sense of all information. In the last decade, ontologies are increasingly being used to provide background knowledge for several AI domains in different sectors (e.g. energy, transport, health, banking and insurance, etc.). Some of these AI domains are: - Machine learning and deep learning: semantic data selection, semantic data pre-processing, semantic data transformation, semantic data prediction, semantic clustering correction of the outputs, semantic enrichment with ontological concepts, use the semantic structure for promoting distance measure, etc. - Probabilistic Graphical Models: learning PGM (structure or parameters) using ontologies, probabilistic semantic reasoning, semantic causality and probability, etc. - Computer Vision: semantic image processing, semantic image classification, semantic object recognition/classification, etc. - Blockchain: semantic transactions, interoperable blockchain systems, etc. - Natural Language Processing: semantic text mining, semantic text classification, semantic role labelling, semantic machine translation, semantic question answering, ontology based text summarizing, semantic recommendation systems, etc. - Robotics: semantic task composition, task assignment, communication, cooperation and coordination, etc. - Voice-video-speech: semantic voice recognition, semantic speech annotation, etc. - Game Theory: semantic definition of specific games, semantic rules and goals definition, etc. - etc. ** Objective ** This workshop aims at highlighting recent and future advances on the role of ontologies and knowledge graphs in different domains of AI and how it can be used in order to reduce the semantic gap between the data, applications, machine learning process, etc., in order to obtain a semantic-aware approaches. In addition, the goal of this workshop is to bring together an area for experts from industry, science and academia to exchange ideas and discuss results of on-going research in ontologies and AI approaches. We invite the submission of original works that are related -- but are not limited to -- the topics below. ** Topics of interests ** - Ontology for Machine Learning/Deep Learning - Ontology for Probabilistic Graphical Models - Ontology for Federated Machine Learning - Ontology for Smart Contracts - Ontology for Computer Vision - Ontology for Natural Language Processing - Ontology for Robotics and Multi-agent Systems - Ontology for Voice-video-speech - Ontology for Game Theory - and so on. ** Submission ** The workshop is open to submit unpublished work resulting from research that presents original scientific results, methodological aspects, concepts and approaches. All submissions are not anonymous and must be PDF documents written in English and formatted using the following style files: *KR2021_authors_kit <https://www.google.com/url?q=https%3A%2F%2Fkr2021.kbsg.rwth-aachen.de%2Fdow…>* Papers are to be submitted through the workshop's *EasyChair <https://www.google.com/url?q=https%3A%2F%2Feasychair.org%2F&sa=D&sntz=1&usg…>* submission page. We welcome the following types of contributions: - *Full papers* of up to 9 pages, including abstract, figures and appendices (if any), but excluding references and acknowledgements: Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* of up to 4 pages, excluding references and acknowledgements: Ongoing works with relevant preliminary results, opened to discussion. At least one author of each accepted paper must register for the workshop, in order to present the paper. For further instructions, please refer to the *KR 2021 <https://www.google.com/url?q=https%3A%2F%2Fkr2021.kbsg.rwth-aachen.de%2F&sa…>* page. ** Workshop chairs ** - Sarra Ben Abbès, Engie, France - Lynda Temal, Engie, France - Nada Mimouni, CNAM, France - Ahmed Mabrouk, Engie, France - Philippe Calvez, Engie, France ** Program Committee ** - Shridhar Devamane, Physical Design Engineer, Tecsec Technologies, Bangalore, India - Philippe Leray, Professor at University of Nantes - Stefan Fenz, key researcher at SBA Research and Senior Scientist at Vienna University of Technology <https://informatics.tuwien.ac.at/people/stefan-fenz> - Olivier Dameron, Professor at Université de Rennes I, Dyliss team, Irisa / Inria Rennes-Bretagne Atlantique - Aarón Ayllón Benitez, Phd in bioinformatique, ontology leader at BASF digital solutions - François Scharffe, Researcher on Knowledge based AI, New York, United States - Maxime Lefrançois, Associate Professor at Saint Etienne University, France - Pierre Maret, The QA Company & Saint Etienne University, France - Sanju Tiwari, Universidad Autonoma de Tamaulipas, Mexico **Publication* The best papers from this workshop may be included in the supplementary proceedings of KR 2021.

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