We describe an approach to reducing the computational cost of identifying coreferent instances in heterogeneous semantic graphs where the underlying ontologies may not be informative or even known. The problem is similar to coreference resolution in unstructured text, where a variety of linguistic clues and contextual information is used to infer entity types and predict coreference. Semantic graphs, whether in RDF or another formalism, are semi-structured data with very different contextual clues and need different approaches to identify potentially coreferent entities. When their ontologies are unknown, inaccessible or semantically trivial, coreference resolution is difficult. For such cases, we can use supervised machine learning to map entity attributes via dictionaries based on properties from an appropriate background knowledge base to predict instance entity types, aiding coreference resolution. We evaluated the approach in experiments on data from Wikipedia, Freebase and Arnetminer and DBpedia as the background knowledge base.

Wild Big Data (WBD) is data that is hard to extract, understand, and use due to its heterogeneous nature and volume. It typically comes without a schema, is obtained from multiple sources and provides a challenge for information extraction and integration. We describe a way to subduing WBD that uses techniques and resources that are popular for processing natural language text. The approach is applicable to data that is presented as a graph of objects and relations between them and to tabular data that can be transformed into such a graph. We start by applying topic models to contextualize the data and then use the results to identify the potential types of the graph's nodes by mapping them to known types found in large open ontologies such as Freebase, and DBpedia. The results allow us to assemble coarse clusters of objects that can then be used to interpret the link and perform entity disambiguation and record linking.

In warehouses across Europe, man and machine are increasingly working more closely together – and a lack of future manpower could accelerate automation further. In Andover, online grocery retailer Ocado operates an automated fulfilment centre, which includes a robot grid – or "Hive" – which uses proprietary storage and picking technology to enhance warehouse efficiency through a tightly-controlled grid of robots shifting groceries with humans working behind the scenes. Global online retailer Amazon's Winsen centre near Hamburg is also staffed in a similar human-meets-robot way. After pioneering the use of robots in the U.S. in 2012, it has rolled out the technology to help with stock-picking in other facilities, with Winsen marking a first for Germany in the process. While robots are no longer a novelty in warehouses, the Andover and Winsen examples are far from the norm.

This paper reports the results obtained with a group of 24 14-year-old pupils when presented with sets of algebra tasks by the Leeds Modelling System.

To appear inProc. of the American Association for Medical Systems & Informatics, 1983 Reprinted by permission of the American Association for Medical Systems and Informatics (AAMSI). Hardware advances in the next decade promise to make poss:*ale new medical educational technologies. New media for expressing, collecting, and sharing knowledge will provide students with means for coping with the increasing amounts of information. Novel means of graphically modelling physical phenomena--providing motivating and intuitively pleasing means for explorative interaction--could complement and sometimes replace traditional text material. Intelligent programs may serve as assistants, serving roles ranging from calculator to librarian to tutor, embracing a full range of secretarial and problen solving aids.