Evaluation of unsupervised semantic mapping of natural language with Leximancer concept mapping

The Leximancer system is a relatively new method for transforming lexical co-occurrence information from natural language into semantic patterns in an unsupervised manner. It employs two stages of co-occurrence information extraction-semantic and relational-using a different algorithm for each stage. The algorithms used are statistical, but they employ nonlinear dynamics and machine learning. This article is an attempt to validate the output of Leximancer, using a set of evaluation criteria taken from content analysis that are appropriate for knowledge discovery tasks.

Leximancer concept mapping of patient case studies

Quantitative databases are limited to information identified as important by their creators, while databases containing natural language are limited by our ability to analyze large unstructured bodies of text. Leximancer is a tool that uses semantic mapping to develop concept maps from natural language. We have applied Leximancer to educational based pathology case notes to demonstrate how real patient records or databases of case studies could be analyzed to identify unique relationships. We then discuss how such analysis could be used to conduct quantitative analysis from databases such as the Coronary Heart Disease Database.

Investigating Concept Mapping and Stimulated Recall to Reveal Academic Teacher Beliefs and how they are enacted through learning designs for the Web

Increasingly, academic teachers are designing their own web sites to add value to or replace other forms of university teaching. These web sites are tangible and dynamic constructions that represent the teachers thinking and decisions derived from an implicit belief system about teaching and learning. The emphasis of this study is to explore the potential of the research techniques of concept-mapping and stimulated recall to locate the implicit pedagogies of academic teachers and investigate how they are enacted through the learning designs of their web sites. The rationale behind such an investigation is that once these implicit belief systems are made visible, then conversations can commence about how these beliefs are transformed into practice, providing a potent departure point for academic development.

Asymmetric page split generalized index search trees for formal concept analysis

Formal Concept Analysis is an unsupervised machine learning technique that has successfully been applied to document organisation by considering documents as objects and keywords as attributes. The basic algorithms of Formal Concept Analysis then allow an intelligent information retrieval system to cluster documents according to keyword views. This paper investigates the scalability of this idea. In particular we present the results of applying spatial data structures to large datasets in formal concept analysis. Our experiments are motivated by the application of the Formal Concept Analysis idea of a virtual filesystem [11,17,15]. In particular the libferris [1] Semantic File System. This paper presents customizations to an RD-Tree Generalized Index Search Tree based index structure to better support the application of Formal Concept Analysis to large data sources.

History of the events leading to the formulation of the apoptosis concept

Histological studies of ischaemic liver injury performed between 1962 and 1964 distinguished two types of cell death: classical necrosis, and a process involving conversion of scattered cells into small round masses of cytoplasm that often contained specks of condensed nuclear chromatin. Enzyme histochemistry demonstrated rupture of lysosomes in the former, but preservation of lysosomes in the latter. Similar small round masses were also observed sparsely in normal liver. Electron microscopy showed that the small round bodies resulted from cellular condensation and budding, that they were bounded by membranes and contained intact organelles, and that they were phagocytosed and digested by resident tissue cells, including epithelial cells. In work done in association with Jeffrey Searle, the process was found to occur spontaneously in a variety of malignant tumours and to be enhanced in squamous cell carcinomas of skin responding to radiotherapy. During 1971-1972, I collaborated with Andrew Wyllie and Alastair Currie while on sabbatical leave in Scotland. The newly defined type of cell death was shown to be regulated by hormones in the adrenal cortex and in breast carcinomas. Further, review of published electron micrographs of the cell death known to play an essential role in normal development revealed the same morphological pattern. We proposed that this distinctive phenomenon subserves a general homoeostatic function and suggested it be called apoptosis. © 2002 Elsevier Science Ireland Ltd. All rights reserved.

The modeling of turbulent reactive flows based on multiple mapping conditioning

A new modeling approach-multiple mapping conditioning (MMC)-is introduced to treat mixing and reaction in turbulent flows. The model combines the advantages of the probability density function and the conditional moment closure methods and is based on a certain generalization of the mapping closure concept. An equivalent stochastic formulation of the MMC model is given. The validity of the closuring hypothesis of the model is demonstrated by a comparison with direct numerical simulation results for the three-stream mixing problem. (C) 2003 American Institute of Physics.