Mining topological relations from the web

Topological relations between geographic regions are of interest in many applications. When the exact boundaries of regions are not available, such relations can be established by analysing natural language information from web documents. In particular we demonstrate how redundancy-based techniques can be used to acquire containment and adjacency relations, and how fuzzy spatial reasoning can be employed to maintain the consistency of the resulting knowledge base

Continuous Improvement Through Knowledge-Guided Analysis in Experience Feedback

Continuous improvement in industrial processes is increasingly a key element of competitiveness for industrial systems. The management of experience feedback in this framework is designed to build, analyze and facilitate the knowledge sharing among problem solving practitioners of an organization in order to improve processes and products achievement. During Problem Solving Processes, the intellectual investment of experts is often considerable and the opportunities for expert knowledge exploitation are numerous: decision making, problem solving under uncertainty, and expert configuration. In this paper, our contribution relates to the structuring of a cognitive experience feedback framework, which allows a flexible exploitation of expert knowledge during Problem Solving Processes and a reuse such collected experience. To that purpose, the proposed approach uses the general principles of root cause analysis for identifying the root causes of problems or events, the conceptual graphs formalism for the semantic conceptualization of the domain vocabulary and the Transferable Belief Model for the fusion of information from different sources. The underlying formal reasoning mechanisms (logic-based semantics) in conceptual graphs enable intelligent information retrieval for the effective exploitation of lessons learned from past projects. An example will illustrate the application of the proposed approach of experience feedback processes formalization in the transport industry sector

The Laws of Thought

The Laws of Thought is an exploration of the deductive and inductive foundations of rational thought. The author here clarifies and defends Aristotle’s Three Laws of Thought, called the Laws of Identity, Non-contradiction and Exclusion of the Middle – and introduces two more, which are implicit in and crucial to them: the Fourth Law of Thought, called the Principle of Induction, and the Fifth Law of Thought, called the Principle of Deduction. This book is a thematic compilation drawn from past works by the author over a period of twenty-three years (1990-2013)

Voronoi-Based Region Approximation for Geographical Information Retrieval with Gazetteers

Gazetteers and geographical thesauri can be regarded as parsimonious spatial models that associate geographical location with place names and encode some semantic relations between the names. They are of particular value in processing information retrieval requests in which the user employs place names to specify geographical context. Typically the geometric locational data in a gazetteer are confined to a simple footprint in the form of a centroid or a minimum bounding rectangle, both of which can be used to link to a map but are of limited value in determining spatial relationships. Here we describe a Voronoi diagram method for generating approximate regional extents from sets of centroids that are respectively inside and external to a region. The resulting approximations provide measures of areal extent and can be used to assist in answering geographical queries by evaluating spatial relationships such as distance, direction and common boundary length. Preliminary experimental evaluations of the method have been performed in the context of a semantic modelling system that combines the centroid data with hierarchical and adjacency relations between the associated place names

Toward automated evaluation of interactive segmentation

We previously described a system for evaluating interactive segmentation by means of user experiments (McGuinness and O’Connor, 2010). This method, while effective, is time-consuming and labor-intensive. This paper aims to make evaluation more practicable by investigating if it is feasible to automate user interactions. To this end, we propose a general algorithm for driving the segmentation that uses the ground truth and current segmentation error to automatically simulate user interactions. We investigate four strategies for selecting which pixels will form the next interaction. The first of these is a simple, deterministic strategy; the remaining three strategies are probabilistic, and focus on more realistically approximating a real user. We evaluate four interactive segmentation algorithms using these strategies, and compare the results with our previous user experiment-based evaluation. The results show that automated evaluation is both feasible and useful

Logical and uncertainty models for information access: current trends

The current trends of research in information access as emerged from the 1999 Workshop on Logical and Uncertainty Models for Information Systems (LUMIS'99) are briefly reviewed in this paper. We believe that some of these issues will be central to future research on theory and applications of logical and uncertainty models for information access

A Deep Belief Network and Case Reasoning Based Decision Model for Emergency Rescue

The frequent occurrence of major public emergencies in China has caused significant human and economic losses. To carry out successful rescue operations in such emergencies, decisions need to be made as efficiently as possible. Using earthquakes as an example of a public emergency, this paper combines the Deep Belief Network (DBN) and Case-Based Reasoning (CBR) models to improve the case representation and case retrieval steps in the decision-making process, then designs and constructs a decision-making model. The validity of the model is then verified by an example. The results of this study can be applied to maximize the efficiency of emergency rescue decisions