Ontologies are often viewed as the answer to the need for interoperable semantics in
modern information systems. The explosion of textual information on the Read/Write Web …

This chapter provides an introduction to the Bayes' theorem evolution to include:(a) the early
1900s,(b) 1920–1930s,(c) 1940–1950s, and (d) 1960s–Mid 1980s, a review of the BBN …

We model scientific expertise as a mixture of topics and authority. Authority is calculated
based on the network properties of each topic network. ThemedPageRank, our combination …

Abstract The goal of Ontology Learning from Text is to learn ontologies that represent
domains or applications that change often. Manually learning and updating such ontologies …

Ontology learning techniques are based on extensive basic knowledge, from unstructured
data to structured data. Creating an ontology manually is undoubtedly a time consuming …

The ability to provide abstractions of documents in the form of important concepts and their
relations is a key asset, not only for bootstrapping the Semantic Web, but also for relieving …

This thesis addresses the task of automatically generating reading lists for novices in a
scientific field. Reading lists help novices to get up to speed in a new field by providing an …

This chapter addresses the issue of topic extraction from text corpora for ontology learning.
The first part provides an overview of some of the most significant solutions present today in …

Despite the rapid global movement towards electronic health records, clinical letters written
in unstructured natural languages are still the preferred form of inter-practitioner …

The role of the Web for text corpus construction is becoming increasingly significant.
However, the contribution of the Web is largely confined to building a general virtual corpus …