Task-based support in search engines

Darío Garigliotti is a postdoctoral researcher at Aalborg University, Denmark, where he works in collaboration with Prof. Katja Hose and Assoc. Prof. Johannes Bjerva, in the areas of information extraction, information retrieval and natural language processing. He completed his doctoral degree in Computer Science at University of Stavanger, Norway, under the supervision of Prof. Krisztian Balog. The website at https://dariogarigliotti.github.io/ contains more information about Garigliotti's work. The research conducted by Garigliotti focused on utilizing and extending methods and techniques from semantic search within an information access paradigm that aims to support users in achieving their tasks. More specifically, to enhance search engines with functionalities for recognizing the underlying tasks in searches and providing support for task completion. The work presented in this thesis is organized in three grand themes: entity type information for entity retrieval, entity-oriented search intents, and task-based search. Alongside the theoretical and empirical contributions, a number of resource contributions were developed, including several corpora and test collections, and a knowledge base of entity-oriented search intents.

Ontology Modelling for Metallurgy as a Domain and Retrieval Using Particle Swarm Optimization

Ontologies are the entities that are built to gather knowledge from the chosen domain and make it available to both machines and humans. In this chapter, an ontology that structures all the processes involved in the metallurgical extraction of the metals has been modelled. Ontology Development 101 methodology with minor modifications has been adopted to model the ontologies for metallurgy as the principal domain, and the processes involved in the various fields of metallurgy have been modelled into an ontology by defining the classes and the relationship between them. The model is implemented using Protégé and quantitatively evaluated using the semiotics approach. To comprehensively demonstrate the applicability of the developed ontology, an algorithm for retrieval of entities from the web incorporating particle swarm optimization and that makes use of the modelled ontology has been proposed. Finally, the quality of the modelled ontology and as well as the performance efficiency of the entity retrieval has been quantitatively evaluated.