An Ontology of Megaprojects

Megaprojects are symbolic milestones of human history. From the Great Pyramid of Giza and the Great Wall of China to the Hoover Dam and the Manhattan Project, history is marked by an array of megaprojects. Some megaprojects are born out of necessity while others showcase power and status of individuals, groups, or countries. Most megaprojects are one-of-a-kind endeavors to which traditional project management principles are neither applicable nor suitable, rendering the holistic study of megaprojects especially difficult. Regardless of the recent uptick in research on megaprojects there is no systemic framework that can help systematically assess and guide megaprojects and megaproject research. In the absence of such a framework there is a significant risk of bias in planning the projects and the topics researched. In this paper, we present an ontology of megaprojects and discuss how it can help analyze individual megaprojects and synthesize the corpus of megaproject research

Information Systems to Manage Local Climate Change Effects: A Unified Framework

In many localities, local climate change effects are disasters-in-the-making or -in- waiting. They must therefore be managed coherently and consistently to assure the resilience of the local population and its communities. They are of deep concern at the local, state, federal, and international levels of government. Information systems play a critical role in managing local climate change effects. We draw upon many simple and selective frameworks in the literature, some explicitly articulated, and others implicitly incorporated, to present a unified framework for information systems to manage local climate change effects. The framework is both systemic in its coverage and systematic in its development. Its outlook is symmetrically neutral with respect to local climate change effects, recognizing that the change could be both beneficial and harmful to the local community. It is presented using structured natural English and can be easily understood, interpreted, and applied by the researchers, policy makers, and practitioners

Domain-Specific Knowledge Exploration with Ontology Hierarchical Re-Ranking and Adaptive Learning and Extension

The goal of this research project is the realization of an artificial intelligence-driven lightweight domain knowledge search framework that returns a domain knowledge structure upon request with highly relevant web resources via a set of domain-centric re-ranking algorithms and adaptive ontology learning models. The re-ranking algorithm, a necessary mechanism to counter-play the heterogeneity and unstructured nature of web data, uses augmented queries and a hierarchical taxonomic structure to get further insight into the initial search results obtained from credited generic search engines. A semantic weight scale is applied to each node in the ontology graph and in turn generates a matrix of aggregated link relation scores that is used to compute the likely semantic correspondence between nodes and documents. Bootstrapped with a light-weight seed domain ontology, the theoretical platform focuses on the core back-end building blocks, employing two supervised automated learning models as well as semi-automated verification processes to progressively enhance, prune, and inspect the domain ontology to formulate a growing, up-to-date, and veritable system.\\ The framework provides an in-depth knowledge search platform and enhances user knowledge acquisition experience. With minimum footprint, the system stores only necessary metadata of possible domain knowledge searches, in order to provide fast fetching and caching. In addition, the re-ranking and ontology learning processes can be operated offline or in a preprocessing stage, the system therefore carries no significant overhead at runtime

Thirty years of artificial intelligence in medicine (AIME) conferences: A review of research themes

Over the past 30 years, the international conference on Artificial Intelligence in MEdicine (AIME) has been organized at different venues across Europe every 2 years, establishing a forum for scientific exchange and creating an active research community. The Artificial Intelligence in Medicine journal has published theme issues with extended versions of selected AIME papers since 1998

Cognition-based approaches for high-precision text mining

This research improves the precision of information extraction from free-form text via the use of cognitive-based approaches to natural language processing (NLP). Cognitive-based approaches are an important, and relatively new, area of research in NLP and search, as well as linguistics. Cognitive approaches enable significant improvements in both the breadth and depth of knowledge extracted from text. This research has made contributions in the areas of a cognitive approach to automated concept recognition in. Cognitive approaches to search, also called concept-based search, have been shown to improve search precision. Given the tremendous amount of electronic text generated in our digital and connected world, cognitive approaches enable substantial opportunities in knowledge discovery. The generation and storage of electronic text is ubiquitous, hence opportunities for improved knowledge discovery span virtually all knowledge domains. While cognition-based search offers superior approaches, challenges exist due to the need to mimic, even in the most rudimentary way, the extraordinary powers of human cognition. This research addresses these challenges in the key area of a cognition-based approach to automated concept recognition. In addition it resulted in a semantic processing system framework for use in applications in any knowledge domain. Confabulation theory was applied to the problem of automated concept recognition. This is a relatively new theory of cognition using a non-Bayesian measure, called cogency, for predicting the results of human cognition. An innovative distance measure derived from cogent confabulation and called inverse cogency, to rank order candidate concepts during the recognition process. When used with a multilayer perceptron, it improved the precision of concept recognition by 5% over published benchmarks. Additional precision improvements are anticipated. These research steps build a foundation for cognition-based, high-precision text mining. Long-term it is anticipated that this foundation enables a cognitive-based approach to automated ontology learning. Such automated ontology learning will mimic human language cognition, and will, in turn, enable the practical use of cognitive-based approaches in virtually any knowledge domain --Abstract, page iii

Extracting fine-grained economic events from business news

Based on a recently developed fine-grained event extraction dataset for the economic domain, we present in a pilot study for supervised economic event extraction. We investigate how a state-of-the-art model for event extraction performs on the trigger and argument identification and classification. While F1-scores of above 50{%} are obtained on the task of trigger identification, we observe a large gap in performance compared to results on the benchmark ACE05 dataset. We show that single-token triggers do not provide sufficient discriminative information for a fine-grained event detection setup in a closed domain such as economics, since many classes have a large degree of lexico-semantic and contextual overlap

Machine Learning & Neurosciences

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