Pesquisa de Doutorado da Veronica

Catherine L. Smith and Soo Young Rieh. 2019. Knowledge-Context in Search Systems: Toward Information-Literate Actions . In Proceedings of the 2019 Conference on Human Information Interaction and Retrieval (CHIIR '19). Association for Computing Machinery, New York, NY, USA, 55–62. https://doi.org/10.1145/3295750.3298940 ABSTRACT In this perspective paper we define knowledge-context as meta information that searchers use when making sense of information displayed in and accessible from a search engine results page (SERP). [ Mais uma definição sobre Contexto, específica para conhecimento e ferramentas de busca ] We argue that enriching the knowledge-context in SERPs has great potential for facilitating human learning, critical thinking, and creativity by expanding searchers' information-literate actions such as comparing, evaluating, and differentiating between information sources. Thus it supports the development of learning-centric search systems. [ Motivação para incluir contex

Čebirić, Š., Goasdoué, F., Kondylakis, H. et al. Summarizing semantic graphs: a survey. The VLDB Journal 28, 295–327 (2019). https://doi.org/10.1007/s00778-018-0528-3 Abstract The explosion in the amount of the available RDF data has lead to the need to explore, query and understand such data sources. Due to the complex structure of RDF graphs and their heterogeneity, the exploration and understanding tasks are significantly harder than in relational databases, where the schema can serve as a first step toward understanding the structure. [ Conhecer o esquema ajuda na exploração ] Summarization has been applied to RDF data to facilitate these tasks. Its purpose is to extract concise and meaningful information from RDF knowledge bases, representing their content as faithfully as possible. There is no single concept of RDF summary, and not a single but many approaches to build such summaries; each is better suited for some uses, and each presents specific challenges with respect to its

Sarrafzadeh, B., Roegiest, A., & Lank, E. (2020). Hierarchical knowledge graphs: A novel information representation for exploratory search tasks. arXiv preprint arXiv:2005.01716. ACM Transactions on Information Systems, Vol. 4, No. TOIS, Article 1. Publication date: April 2020. 5 IMPACT OF INFORMATION EXTRACTION ERRORS ON HKGS In this section, we evaluate the performance of HKGs in light of errors in information extraction. To understand why we wish to explore the impact of errors in information extraction, consider Figure 5. In typical web search, users formulate queries, inspect retrieved documents, and either view documents or, if they find that the returned documents are not exactly appropriate, reformulate queries to refine the set of documents retrieved. Because a user can directly examine the results of a query retrieval operation, the user can refine the search query to modify the retrieved documents as needed. However, when performing information extraction, one challenge