Sensor nets discover search

In the world of information discovery there are several major trends which are emerging. These include the fact that the nature of search itself is changing because our information needs are themselves becoming more complex and the data volume is increasing. Other trends are that information is increasingly being aggregated, and that search is now becoming information discovery. In this presentation I address a different kind of information source to the usual media, scientific, leisure, and entertainment information we usually consume, whose availability is now upon us, namely data gathered from sensors. This covers both the physical sensors around us which monitor our environment, our wellbeing and our activities, as well as the online sensors which monitor and track things happening elsewhere in the work and to which we have access. These sensor information sources are noisy, errorsome, unpredictable and dynamic, exactly like both our real and our virtual worlds. Several wide-ranging sensor web applications are used to demonstrate the importance of event processing in managing information discovery from the sensor web

Knowledge will Propel Machine Understanding of Content: Extrapolating from Current Examples

Machine Learning has been a big success story during the AI resurgence. One particular stand out success relates to learning from a massive amount of data. In spite of early assertions of the unreasonable effectiveness of data, there is increasing recognition for utilizing knowledge whenever it is available or can be created purposefully. In this paper, we discuss the indispensable role of knowledge for deeper understanding of content where (i) large amounts of training data are unavailable, (ii) the objects to be recognized are complex, (e.g., implicit entities and highly subjective content), and (iii) applications need to use complementary or related data in multiple modalities/media. What brings us to the cusp of rapid progress is our ability to (a) create relevant and reliable knowledge and (b) carefully exploit knowledge to enhance ML/NLP techniques. Using diverse examples, we seek to foretell unprecedented progress in our ability for deeper understanding and exploitation of multimodal data and continued incorporation of knowledge in learning techniques.Comment: Pre-print of the paper accepted at 2017 IEEE/WIC/ACM International Conference on Web Intelligence (WI). arXiv admin note: substantial text overlap with arXiv:1610.0770

SciRecSys: A Recommendation System for Scientific Publication by Discovering Keyword Relationships

In this work, we propose a new approach for discovering various relationships among keywords over the scientific publications based on a Markov Chain model. It is an important problem since keywords are the basic elements for representing abstract objects such as documents, user profiles, topics and many things else. Our model is very effective since it combines four important factors in scientific publications: content, publicity, impact and randomness. Particularly, a recommendation system (called SciRecSys) has been presented to support users to efficiently find out relevant articles