A better understanding of what motivates humans to perform certain actions is relevant for a range of research challenges including generating action sequences that implement goals (planning). A first step in this direction is the task of acquiring knowledge about human goals. In this work, we investigate whether Search Query Logs are a viable source for extracting expressions of human goals. F...

The ability of case-based reasoning (CBR) systems to apply cases to novel situations depends on their case adaptation knowledge. However, endowing CBR systems with adequate adaptation knowledge has proven to be a very difficult task. This paper describes a hybrid method for performing case adaptation, using a combination of rule-based and case-based reasoning. It shows how this approach provide...

There are several RDF (Resource Description Framework) knowledge bases that store community-generated categories of entities and conceptual or factual information about entities. These two types of information may have strong associations; for example, entities categorized in People from Korea (categorial information) have a high probability of being a person (conceptual information) and being ...

Access to knowledge about user goals represents a critical component for realizing the vision of intelligent agents acting upon user intent on the web. Yet, the acquisition of knowledge about user goals represents a major challenge. In a departure from existing approaches, this paper proposes a novel perspective for knowledge acquisition: The utilization of search query logs for this task. The ...

This paper describes an approach to acquire qualitative and quantitative knowledge from verbally stated models in complex, probabilistic domains. This work is part of the development of an intelligent environment, MEDICUS2, that supports modelling and diagnostic reasoning in the domains of environmental medicine and human genetics. These domains are two yet new subdomains of medicine receiving ...

We develop a new methodology and tool for knowledge acquisition under uncertainty. A new knowledge representation called Bayesian Knowledge Bases provides a powerful key to our approach and is well-grounded in probability theory. In this paper, we demonstrate the ease and flexibility with which knowledge acquisition can be accomplished while ensuring the consistency of the knowledge base as dat...

EHCPRs system is a knowledge representation and reasoning system for representing common sense knowledge and reasoning with it. In such a system an EHCPR is used as a unit of knowledge for representing any universal concept. There are a number of EHCPRs at various levels of hierarchy of knowledge structure in the EHCPRs system, which results in a tree of EHCPRs. This EHCPRs tree has the capabil...

Many Artificial Intelligence tasks need large amounts of commonsense knowledge. Because obtaining this knowledge through machine learning would require a huge amount of data, a better alternative is to elicit it from people through human computation. We consider the sentiment classification task, where knowledge about the contexts that impact word polarities is crucial, but hard to acquire from...

The WWW provides a semantic rich structure of information that, however, is primarily addressed to human understanding rather than automated processing by a computer. The problem of acquiring data from the WWW and organizing this data in a meaningful way, suitable for automated machine processing, has received a lot of attention during the last years, in the context of the new concept of semant...