September 2021 - sem-grd

[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] First International Workshop on Joint Use of Probabilistic Graphical Models and Ontology @KGSWC 2021
by Sarra Ben Abbès 20 Sep '21

20 Sep '21

Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Joint Use of Probabilistic Graphical Models and Ontology" which will be held online - November 19-24, 2021. *PGMOnto: * *Joint Use of Probabilistic Graphical Models and Ontology* 1st International Workshop, in conjunction with KGSWC 2021 <https://kgswc.org/> November 19- 24, 2021 - Online https://kgswc.org/pgmonto2021/ *Important dates:* • Workshop paper submission due: *October 01, 2021* • Workshop paper notifications: October 23, 2021 • Workshop paper camera-ready versions due: November 02, 2021 • Workshop: November 19-24, 2021 (half-day) All deadlines are 23:59 anywhere on earth (UTC-12). *Context of the workshop:* An ontology is well known to be the best way to represent knowledge in a domain of discourse. It is defined by Gruber as “an explicit specification of a conceptualization”. It allows 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 resolve many challenging problems related to the information domain (e.g., semantic interoperability among systems, knowledge sharing, and knowledge capitalization). Ontology formalization can be based on First order logic (FOL) to describe concepts, relationships, and constraints, enabling it to make inferences and giving it a graphical representation. 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. Over the last three decades, graphical probabilistic models (PGMs) have enjoyed a surge of interest as a practically feasible framework of expert knowledge encoding and as a new comprehensive data analysis framework. Probabilistic graphical models (PGMs) such as Bayesian network, influence diagram or probabilistic relational model are considered as one of the most successful tools that enable a compact representation of complex systems and the increased ability to effectively learn and perform inference in large networks. Besides the compact representation of probability, PGMs are also intuitively easier for a human to understand than joint probabilities because they highlight the direct dependencies between random variables and their overall semantics is easily captured visually through their graphical representation. In practice, the combination of PGMs and ontologies might be beneficial to have high expressiveness and reasoning possibilities under uncertainty. Despite the difference between these two domain representation models, they have the potential to complement each other: part of the value of ontology baseline knowledge may be used to enhance PGM by resolving challenging tasks: (i) the identification of relevant variables (variable selection), (ii) the determination of structural relationships between the considered variables (arcs), and (iii) the estimation of parameters associated to the model. Once the PGM is learned, its results can be used together with an ontology reasoning engine to perform probabilistic inference. This first regular workshop aims at demonstrating recent and future advances in Semantic Probabilistic Graphical Models and Probabilistic Ontologies. Moreover, this workshop offers an invaluable opportunity to boost collaboration and conversation between Industrial Experts and academic researchers, allowing therefore exchanging ideas and presenting results of on-going research in structured knowledge and causality approaches. *Objective:* We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged. *Topics of interests*: - Construction of probabilistic ontologies - Construction of semantic probabilistic graphical model - Semantic causality and probability - Causality and ontology - PGM for ontology mapping - PGM learning - Ontology for PGM construction - Probabilistic inference engine - Tools, systems and applications - 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: KGSWC2021_authors_kit <http://www.springer.com/%20computer/lncs/lncs+authors?SGWID=0-40209-0-0-0?S…> Papers are to be submitted through the workshop's <https://easychair.org/conferences/?conf=pgmonto2021>*EasyChair <https://easychair.org/conferences/?conf=pgmonto2021>* submission page. We welcome the following types of contributions: - *Full papers* (8-10 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion. Accepted papers are planned to publish with Springer Proceeding (Approval Pending) 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 *KGSWC 2021 <https://kgswc.org/>* page. *Workshop chairs:* Sarra Ben Abbès, Engie, France Ahmed Mabrouk, Engie, France Lynda Temal, Engie, France Philippe Calvez, Engie, France

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[CFP] 1st International Workshop on Deep Learning for Question Answering @ KGSWC 2021
by Sarra Ben Abbès 20 Sep '21

20 Sep '21

Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Deep Learning for Question Answering" which will be held online - November 19-24, 2021. *DLQA: **Deep Learning for Question Answering* 1st International Workshop, in conjunction with KGSWC 2021 <https://kgswc.org/> November 19- 24, 2021 - Online *https://kgswc.org/iwdlq2021/ <https://kgswc.org/iwdlq2021/>* *Important dates:* • Workshop paper submission due: October 01, 2021 • Workshop paper notifications: October 23, 2021 • Workshop paper camera-ready versions due: November 02, 2021 • Workshop: November 19-24, 2021 (half-day) All deadlines are 23:59 anywhere on earth (UTC-12). *Context of the workshop:* Question Answering System (QAS) is an important area in Artificial Intelligence. Generating automatic response is a fastidious and time-consuming task for there exists only some very general approaches to understand the intent of users. Question Answering (QA) is applied in many domain applications such as medical, finance, e-commerce, etc. Given a list of documents, a QAS can provide the right answer to the query pose in natural language. It combines natural language processing (NLP), information retrieval (IR) and knowledge representation and reasoning (KRR) as a relevant component for this process. The general process of QA is composed of different steps: (i) user query, (ii) question analysis (simple or complex query, open or closed domain, linguistic layer, semantic layer, etc), (iii) answer retrieval, and (iv) answer extraction from a set of candidate ones. All these steps are important to answer correctly, precisely and briefly to the user native language question. The answer can refer to a term, a sentence, an image, an audio, a video or to the full textual document. Recent Deep Learning approaches and information retrieval are implemented in order to reason over the questions and its links with the corresponding response. Modern NLP techniques make it possible for computers to read and interpret text, hear and understand speech, measure sentiment, and determine which parts in a document are important. Another important aspect of QAS is the integration of knowledge graphs (KGs) as a new dimension to provide a concise answer issued from the KG. KG is graph-based data model that structure and store real-world entities (abstract concepts) and their relationships (hierarchical and associative) in a graph. The KG is the most suitable and beneficial way to solve many challenging problems related to information domain. *Objective:* The first edition of this workshop aims at highlighting recent and future advances on question answering systems over structured semantic and unstructured textual data and to demonstrate the role of deep learning algorithms to enrich this process. In addition to that, 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 Question Answering approaches. *Topics of interests*: • Question answering over Linked Data • Knowledge Graphs for Question Answering • Complex Question Answering over texts and linked data • Reasoning for Complex Question Answering • Natural Language Processing based question answering • Hybrid text and knowledge graph reasoning • SPARQL query pattern generation • Natural language querying of RDF exposed as Linked Data • Ontology-based query answering • Visual Question Answering • Image question answering • Audio and Speech Question Answering • Video Question Answering • Datasets combining structured and unstructured knowledge • Applications of question answering • 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: KGSWC2021_authors_kit <http://www.springer.com/%20computer/lncs/lncs+authors?SGWID=0-40209-0-0-0?S…> Papers are to be submitted through the workshop's *EasyChair <https://easychair.org/conferences/?conf=iwdlq2021>* submission page. We welcome the following types of contributions: - *Full papers* (9-12 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion. Accepted papers are planned to publish with Springer Proceeding (Approval Pending). 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 *KGSWC 2021 <https://kgswc.org/>* page. *Workshop chairs:*Sarra Ben Abbès, Engie, France Rim Hantach, Engie, France Philippe Calvez, Engie, France

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K-CAP deadline extension - abstracts due 16 September, full papers 21 September
by Marieke van Erp 15 Sep '21

15 Sep '21

*apologies for cross-posting* ¡ Given the high number of requests the deadline is extended to 21 September 2021 but you *MUST* submit an abstract by 16 September so reviewers can be assigned ! K-CAP 2021 The Eleventh International Conference on Knowledge Capture 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* - Abstract submission: September 16, 2021 - Paper submission: September 21, 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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ISWC 2021 - Registration is Open!
by International Semantic Web Conference 13 Sep '21

13 Sep '21

*20th International Semantic Web Conference (ISWC 2021)* Virtual, October 24-28, 2021 https://iswc2021.semanticweb.org *Registration* The Registration for ISWC 2021 is now open! Register with Early registration rate by October 15 at: https://iswc2021.semanticweb.org/attending *Accepted Papers* The accepted papers for the Research, Resource, In-Use, and Industry tracks are listed at: https://iswc2021.semanticweb.org/accepted-papers *Invited Speakers* - Abraham Bernstein, University of Zürich - Yoelle Maarek, Amazon - Julia Stoyanovich, New York University Details about the ISWC 2021 speakers can be found at: https://iswc2021.semanticweb.org/keynote-speakers Follow ISWC on social media: - Twitter: @iswc_conf #iswc_conf (https://twitter.com/iswc_conf) - LinkedIn: https://www.linkedin.com/groups/13612370 - Facebook: https://www.facebook.com/ISWConf The ISWC 2021 Organising Team https://iswc2021.semanticweb.org/organizing-committee

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[CFP] First International Workshop on Joint Use of Probabilistic Graphical Models and Ontology @KGSWC 2021
by Sarra Ben Abbès 13 Sep '21

13 Sep '21

Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Joint Use of Probabilistic Graphical Models and Ontology" which will be held online - November 19-24, 2021. *PGMOnto: * *Joint Use of Probabilistic Graphical Models and Ontology* 1st International Workshop, in conjunction with KGSWC 2021 <https://kgswc.org/> November 19- 24, 2021 - Online https://kgswc.org/pgmonto2021/ *Important dates:* • Workshop paper submission due: *October 01, 2021* • Workshop paper notifications: October 23, 2021 • Workshop paper camera-ready versions due: November 02, 2021 • Workshop: November 19-24, 2021 (half-day) All deadlines are 23:59 anywhere on earth (UTC-12). *Context of the workshop:* An ontology is well known to be the best way to represent knowledge in a domain of discourse. It is defined by Gruber as “an explicit specification of a conceptualization”. It allows 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 resolve many challenging problems related to the information domain (e.g., semantic interoperability among systems, knowledge sharing, and knowledge capitalization). Ontology formalization can be based on First order logic (FOL) to describe concepts, relationships, and constraints, enabling it to make inferences and giving it a graphical representation. 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. Over the last three decades, graphical probabilistic models (PGMs) have enjoyed a surge of interest as a practically feasible framework of expert knowledge encoding and as a new comprehensive data analysis framework. Probabilistic graphical models (PGMs) such as Bayesian network, influence diagram or probabilistic relational model are considered as one of the most successful tools that enable a compact representation of complex systems and the increased ability to effectively learn and perform inference in large networks. Besides the compact representation of probability, PGMs are also intuitively easier for a human to understand than joint probabilities because they highlight the direct dependencies between random variables and their overall semantics is easily captured visually through their graphical representation. In practice, the combination of PGMs and ontologies might be beneficial to have high expressiveness and reasoning possibilities under uncertainty. Despite the difference between these two domain representation models, they have the potential to complement each other: part of the value of ontology baseline knowledge may be used to enhance PGM by resolving challenging tasks: (i) the identification of relevant variables (variable selection), (ii) the determination of structural relationships between the considered variables (arcs), and (iii) the estimation of parameters associated to the model. Once the PGM is learned, its results can be used together with an ontology reasoning engine to perform probabilistic inference. This first regular workshop aims at demonstrating recent and future advances in Semantic Probabilistic Graphical Models and Probabilistic Ontologies. Moreover, this workshop offers an invaluable opportunity to boost collaboration and conversation between Industrial Experts and academic researchers, allowing therefore exchanging ideas and presenting results of on-going research in structured knowledge and causality approaches. *Objective:* We invite submission of papers describing innovative research and applications around the following topics. Papers that introduce new theoretical concepts or methods, help to develop a better understanding of new emerging concepts through extensive experiments, or demonstrate a novel application of these methods to a domain are encouraged. *Topics of interests*: - Construction of probabilistic ontologies - Construction of semantic probabilistic graphical model - Semantic causality and probability - Causality and ontology - PGM for ontology mapping - PGM learning - Ontology for PGM construction - Probabilistic inference engine - Tools, systems and applications - 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: KGSWC2021_authors_kit <http://www.springer.com/%20computer/lncs/lncs+authors?SGWID=0-40209-0-0-0?S…> Papers are to be submitted through the workshop's <https://easychair.org/conferences/?conf=pgmonto2021>*EasyChair* submission page. We welcome the following types of contributions: - *Full papers* (8-10 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion. Accepted papers are planned to publish with Springer Proceeding (Approval Pending) 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 *KGSWC 2021 <https://kgswc.org/>* page. *Workshop chairs:* Sarra Ben Abbès, Engie, France Ahmed Mabrouk, Engie, France Lynda Temal, Engie, France Philippe Calvez, Engie, France

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[CFP] 1st International Workshop on Deep Learning for Question Answering @ KGSWC 2021
by Sarra Ben Abbès 13 Sep '21

13 Sep '21

Dear colleagues and researchers, Please consider submitting a paper for the 1st International workshop on "Deep Learning for Question Answering" which will be held online - November 19-24, 2021. *DLQA: **Deep Learning for Question Answering* 1st International Workshop, in conjunction with KGSWC 2021 <https://kgswc.org/> November 19- 24, 2021 - Online *https://kgswc.org/iwdlq2021/ <https://kgswc.org/iwdlq2021/>* *Important dates:* • Workshop paper submission due: October 01, 2021 • Workshop paper notifications: October 23, 2021 • Workshop paper camera-ready versions due: November 02, 2021 • Workshop: November 19-24, 2021 (half-day) All deadlines are 23:59 anywhere on earth (UTC-12). *Context of the workshop:* Question Answering System (QAS) is an important area in Artificial Intelligence. Generating automatic response is a fastidious and time-consuming task for there exists only some very general approaches to understand the intent of users. Question Answering (QA) is applied in many domain applications such as medical, finance, e-commerce, etc. Given a list of documents, a QAS can provide the right answer to the query pose in natural language. It combines natural language processing (NLP), information retrieval (IR) and knowledge representation and reasoning (KRR) as a relevant component for this process. The general process of QA is composed of different steps: (i) user query, (ii) question analysis (simple or complex query, open or closed domain, linguistic layer, semantic layer, etc), (iii) answer retrieval, and (iv) answer extraction from a set of candidate ones. All these steps are important to answer correctly, precisely and briefly to the user native language question. The answer can refer to a term, a sentence, an image, an audio, a video or to the full textual document. Recent Deep Learning approaches and information retrieval are implemented in order to reason over the questions and its links with the corresponding response. Modern NLP techniques make it possible for computers to read and interpret text, hear and understand speech, measure sentiment, and determine which parts in a document are important. Another important aspect of QAS is the integration of knowledge graphs (KGs) as a new dimension to provide a concise answer issued from the KG. KG is graph-based data model that structure and store real-world entities (abstract concepts) and their relationships (hierarchical and associative) in a graph. The KG is the most suitable and beneficial way to solve many challenging problems related to information domain. *Objective:* The first edition of this workshop aims at highlighting recent and future advances on question answering systems over structured semantic and unstructured textual data and to demonstrate the role of deep learning algorithms to enrich this process. In addition to that, 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 Question Answering approaches. *Topics of interests*: • Question answering over Linked Data • Knowledge Graphs for Question Answering • Complex Question Answering over texts and linked data • Reasoning for Complex Question Answering • Natural Language Processing based question answering • Hybrid text and knowledge graph reasoning • SPARQL query pattern generation • Natural language querying of RDF exposed as Linked Data • Ontology-based query answering • Visual Question Answering • Image question answering • Audio and Speech Question Answering • Video Question Answering • Datasets combining structured and unstructured knowledge • Applications of question answering • 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: KGSWC2021_authors_kit <http://www.springer.com/%20computer/lncs/lncs+authors?SGWID=0-40209-0-0-0?S…> Papers are to be submitted through the workshop's *EasyChair <https://easychair.org/conferences/?conf=iwdlq2021>* submission page. We welcome the following types of contributions: - *Full papers* (9-12 pages): Finished or consolidated R&D works, to be included in one of the Workshop topics. - *Short papers* (6-8 pages): Ongoing works with relevant preliminary results, opened to discussion. Accepted papers are planned to publish with Springer Proceeding (Approval Pending) 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 *KGSWC 2021 <https://kgswc.org/>* page. *Workshop chairs: *Sarra Ben Abbès, Engie, France Rim Hantach, Engie, France Philippe Calvez, Engie, France

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Final CfP: K-CAP - The 11th International Conference on Knowledge Capture - deadline 15 September
by Marieke van Erp 04 Sep '21

04 Sep '21

*apologies for cross-posting* ¡ Also check out the student support opportunities sponsored by SIGAI: https://sigai.acm.org/activities/student_support.html ! K-CAP 2021 The Eleventh International Conference on Knowledge Capture 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 ISWC 2021 - Call for Lightning Talks
by International Semantic Web Conference 02 Sep '21

02 Sep '21

*20th International Semantic Web Conference (ISWC 2021)* Virtual, October 24-28, 2021 https://iswc2021.semanticweb.org Call for Lightning Talks ******************************************* The Lightning Talks continue the Minute Madness tradition at the International Semantic Web Conference that started back in 2011. It has always been an entertaining session that provides conference participants with a quick and fun overview of what is going on in the ISWC community. This session will be opened up to every conference attendee to submit a contribution to the Lightning Talks call. Although we aim to accommodate as many talk proposals as possible, the submissions, in the form of a 1-minute video, will be ranked by the Poster and Demo Program Committee and the best 20 contributions will be selected. More details: https://iswc2021.semanticweb.org/lightning-talks Track Chairs: - Catia Pesquita (LASIGE Faculdade de Ciências Universidade de Lisboa, Portugal) - Oshani Seneviratne (Rensselaer Polytechnic Institute, NY, USA) ***** Important Dates ***** Submissions due: September 30 2021 (Anywhere on Earth) Submission URL: https://bit.ly/iswc21-lightning-talk-submission Follow ISWC on social media: - Twitter: @iswc_conf #iswc_conf (https://twitter.com/iswc_conf) - LinkedIn: https://www.linkedin.com/groups/13612370 - Facebook: https://www.facebook.com/ISWConf The ISWC 2021 Organising Team https://iswc2021.semanticweb.org/organizing-committee

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