We describe a system for extracting concepts from unstructured text. We do this by clustering document words and then assembling a structure which relates these words semantically. The clustering process identifies words which co--occur across a set of documents and creates groups of words which suggest a semantic context common across the document set. This context is formalized by identifying semantic relationships between the cluster words using a lexical database to build a Semantic Relationship Graph (SRG). This SRG is a directed graph which conveys a robust representation of the sub-- and super--class relationships between the correct word senses. We show how this process can be applied to a user--selected set of HTML documents; the SRGs can subsequently aid in searching the World Wide Web for documents which are similar. 1 Introduction Mining textual information presents challenges over data mining of relational or transaction databases because there are no predefined fields...