| Abstract |
With the increase in the amount of data published in the Web an important subject of interest that has recently gained momentum in the Semantic Web community is how to provide scalable reasoning techniques to support the type of Web applications normally found in the Web, such as information search, data integration, personalization and query answering, among others. In practice, the Semantic Web requires reasoning under constraints and limited resources such as time, knowledge and storage. These circumstances are somehow similar to the conditions under which humans normally make decisions and solve problems. After all, we only have limited time and knowledge to make reasonable decisions, or decisons that are good enoguh, in everyday life situations.
In the field of Cognitive Psychology, heuristics are seen as essential and powerful cognitive tools that enable humans (or artificial systems) to make reasonable decisions and inferences and behave adaptively in the environment. Their beauty as tools of reasoning comes from their simplicity, which is due to their ability to exploit the structure of information in the environment and evolved human capacities. Several cognitive heuristics have been developed and studied in different domains, including the recognition heuristic, take-the-best and take-the-first heuristics and the fluency heuristic, among others.
In my talk I will introduce the concept of cognitive heuristics, explain why they work (and why they don't sometimes) and present example of some of the most common heuristics used by humans in decision making and problem solving tasks. I will then explain why I think heuristics can be useful as tools to enable scalable reasoning in the Semantic Web.
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