Agents based ontological mediation in IE systems

Building more adaptive SW applications is a crucial issue to scale up IE technology to the Web, where information is organized following different underlying knowledge and/or presentation models. Information agents are more and more being adopted to support extraction of relevant information from semi-structured web sources. To efficiently manage heterogeneous information sources they must be able to cooperate, to share their knowledge, and to agree upon appropriate terminology to be used during interaction. Being the internal knowledge representation possibly different for each participant, it reveals to be unfeasible to directly communicate concepts, while agents autonomy promotes abstraction from details about the internal structure of other agents. We will argue on main topics involved in adapting natural language to achieve semantic agreement in communication, and we will introduce a novel architecture based on a pool of intelligent agents. It will be done by defining a communication model that foresees a strong separation between terms and concepts, (being this difference often undervalued in the literature, where terms play the ambiguous roles of both concept labels and communication lexicon). For agents communicating through the language, lexical information embodies the possibility to "express" the underlying conceptualizations thus agreeing to a shared representation. To make the resulting architecture adaptive to the application domain three different agents typologies have been defined: resource agents, owning the target knowledge; service agents, providing basic skills to support complex activities and control agents, supplying the structural knowledge of the task, with coordination and control capabilities. We will focus on two dedicated service agents: a mediator, that will care about understanding the information an agent wants to express as well as the way to present it to others, and a translator, dealing with lexical misalignment due to different languages. The resulting agent community dynamically assumes the most appropriate configuration, in a transparent way with respect to the involved participants.