Information discovery is a very difficult and frustrating aspect of software development. Novice developers are often assigned a mentor who preemptively provides answers and advice without requiring the novice to explicitly ask for help. A similar situation occurs among expert developers in radically collocated settings. The close proximity enhances communication between all members of a group,...

Organizing data into category hierarchies (taxonomies) is useful for content discovery, search, exploration and analysis. In industrial settings targeted taxonomies for specific domains are mostly created manually, typically by domain experts, which is time consuming and requires a high level of expertise. This paper presents an algorithm and an implemented interactive system for automatically ...

The current trend towards networked business forces enterprises to enter federated, loosely-coupled business networks, since much of the competition takes place between networks and value nets. The Pilarcos architecture provides solutions for B2B interoperability middleware to support various kinds of collaboration and cooperation networks by business service discovery and selection, interopera...

Prediction of adverse drug reactions is an important problem in drug discovery endeavors which can be addressed with data-driven strategies. SIDER is one of the most reliable and frequently used datasets for identification of key features as well as building machine learning models for side effects prediction. The inherently unbalanced nature of this data presents with a difficult multi-label m...

We compared and combined the traditional information retrieval (IR) methods of expert identification with a computational cognitive model to test their effectiveness in facilitating exploratory search performance using a data set from a large-scale social tagging system. We found that the two methods of expert identification, although based on different assumptions, were in general consistent i...

Often the manual review of large data sets, either for purposes of labeling unlabeled instances or for classifying meaningful results from uninteresting (but statistically significant) ones is extremely resource intensive, especially in terms of subject matter expert (SME) time. Use of active learning has been shown to diminish this review time significantly. However, since active learning is a...

Expert search, in which given a query a ranked list of experts instead of documents is returned, has been intensively studied recently due to its importance in facilitating the needs of both information access and knowledge discovery. Many approaches have been proposed, including metadata extraction, expert profile building, and formal model generation. However, all of them conduct expert searc...

This paper presents a comparison between traditional and automatic approaches for the extraction of an audio descriptor to recognize chord into classes. The traditional approach requires signal processing (SP) skills, constraining it to be used only by expert users. The Extractor Discovery System (EDS) [1] is a recent approach, which can also be useful for non expert users, since it intends to ...

This paper discusses actionable knowledge generation. Actionable knowledge is explicit symbolic knowledge, typically presented in the form of rules, that allows the decision maker to recognize some important relations and to perform an action, such as targeting a direct marketing campaign, or planning a population screening campaign aimed at targeting individuals with high disease risk. The dis...