Aleksander Pivk

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We present a novel approach to learning taxonomic relations between terms by considering multiple and heterogeneous sources of evidence. In order to derive an optimal combination of these sources, we exploit a machine-learning approach , representing all the sources of evidence as first-order features and training standard classifiers. We consider in(More)
Turning the current Web into a Semantic Web requires automatic approaches for annotation of existing data since manual approaches will not scale in general. We here present an approach for automatic generation of frames out of tables which subsequently supports the automatic population of ontologies from table-like structures. The approach consists of a(More)
The tremendous success of the World Wide Web is countervailed by efforts needed to search and find relevant information. For tabular structures embedded in HTML documents typical keyword or link-analysis based search fails. The Semantic Web relies on annotating resources such as documents by means of ontologies and aims to overcome the bottleneck of finding(More)
Intelligent agents have been applied to electronic commerce, promising a revolution in the way we conduct business, whether business-to-business, business-to-customer or customer-to-customer. This article gives a brief review of agent technologies involved in buying and selling, followed by lists of Internet e-commerce agents. Several agent-mediated(More)
A universal agent should be capable of gathering information from arbitrary heterogeneous sites and offer intelligent information services on its own based on information so gathered. We present a domain-dependent agent for information gathering. It can visit an arbitrary domain-related site by observing a user perform the first query. By understanding key(More)