Accession Number:



KI-LEARN: Knowledge-Intensive Learning Methods for Knowledge-Rich/Data-Poor Domains

Descriptive Note:

Final technical rept., 29 Oct 2003-30 Sep 2005

Corporate Author:


Report Date:


Pagination or Media Count:



Knowledge Representation and Reasoning KRR has developed a wide range of methods for representing knowledge and reasoning from it to produce expert-level performance. Despite these accomplishments, there is one major problem preventing the wide-spread application of KRR technology the inability to support learning. This makes KRR systems brittle and difficult to maintain. On the other hand, Machine Learning ML has developed a wide range of methods for learning from examples. However, there are two major problems preventing the wide-spread application of machine learning technology the need for large amounts of training data and the high cost of manually designing the hypothesis space of the learning system. Our goal in this research effort was to develop a new methodology, called KI-LEARN Knowledge Intensive LEARNing, that combines domain knowledge and sparse training data to construct high-performance systems. This report provides an overview of the major results we obtained on specific tasks as outlined in our proposal. More specifically, to address issues in knowledge representation and efficient learning we designed a language called First-Order Conditional Influence FOCI Language for expressing attributes relevant to learning. Our language extends probabilistic relational models PRMs which are themselves probabilistic representations most similar to first-order representation languages employed in KRR systems. A distinct feature of our language is its support for explicit expression of qualitative constraints such as monotonicity, saturation, and synergies. More importantly, we have demonstrated via mathematical proofs and experimental results how these qualitative constraints can be used and exploited when learning with sparse training data. We specifically show how qualitative constraints can be incorporated into learning algorithms. In addition, this report describes the models we constructed for our testbed domains.

Subject Categories:

  • Cybernetics
  • Psychology

Distribution Statement: