Adrien, Bibal, University of Namur, Belgium
Tassadit, Bouadi, University of Rennes I, France
Benoît, Frénay, University of Namur, Belgium
Luis, Galárraga, Inria, France
José, Oramas, University of Antwerp, Belgium
Recent technological advances rely on accurate decision support systems that can be perceived as black boxes due to their overwhelming complexity. This lack of transparency can lead to technical, ethical, legal, and trust issues. For example, if the control module of a self-driving car failed at detecting a pedestrian, it becomes crucial to know why the system erred. In some other cases, the decision system may reflect unacceptable biases that can generate distrust. The General Data Protection Regulation (GDPR), approved by the European Parliament in 2018, suggests that individuals should be able to obtain explanations of the decisions made from their data by automated processing, and to challenge those decisions. All these reasons have given rise to the domain of interpretable AI. AIMLAI (Advances in Interpretable Machine Learning and Artificial Intelligence) aims at gathering researchers, experts and professionals, from inside and outside the domain of AI, interested in the topic of interpretable ML and interpretable AI. The workshop encourages interdisciplinary collaborations, with particular emphasis in knowledge management, infovis, human computer interaction and psychology. It also welcomes applied research for use cases where interpretability matters.
Hyojung Paik PhD, Senior Researcher, Korea Institute of Science and Technology Institute
Mark Stevenson PhD, Senior Lecturer, Dept. of Computer Science, University of Sheffield, UK
Sunyong Yoo PhD, Assistant Professor, School of Electronics and Computer Enineering, Connam National University, Korea
Albert No PhD, Assistant Professor, Dept. of Electronic and Electrical Engineering, Hongik University, Korea
Hojung Nam PhD, Associate Professor, School of Electrical Engineering and Computer Science, Gwangju Institute of Science and Technology, Korea
The focus of this workshop is to bring together researchers who are interested in applying advanced biomedical big-data and text mining techniques to improve sharing, integration, managing and understanding of biomedical information.
Anett Hoppe, Leibniz Information Centre for Science & Technology (TIB), Hannover, Germany
Ran Yu, Leibniz Institute for the Social Sciences (GESIS), Cologne, Germany
Yvonne Kammerer, Leibniz-Institut für Wissensmedien (IWM), Tübingen, Germany / Open Universiteit, Heerlen, The Netherlands
Ladislao Salmerón, University of Valencia, Department of Developmental and Educational Psychology, Research Unit on Reading (ERI Lectura), Valencia, Spain
Web search is one of the most ubiquitous online activities, commonly used for learning purposes, i.e. to acquire or extend one’s knowledge or skills about certain topics or procedures. The importance of learning as an outcome of Web search has also been recognized in research at the intersection of information retrieval, human-computer interaction, psychology, and educational sciences. When learning by searching the Web, individuals are confronted with an unprecedented amount of information of varying quality. Thus, successful learning on the Web requires high degrees of self-regulation and might be supported by an adequate design of search or recommender systems and training tools. Search as Learning (SAL) research examines relationships between querying, navigation, and reading behavior during Web search and the resulting learning outcomes, and how they can be measured, predicted, and supported.
Building on the growing research community of SAL, IWILDS provides an interdisciplinary forum in a full-day workshop that consists of keynotes, presentations of accepted papers, and discussion. The intended audience consists of researchers and practitioners from the general areas of computer science, psychology, information science, and educational science.
Tareq Al-Moslmi, University of Bergen, Norway
Andre Freitas, The University of Manchester, UK
Raphael Troncy, EURECOM, France
Davide Ceolin, Centrum Wiskunde & Informatica, The Netherlands
Abdullatif Abolohom, University of Beira Interior, Portugal
A massive amount of news information is being shared online every day by individuals and media companies. It is difficult for a human to deal with this large-scale data without computational support. Knowledge Graphs (KGs) are emerging as a representation infrastructure to support the organisation, integration and representation of journalistic content. KGs are used in numerous applications such as search, question answering, recommendation systems, data integration and across diverse application domains such as geosciences, healthcare, finance, e-commerce, oil and gas, creative industries and cultural heritage. In recent years, KGs have started to emerge in the journalism domain and it was, for example, the technology being used for processing the Panama papers1. The goal of this workshop is to look at the recent development in the use of KGs in journalism and also to discuss the main challenges to the adaptation of this technology. Moreover, the workshop aims to cover both technological and scientific aspects related to KGs as well as practical deployment and commercial exploitation. Specifically, the workshop will focus on four different aspects: i) journalism KGs generation, enrichment, and evaluation; ii) ontologies & linked open data for journalism; iii) techniques and applications of KGs; iv) and mining journalism KGs. This workshop is an excellent chance to inspire experts and researchers to share theoretical and practical knowledge of the various aspects related to KGs applications for journalism and to help them convert their ideas into the innovations of the future.
Mehwish Alam, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany
Paul Groth, Universiteit van Amsterdam, the Netherlands
Pascal Hitzler, Kansas State University, Manhattan, KS, U.S.A.
Heiko Paulheim, University of Mannheim, Germany
Harald Sack, FIZ Karlsruhe – Leibniz Institute for Information Infrastructure, Germany
Volker Tresp, Ludwig Maximillian University of Munich, Research Scientist Siemens, Germany
There has been a rapid growth in the use of symbolic representations along with their applications in many important tasks. Symbolic representations, in the form of Knowledge Graphs (KGs), constitute large networks of real-world entities and their relationships. On the other hand, sub-symbolic artificial intelligence has also become an important area of research which is inspired by how information is propagated in the (human) brain. These algorithms create an artificial neural network, with nodes (called neurons). Many studies have been proposed which focus on learning distributed representations from KGs. These KGs are generated manually or automatically by processing text or other data sources. These embedding techniques are typically based on translational, factorization, or random walk based methods. Other approaches apply neural network ideas directly onto the graph, like graph convolutional networks. These approaches have been successfully applied to Knowledge Base Completion, Link Prediction, question answering, text classification, etc. In order to pursue more advanced methodologies, it has become critical that these two communities join forces in order to develop more effective algorithms and applications.
Juliana Bowles, School of Computer Science, University of St Andrews, UK
Giovanna Broccia, FMT Lab, ISTI-CNR, Italy
Dr Mirco Nanni, KDD Lab, ISTI-CNR, Italy
DataMod 2020 aims to bring together practitioners and researchers from academia, industry and research institutions interested in the combined application of data-driven techniques from the areas of knowledge management, data mining and machine learning with computational modelling methods. Modelling methodologies of interest include, but are not restricted to, automata, agents, Petri nets, process algebras and rewriting systems. Application domains include social systems, ecology, biology, medicine, smart cities, governance, education, software engineering, and any other eld that deals with complex systems and large amounts of data. Papers can present research results in any of the themes of interest for the symposium as well as application experiences, tools and promising preliminary ideas. Papers dealing with synergistic approaches that integrate knowledge management/discovery and modelling or that exploit knowledge management/discovery to develop/synthesise system models are especially welcome.
Gong Cheng, Nanjing University, China
Kalpa Gunaratna, Samsung Research America, USA
Jun Wang, University College London, UK
The intention of over half of Web queries is to find a particular entity, or entities of a particular type. Entities and structured representations (i.e., knowledge graphs) became popular in the recent past and hence, tremendous interest is in this area at the moment. Beyond the traditional text-based retrieval and learning problems, the recent surge in entity-centered structured data on the Web like Wikidata and progress in deep and machine learning techniques enable more powerful entity-centered solutions, but also bring new challenges. The hybrid exploitation of unstructured and structured data and their use in experiments ranging from traditional machine learning to advanced deep learning techniques have led to diversified involvement of researchers and practitioners in the areas of IR, Database, Semantic Web, and Machine and Deep Learning. There is a demand for a platform where interdisciplinary studies of entity retrieval and learning can be presented, and focused discussions can take place. Therefore, a workshop on entity retrieval and learning is proposed.
3rd Workshop on Knowledge-driven Analytics and Systems Impacting Human Quality of Life (KDAH-CIKM-2020)
Leandro Marin, University of Murcia, Spain
John Farserotu, Centre Suisse d’Electronique et de Microtechnique (CSEM), Switzerland
Antonio Jara, University of Applied Sciences Western Switzerland (HES-SO), Switzerland
Arijit Ukil, Research and Innovation, Tata Consultancy Services, India
Technology disruption through knowledge driven intelligent systems is gradually controlling human life. Management of the knowledge-driven artificial intelligence- based technologies is of highest importance to maximize its progressive influence on human life and human society. Social network affinity, technology-abuse negatively affect our physical, emotional, social and mental health. Conversely, intelligent systems have the capability to bring positive impact on human life. This workshop will bring forward those positive applications and technologies as well as the path towards
transformation of intelligent systems that minimize the negative impacts. The intended thrust is to promote the development of human-centric intelligent technologies like precise and personalized medication and prognosis prediction, improved elderly care, minimizing private data theft, knowledge-driven energy and resource management, deep learning and artificial intelligence-based applications for risk prediction and augmented human capability generation and related others. This workshop aims to bring research outcomes and insights that demonstrate the knowledge-driven technologies, developments, applications for ensuring improvement of human quality of life. The impact would be micro-level, where human life gets impacted in daily basis and at macro-level where human life would be impacted in long term with pronounced influence on the betterment to human society.
Ebrahim Bagheri, Ryerson University, Toronto, Canada
Huan Liu, Arizona State University, Arizona, United States
Kai Shu, Arizona State University, Arizona, United States
Fattane Zarrinkalam, Ryerson University, Toronto, Canada
The MAISoN workshop on Mining Actionable Insights from Social Networks is a yearly event, now approaching its 5th edition. For this edition, we plan to run a special edition of the workshop with focus on dis/misinformation mining from social networks at CIKM 2020, which will take place at Galway, Ireland. We believe this is especially timely and will definitely attract a lot of interest from the community because of the Coronavirus virus (COVID-19) epidemic, misinformation has been spreading over social networks rapidly. The need to consider misinformation on social networks is becoming ever more pertinent and relevant.
The aim of this special edition is to bring together researchers from different disciplines interested in mining dis/misinformation on social networks. In particular, the goal is to discuss research that goes beyond descriptive analysis of social media data or incremental algorithmic improvements on synthetic or existing datasets. Instead, the distinguishing focus of this special edition is its emphasis on techniques that use social network data for building diagnostic, predictive and prescriptive analysis models related to misinformation. This means that there is rigorous attention for techniques that can be used to understand how and why dis/misinformation is created and spread, to uncover hidden and unexpected aspects of dis/misinformation content, and to recommend insightful countermeasures to restrict the circulation of dis/misinformation and alleviate their negative effects.