Keynote Lectures

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

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

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

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

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

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.

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.

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.

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.

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.