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Keynote Lectures

IC3K is a joint conference composed of three concurrent conferences: KDIR, KEOD and KMIS. These three conferences are always co-located and held in parallel. Keynote lectures are plenary sessions and can be attended by all IC3K participants.

Enterprise Ontologies - Emerging Issues from the Internet of Things and Social Media
Daniel O'Leary, University of Southern California, United States

Extracting Semantically Enriched Events from the Literature
Sophia Ananiadou, University of Manchester, United Kingdom

Parallel Coordinates: Visual Multidimensional Geometry and Its Applications
Alfred Inselberg, Tel Aviv University, Israel

Challenges in the Core of Ontology Support Systems
Peter F. Patel-Schneider, Nuance, United States

Internet of Things – Towards New Frontiers of Knowledge Management
Florian Michahelles, ETH Zürich, Switzerland

 

Enterprise Ontologies - Emerging Issues from the Internet of Things and Social Media

Daniel O'Leary
University of Southern California
United States
 

Brief Bio
Daniel O'Leary is a Professor at the University of Southern California, focusing on emerging technologies (e.g., Social Media and the Internet of Things), artificial intelligence, enterprise resource planning systems and knowledge management systems. Dan received his Ph. D. from Case Western Reserve University and his master’s degree from the University of Michigan. He is the former editor of IEEE Intelligent Systems and current editor of John Wiley's Intelligent Systems in Accounting, Finance and Management. His book, Enterprise Resource Planning Systems, published by Cambridge University Press, has been translated into both Chinese and Russian. Dan’s research is been concerned with the nexus of emerging and advanced technologies and enterprise systems.


Abstract
Enterprise ontologies emerged in artificial intelligence roughly 15 years ago. Recently, the Internet of Things (IoT) and social media have developed as two of the most important technologies and philosophies of the world. Unfortunately, researchers in enterprise ontologies have largely ignored recent developments in these areas. Accordingly, this lecture will address the interaction between generation and generations of enterprise ontologies in light of the Internet of Things and Social Media. For example, I will investigate development of an ontology generated for a “supply chain of things,” evolution (i.e., generations) of a supply chain taxonomy based on data gathered from social media sources and other research issues. As part of this analysis, I will examine different emerging approaches to development of these ontologies, and their relationship to some classic notions of the semantic web.



 

 

Extracting Semantically Enriched Events from the Literature

Sophia Ananiadou
University of Manchester
United Kingdom
 

Brief Bio

Sophia Ananiadou received her PhD in Natural Language Processing (NLP) from the University of Manchester. Currently, she is Professor of Computer Science in the School of Computer Science, University of Manchester and director of the National Centre for Text Mining (www.nactem.ac.uk), hosted at the School of Computer Science and Manchester Institute of Biotechnology. Her research interests include using advanced NLP techniques for biomedical text mining, such as event extraction, developing large-scale resources (terminological resources and annotated data), text mining services and interoperable text mining platforms. Her current projects include UKPubMedCentral, text mining for the reconstruction of pathways, and extraction of semantic metadata for the automated measurement of open source software. She was the recipient three times (2006-2008) of the IBM UIMA innovation award for her work in interoperable platforms for text mining and was also awarded the Daiwa Adrian prize (2004). She has authored over 200 publications.


Abstract
Due to increasing specialisation, silo effects and literature deluge, researchers are struggling to draw out general truths and to generate hypotheses to test. The evidence to generate hypotheses is hidden in text and in addition, the type of evidence needed is complex, requiring techniques beyond statistical keyword search mechanisms, such as question answering about facts, relations and events. Biomedical information extraction has focused recently on event extraction as it permits complex associations between a number of semantic types. Event extraction allows applications such as advanced search beyond document or sentence retrieval, linking the literature with biological models, etc. Going a step further, enriching events with discourse type of knowledge opens new areas of research and applications beyond the biomedical domain, such as scholarly communications.



 

 

Parallel Coordinates: Visual Multidimensional Geometry and Its Applications

Alfred Inselberg
Tel Aviv University
Israel
 

Brief Bio
Alfred Inselberg received a Ph.D. in Mathematics and Physics from the University of Illinois (Champaign-Urbana) then was Research Professor there until 1966. He held research positions at IBM, where he developed a Mathematical Model of Ear (TIME Nov. 74), concurrently having joint appointments at UCLA, USC and later at the Technion and Ben Gurion University. Since 1995 he is Professor at the School of Mathematical Sciences at Tel Aviv University. He was elected Senior Fellow at the San Diego Supercomputing Center in 1996, Distinguished Visiting Professor at Korea University in 2008 and Distinguished Visiting Professor at National University of Singapore in 2011. Alfred invented and developed the multi-dimensional system of Parallel Coordinates for which he received numerous awards and patents (on Air Traffic Control, Collision-Avoidance, Computer Vision, Data Mining). The textbook "Parallel Coordinates: VISUAL Multidimensional Geometry and its Applications", Springer (October) 2009, has a full chapter on Data Mining and was acclaimed, among others, by Stephen Hawking.


Abstract
With parallel coordinates the perceptual barrier imposed by our 3-dimensional habitation is breached enabling the visualization of multidimensional problems. A panorama of highlights from the foundations to the most recent results, interlaced with applications and interactive demonstrations, are intuitively developed. By learning to untangle patterns from the displays, a powerful knowledge discovery process has evolved. It is illustrated on real datasets together with guidelines for exploration and good query design. Realizing that this approach is intrinsically limited leads to a deeper geometrical insight, the recognition of M-dimensional objects recursively from their (M-1)-dimensional subsets. In turn, this yields powerful geometrical algorithms (e.g. for intersections, containment, proximities) and applications including classification. A smooth surface is the envelope of its tangent planes. This is equivalent to representing the surface by its normal vectors, rather than projections as in standard surface descriptions. Developable surfaces are represented by curves revealing the surfaces’ characteristics. Convex surfaces in any dimension are recognized by the hyperbola-like (i.e. having two assymptotes) regions from just one orientation. Nonorientable surfaces (i.e. like the M¨obius strip) yield stunning patterns unlocking new geometrical insights. Non-convexities like folds, bumps, concavities and more are no longer hidden and are detected from just one orientation. Evidently this representation is preferable for some applications even in 3-D. The patterns persist in the presence of errors deforming in ways revealing the type and magnitude of the errors and that’s good news for the applications. We stand on the threshold of cracking the gridlock of multidimensional visualization. The parallel coordinates methodology is used in collision avoidance and conflict resolution algorithms for air traffic control (3 USA patents), computer vision (USA patent), data mining (USA patent) for data exploration and automatic classification, optimization, process control and elsewhere.



 

 

Challenges in the Core of Ontology Support Systems

Peter F. Patel-Schneider
Nuance
United States
 

Brief Bio
Peter F. Patel-Schneider received his Ph. D. in Computer Science from the University of Toronto in 1987. From 1983 to 1988 he was in the AI research group at Fairchild and Schlumberger. Peter then joined the AI Principles Research Department at AT&T Bell Laboratories, and then went to AT&T Labs - Research when AT&T split up in 1995. In 1997 he rejoined Bell Labs Research and remained there until 2012. Peter has taught courses at both the University of Toronto and Rutgers University. Peter's research interests center on the properties and use of Description Logics, particularly the W3C OWL Web Ontology Language. Peter designed and implemented large sections of CLASSIC, a Description Logic-based Knowledge Representation system. He designed and implemented DLP, a heavily-optimized prover for expressive Description Logics and propositional modal logics. He has performed extensive empirical evaluation of DLP and other provers for Description Logics and propositional modal logics. He developed much of OWL, and its predecessor DAML+OIL, as well as SWRL, the Semantic Web Rule Language, and OWL 2, the recent revision of OWL. Peter has recently been working on extracting semantic information from data sources, allowing data to be more easily integrated into the Semantic Web or the Web of Data. He has been determining how to use parallel computation for more effective semantic processing of large amounts of data. Peter has also been investigating how to represent and reason with services, particularly for semi-automatic service discovery


Abstract
The effective use of ontology languages, such as the W3C Semantic Web Languages OWL and RDFS, requires a complex support system, with developer interface, development cycle, knowledge acquisition, learning, application interface, and data access components as well as the core components that actually implement reasoning and querying using the ontology language. Usable versions of these core components exist for RDFS and the various versions of OWL that can reason with many large complex ontologies, and integrated systems that support the use of OWL and similar languages are now proliferating. However, the current reasoners are less capable when handling large amounts of data and expressive ontologies, and there remain daunting challenges in building reasoners supporting the full use of ontology languages.



 

 

Internet of Things – Towards New Frontiers of Knowledge Management

Florian Michahelles
ETH Zürich
Switzerland
 

Brief Bio
At ETH Zurich Florian Michahelles heads the Auto-ID Lab ETH Zurich/HSG and directs research at the forefront of mobile commerce innovations and global standards for supply-chain optimization. Additionally, he coordinates the research agenda of the global Auto-ID Labs network comprising labs at Fudan, KAIST, Keio University, MIT, Cambridge, and ETH Zurich/St. Gallen. His research interests include RFID-based approaches against anti-counterfeiting, NFC, and and mobile consumer apps in the retail domain. Michahelles received a PhD from ETH Zurich for research in wearable computing and ubiquitous computing. He holds a M.Sc (Diplom-Informatiker Univ.) degree in computer science and psychology from the Ludwig-Maximilians-University of Munich and was an MIT Sloan Visiting Fellow in 2000. Michahelles has published 50+ papers in international journals, conferences and scientific workshops. He is currently chairing the IoT2012, MuM2012 and NFC2013 conferences.


Abstract
With the emergence of networked appliances, tagged objects and products, and computing and communication capabilities in everyday devices, the Internet is reaching out to the real-world. Various application areas from retail to supply-chain logistics and from smart grid to traffic monitoring will accumulate massive amount of data which has to managed and processed in order to derive value. This talk will introduce to the main concepts of Internet of Things, present specific applications and open the space for future research challenges.



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