  Entity relationship extraction envisions the automatic generation of semantic
data models from collections of text, by automatic recognition of entities, by
association of entities to form relationships, and by classifying these
instances to assign them to entity sets (or classes) and relationship sets (or
associations). As a first step in this direction, the Lokahi prototype can
extract entities based on the TF*IDF measure, and generate semantic
relationships based on document-level co-occurrence statistics, for example
with likelihood ratios and pointwise mutual information. This paper presents
results of an explorative, prototypical, qualitative and synthetic research,
summarizes insights from two research projects and, based on this, indicates an
outline for further research in the field of entity relationship extraction
from text.
