Edit Event: ODBASE 2019

Event description:

The conference on Ontologies, DataBases, and Applications of Semantics for Large Scale Information Systems (ODBASE’19) provides a forum on the use of ontologies, rules and data semantics in novel applications. Of particular relevance to ODBASE are papers that bridge traditional boundaries between disciplines such as artificial intelligence and Semantic Web, databases, data science, data analytics and machine learning, human-computer interaction, social networks, distributed and mobile systems, data and information retrieval, knowledge discovery, and computational linguistics. The increasing effectiveness and adoption of data-driven AI technologies into a widening range of applications has thrown a spotlight into the trustworthiness of such AI applications. Such concerns extends beyond those of vendors, deployers and users of an AI, interested in its envelop of performance and reliability, but also wider professional, governmental and civil society stakeholders who are concerned by the potential impact of bias, safety, robustness and liability issues. This concern is amplified by the opaque nature of techniques such as deep machine learning and the ease with which they can be applies over large varieties of datasets and therefore impact is many different areas of life. Key to addressing these concerns therefore is transparency in how data is collected, selected and prepared in training and using AI and how these processes can be governed, not just within organisations, but in concert with external stakeholders. Ontologies and semantic models are powerful techniques for representing, exchanging and controlling the processing of data for AI applications. They can be used to define and monitor integration mappings between data sets that train AI. They can track the provenance and quality assessments of data selected for training AI. They can be use to track and compare the selection of data in different linguistic and societal settings to ensure AI is developed in a fair, unbiased and inclusive manner. They can be used for tracking and explaining the processing of personal data in AI, which is increasingly a requirement for data protection regulation. They can be used to assess the value of datasets and the contributions to that value made by individual stakeholders. They can be use by communities to assemble and share information about applications of AI, to inform approaches to ethical use of AI and thereby establish trust. In all these areas semantic models offer a promising basis for establishing sector-specific or international standards for interoperable data governance in AI applications. ODBASE’19 intends to draw a highly diverse body of researchers and practitioners by being part of the Federated conferences Event "On the Move to Meaningful Internet Systems 2019 (OnTheMove'19)" that co-locates three conferences: ODBASE'19, C&TC'19 (International Symposium on Secure Virtual Infrastructures), and CoopIS'19 (International Conference on Cooperative Information Systems). ODBASE 2019 will consider two categories of papers: research and experience. Research papers must contain novel, unpublished research results. Experience papers must describe performance or usability of existing real-world systems, empirical studies, business / industry cases with (proven) solutions / systems for applied technological challenges, and concrete results demonstrating real-world importance and impact; preference will be given to papers that describe software products or systems that are in use in the community and/or the industry. TOPICS OF INTEREST Specific areas of interest to ODBASE’19 include but are not limited to: 1) Management of Semantically Expressive Information and Knowledge • Data governance • Knowledge representation and semantic knowledge management • Data modeling • Data quality • Data value assessment • Data integration, including transformation rules, ontology matching, merging, etc. • Ontology-based data management, ontology-based data access (OBDA), linked data management, semantic big data management • Ontology and rule engineering and metadata management • Governance aspects such as workflows, roles and responsibilities in semantic information and knowledge management• • Synergy between ontologies & modern databases and business intelligence 2) Large Scale and Complex Information Management and Analysis • Semantic indexing, search, and query answering and formulation in large volumes of data • Semantic digital curation, semantic annotation, extraction, enrichment, summarization, and integration • Semantic (smart/big) data analytics, data mining, data visualization, and machine learning • Semantic social network analysis • Semantic information extraction and text mining • Semantics in event-driven architectures, streaming analytics, and semantic complex event processing 3) Applications, Evaluations, and Experiences of applying ontology, rule, and database techniques, standards, and tools including but not limited to the following domains: • Semantic Web and Linked Data • Corporate Semantic Web (CSW) • Semantic enterprise information systems and knowledge management • Semantic business process management (SBPM) and decision models • Semantic Web and the Internet of Things (IoT) • Semantic Web and Human-Computer Interaction (HCI) • Crowdsourcing, human computation, and the People Web • Pragmatic Web • Semantic services and semantic Multi Agent Systems (MAS) • Online social networks and social Semantic Web • Personalisation and digital content interaction • Hypertext, multimedia, and hypermedia • Semantic story telling and corporate smart content • Ubiquitous and mobile information systems • Information and data governance, information assurance, security, compliance • Semantic cloud computing, edge computing, fog computing • Semantic Web applications and tools for, e.g., biomedical and healthcare domain, eCommerce, eScience, virtual organizations, 4) Industry 4.0 • Legal ontologies, rules, and reasoning • Distributed ledger / blockchain databases, e.g. for rule-based smart contracts • Role of mutual impact of society on/by IT (with focus on ontologies and databases)

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