Agents and The Semantic Web Portal

Group Members

Project Description

Introduction The rationale behind the Semantic Web effort is to provide machines ability to understand the information so that they can do tasks in behalf of people. This definition itself includes the idea of software agents working on Semantic Web. Actually it is quite reasonable to say that agents will be the most important component in the real semantic web.

There are a myriad of tasks available for agents on the semantic web. While we could create several new agent tasks, the Semantic Web portal is an interesting way to combine agents for a larger purpose. SWPortal may be seen as a powerful search engine where agents search the semantic web for the required query and exchange information with each other to get better results.

There are several steps required to accomplish the tasks presented in this scenerio

In this report we present some of the tools available for these tasks, existing agent systems that use semantic web and give a decription of how a SWPortal may be built using these components.

Tools

Existing Agent Systems

Semantic Web Portal A Semantic Web portal is several steps beyond today's search engine. Instead of requiring users to enter a series of keywords, the SWPortal can generate results for a term or collection of terms specified in an ontology. That step alone removes much of ambiguity from search. This can be extended further by incorporating it into editors like SMORE. As users edit their pages, a panel dedicated to the Portal can be returning pages with similar markup, related images and data, or references to other material.

Several steps are required for implementing this portal:

Here is a fictitious screenshot of the Semantic Search Portal:

It represents the basic idea and is by no means complete. There are two main stages involved. In the first stage, the user provides associations between keywords in the search string and actual ontological references (classes/properties/instances or in the worst case the user leaves it blank providing incomplete information). Secondly, the software performs a combined search using multiple agents (bots) associated with different ontologies that parse the rdf data based on the query, exchange information with one another and use inference rules to further extend the search domain.

As can be seen in the example, when the user searches for 'Java Programmer in College Park' the result of the search returns a 'Professor (of Programming Languages) at the University of Maryland' (note: no keyword match at all) simply because the user provides associations such as 'programmer is a class...', 'college park' is an instance of 'city' etc..and leaves the rest to the search agents that filter data and make inferences based on the rdf query.

On the Nature Dataset

As someone is marking up an article (in a portal enabled editor), the user could find related articles in potentially different research areas (assuming there is access to all of the nature articles which are properly marked up). This could provide additional references, images, or just a general link to research in related disciplines that may be of use.

Links

Agent Systems and Devlopement Environments

Related Tools

Semantic Search Tools

Related Papers

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