From Oliver.Kutz at unibz.it Sun Oct 3 21:02:23 2021 From: Oliver.Kutz at unibz.it (Kutz Oliver) Date: Sun, 3 Oct 2021 19:02:23 +0000 Subject: [iaoa-general] Call for papers: Semantic Web Journal Special Issue on "The Role of Ontologies and Knowledge in Explainable AI" Message-ID: *********************************************************************** *Call for papers: Special Issue on The Role of Ontologies and Knowledge in Explainable AI* to be published in the Semantic Web journal, IOS Press. Paper submission: December 10th, 2021 https://sites.google.com/view/special-issue-on-xai-swj *********************************************************************** Explainable AI (XAI) has been identified as a key factor for developing trustworthy AI systems. The reasons for equipping intelligent systems with explanation capabilities are not limited to user rights and acceptance. Explainability is also needed for designers and developers to enhance system robustness and enable diagnostics to prevent bias, unfairness, and discrimination, as well as to increase trust by all users in why and how decisions are made. The interpretability of AI systems has been described already a long time ago since the mid 1980s, but only recently it became an active research focus in the computer science community due to the advances of big data and various regulations of data protection in developing AI systems, such as the GDPR. For example, according to the GDPR, citizens have the legal right to an explanation of decisions made by algorithms that may affect them (e.g., see Article 22). This policy highlights the pressing importance of transparency and interpretability in algorithm design. XAI focuses on developing new approaches for explanations of black-box models by achieving good explainability without sacrificing system performance. One typical approach is the extraction of local and global post-hoc explanations. Other approaches are based on hybrid or neuro-symbolic systems, advocating a tight integration between symbolic and non-symbolic knowledge, e.g., by combining symbolic and statistical methods of reasoning. The construction of hybrid systems is widely seen as one of the grand challenges facing AI today. However, there is no consensus regarding how to achieve this, with proposed techniques in the literature ranging from knowledge extraction and tensor logic to inductive logic programming and other approaches. Knowledge representation---in its many incarnations--- is a key asset to enact hybrid systems, and it can pave the way towards the creation of transparent and human-understandable intelligent systems. This special issue will feature contributions dedicated to the role played by knowledge bases, ontologies, and knowledge graphs in XAI, in particular with regard to building trustworthy and explainable decision support systems. Knowledge representation plays a key role in XAI. Linking explanations to structured knowledge, for instance in the form of ontologies, brings multiple advantages. It does not only enrich explanations (or the elements therein) with semantic information---thus facilitating evaluation and effective knowledge transmission to users---but it also creates a potential for supporting the customisation of the levels of specificity and generality of explanations to specific user profiles or audiences. However, linking explanations, structured knowledge, and sub-symbolic/statistical approaches raise a multitude of technical challenges from the reasoning perspective, both in terms of scalability and in terms of incorporating non-classical reasoning approaches, such as defeasibility, methods from argumentation, or counterfactuals, to name just a few. **Topics of Interest** Topics relevant to this special issue include – but are not limited to – the following: - Cognitive computational systems integrating machine learning and automated reasoning - Knowledge representation and reasoning in machine learning and deep learning - Knowledge extraction and distillation from neural and statistical learning models - Representation and refinement of symbolic knowledge by artificial neural networks - Explanation formats exploiting domain knowledge - Visual exploratory tools of semantic explanations - Knowledge representation for human-centric explanations - Usability and acceptance of knowledge-enhanced semantic explanations - Evaluation of transparency and interpretability of AI Systems - Applications of ontologies for explainability and trustworthiness in specific domains - Factual and counterfactual explanations - Causal thinking, reasoning and modeling - Cognitive science and XAI - Open source software for XAI - XAI applications in finance, medical and health sciences, etc. **Deadline** - Submission deadline: 10th of December 2021. (Papers submitted before the deadline will be reviewed upon receipt). - Acceptance/rejection notification: March 31st, 2022 - Revision due: May 31st, 2022 - Estimated Publication Date: July 2022 **Author Guidelines** Submissions shall be made through the Semantic Web journal website at http://www.semantic-web-journal.net. Prospective authors must take notice of the submission guidelines posted at http://www.semantic-web-journal.net/authors. We welcome four main types of submissions: (i) full research papers, (ii) reports on tools and systems, (iii) application reports, and (iv) survey articles. The description of the submission types is posted at http://www.semantic-web-journal.net/authors#types. While there is no upper limit, paper length must be justified by content. Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "The Role of Ontologies and Knowledge in Explainable AI” special issue. All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process. Also note that the Semantic Web journal is open access. http://www.semantic-web-journal.net/blog/call-papers-special-issue-role-ontologies-and-knowledge-explainable-ai **Guest editors** The guest editors can be reached at ontologies-knowledge-in-xai-swj at googlegroups.com . - Roberto Confalonieri, Free University of Bozen-Bolzano, Faculty of Computer Science, Italy - Oliver Kutz, Free University of Bozen-Bolzano, Faculty of Computer Science, Italy - Diego Calvanese, Department of Computing Science, Umeå University, Sweden and Free University of Bozen-Bolzano, Faculty of Computer Science - Jose M. Alonso, University of Santiago de Compostela, CiTIUS, Spain - Shang-Ming Zhou, University of Plymouth, Faculty of Health, UK -------------- next part -------------- An HTML attachment was scrubbed... URL: From emilio.sanfilippo at cnr.it Thu Oct 7 12:30:23 2021 From: emilio.sanfilippo at cnr.it (Emilio Sanfilippo) Date: Thu, 7 Oct 2021 12:30:23 +0200 Subject: [iaoa-general] [CFP] Semantic Web Journal (IOS Press) Special issue on Semantic Web for Industrial Engineering: Research and Applications Message-ID: ++++++++++++++++++ Call for paper (CFP): Semantic Web Journal (IOS Press) Special issue on Semantic Web for Industrial Engineering: Research and Applications ++++++++++++++++++ Important information: * Submission deadline: February 1, 2022 (extended) * Contact information: semantic-web-for-industrial-engineering-swj at googlegroups.com * Webpage: http://www.semantic-web-journal.net/blog/call-papers-special-issue-semantic-web-industrial-engineering-research-and-applications Guest editors: * Bahar Aameri, University of Toronto * María Poveda-Villalón, Universidad Politécnica de Madrid, Spain * Emilio M. Sanfilippo, ISTC-CNR Laboratory for Applied Ontology, Italy * Walter Terkaj, STIIMA-CNR, Italy ++++++++++++++++++ The Semantic Web is increasingly used for research and applications in industrial engineering covering several tasks related to knowledge-based data management. It is sufficient to recall worldwide efforts related to Industry 4.0 aimed at the development of industrial environments where machines are designed to smoothly interact between themselves and with humans via knowledge models. At the same time, however, practitioners and stakeholders lack methodologies and guidelines to reliably develop, use or integrate (existing) ontologies. In addition, when ontologies are developed within specific application contexts, they are hardly accessible and reusable by third-parties. Recent initiatives like (the European project)OntoCommons , theIndustry Ontologies Foundry (IOF) , and theUK National Digital Twins , among others, attempt to improve this situation, e.g., by collecting methodologies, ontologies, and tools to facilitate the exploitation of the Semantic Web for industrial needs. The purpose of the Special Issue is to collect contributions on the use of Semantic Web languages and technologies within industrial engineering areas such as discrete manufacturing (e.g., aerospace, automotive, machinery, electronics), continuous production (e.g., chemical engineering, oil and gas industry), product design, Architecture, Engineering and Construction (AEC), among others. Topics relevant to this special issue include, but are not limited to, the following: * Research and application challenges for the industrial exploitation of Semantic Web languages and technologies, including Industry 4.0 scenarios. * Semantic Web as an enabler for Digital Twin applications in industry. * Semantic Web frameworks (including frameworks adopting the FAIR principles) for knowledge-based industrial data management, covering tasks such as data modeling, sharing, integration, systems interoperability, reasoning about data and knowledge. * Data analysis workflows supported by the Semantic Web. * Novel technologies for Semantic Web applications in industry. * Bridging methods between Machine Learning and the Semantic Web tuned to industrial engineering. * Real cases of successful/unsuccessful use of the Semantic Web in industrial engineering applications * Ontologies for knowledge representation and reasoning about topics relevant for industrial engineering (e.g., products, processes, manufacturing resources, requirements and capabilities, etc.). * Ontology-based patterns for industrial engineering knowledge representation. * Methodologies, methods, and techniques targeted to industrial contexts supporting the development, modularization, extension, and evolution of ontologies. * Literature review of existing ontologies for industrial engineering, including structured comparisons. * Experiences with the use of top-level ontologies (e.g. BFO, DOLCE, ISO 15926, among others) in industrial engineering. * Experiences with research and application initiatives such as OntoCommons, the Industry Ontologies Foundry (IOF), and the UK National Digital Twins. ++++++++++++++++++++Author Guidelines++++++++++++++++++++ Submissions shall be made through the Semantic Web journal website athttp://www.semantic-web-journal.net . Prospective authors must take notice of the submission guidelines posted athttp://www.semantic-web-journal.net/authors . We welcome four main types of submissions: (i) full research papers, (ii) reports on tools and systems, (iii) application reports, and (iv) survey articles. The description of the submission types is posted athttp://www.semantic-web-journal.net/authors#types . While there is no upper limit, paper length must be justified by content. Extended versions of manuscripts published in conferences and workshops are welcome as long as the previous publications are clearly acknowledged and the new submission introduces substantial revisions and updates. Note that you need to request an account on the website for submitting a paper. Please indicate in the cover letter that it is for the "Semantic Web for Industrial Engineering: Research and Applications" special issue. All manuscripts will be reviewed based on the SWJ open and transparent review policy and will be made available online during the review process. Also note that the Semantic Web journal is open access (http://www.semantic-web-journal.net/blog/open-access-swj-going-gold). Finally please note that submissions must comply with the journal’s Open Science Data requirements, which are detailed in http://www.semantic-web-journal.net/blog/open-science-data-impending-changes-review-process-semantic-web-journal. -------------- next part -------------- An HTML attachment was scrubbed... URL: