Abduction for extending incomplete information sources

Meghini C., Tzitzikas Y., Spyratos N.

Extension  Taxonomy  Abduction 

The extraction of information from a source containing term-classified objects is plagued with uncertainty, due, among other things, to the possible incompleteness of the source index. To overcome this incompleteness, the study proposes to expand the index of the source, in a way that is as reasonable as possible with respect to the original classification of objects. By equating reasonableness with logical implication, the sought expansion turns out to be an explanation of the index, captured by abduction. We study the general problem of query evaluation on the extended information source, providing a polynomial time algorithm which tackles the general case, in which no hypothesis is made on the structure of the taxonomy. We then specialize the algorithm for two well-know structures: DAGs and trees, showing that each specialization results in a more efficient query evaluation.

Source: 4th Helenic Conference on AI, SETN 2006, pp. 267–278, Heraklion, Crete, May 18-20, 2006

Metrics