Fusion rule technology is being developed to merge structured reports. Syntactically, a structured
report is a data structure containing a number of grammatically simple phrases together with a tag (giving semantic information) for each phrase. Each phrase that is tagged is a textentry. The set of tags in a structured
report is meant to parameterize a stereotypical situation, and so a particular structured
report is an instance of that stereotypical situation. For example, news reports on corporate acquisitions can be represented as structured
reports using tags including buyer, seller, acquisition, value, and date. Each phrase in structured
report is very simple, such as a proper noun, a date, or a number with unit of measure, or a word or phrase from a prescribed lexicon. For an application, the prescribed lexicon delineates the types of states, actions, and attributes, that could be conveyed by the structured
report.
In order to merge structured reports, we need to take account of their content. Different kinds of content need to be merged in different ways. In our approach to merging structured
reports we draw on domain knowledge to help produce merged reports. The approach is based on fusion rules defined in an XML file. These rules are of the form X IMPLIES Y, where if X is true in the knowledgebase, then Y is an instruction that needs to be undertaken in the process of building an
output merged report.
To merge a set of structured reports, we start with the background knowledge and the information in the
input reports to be merged, and apply the fusion rules to this information. For a set of structured
reports and a set of fusion rules, we attempt to ground each fusion rule with textentries from the structured
reports, and then check whether all the conditions of each ground fusion rule are implied by the background knowledge, and, if they are, then the ground actions of the rule are added to the actionlist (a list of actions that specify how the merged report should be constructed).
The basic architecture for fusion rule technology is based on three key modules implemented in Java: A fusion engine that executes a rulefile (an XML file containing the fusion rules marked up in FusionRuleML) by grounding each fusion rule with textentries from the structured
reports, and then checks whether all the conditions of each ground fusion rule are implied by the background knowledge and, if they are, then the ground actions of the rule are added to the actionlist (a list of actions that specify how the merged report should be constructed); An action engine that executes the actionlist to build a merged report; and a knowledge manager that queries prolog knowledgebases and SQL databases.
A fusion system is a system for merging structured reports for a particular domain. It incorporates the fusion rule technology modules for executing fusion rules and for executing the resulting actions. It also incorporates a set of fusion rules that has been defined for the application domain together with an appropriate background knowledgebase.
It is clear that there is a need for greater use of more sophisticated knowledge representation and reasoning for addressing semantic heterogeneity in information integration. Fusion Rule Technology offers a software platform for context-dependent merging of heterogeneous structured information in a way that minimizes conflict and reduces redundancy. This offers distinct advantages over other approaches to information integration. That is, we obtain the following benefits with fusion rules:
Overall, Fusion Rule Technology provides a principled context-sensitive approach to handling a range of kinds of uncertainty and inconsistency that arise, and taking account of semantic heterogeneity issues arising, can produce aggregated output that is less conflicting, better integrated, and more informative, than the input. In a sense, it creates new knowledge.