DISSERTATION ABSTRACT

Task-Based Support in Search EnginesDarı́o Garigliotti

Abstract

Web search has become a key technology on which people rely daily for getting information
about almost everything. The evolution of the search experience has also shaped the expectationsof people about it. Many users seem to expect today’s web search engines to behave like a kindof “wise interpreter,” capable of understanding the meaning behind a search query, realizing its
current context, and responding to it directly and appropriately. Search by meaning, or semantic
search, encompasses a large portion of information retrieval (IR) research devoted to study moremeaningful representations of the information need expressed by the user query. Entity cards,
direct displays, and verticals are examples of how major commercial search engines have indeed
responded to user expectations, capitalizing on query understanding. Search is usually performed
with a specific goal underlying the query. In many cases, this goal consists of a nontrivial task
to be completed. Current search engines support a small set of basic tasks, and most of theknowledge-intensive workload for supporting more complex tasks is left to the user.
Task-based search can be viewed as an information access paradigm that aims to enhance searchengines with functionalities for recognizing the underlying tasks in searches and providing support
for task completion. The research presented in this thesis focuses on utilizing and extending
methods and techniques from semantic search in the next stage of the evolution of search engines,
namely, to support users in achieving their tasks. Our work can be grouped in three grandthemes: (1) Entity type information for entity retrieval: we conduct a systematic evaluation andanalysis of methods for type-aware entity retrieval, in terms of three main dimensions. Also, werevisit the problem of hierarchical target type identification, present a state-of-the-art supervised
learning method, and analyze the usage of automatically identified target entity types for typeaware entity retrieval; (2) Entity-oriented search intents: we propose a categorization scheme for
entity-oriented search intents, and study the distributions of entity intent categories per entity
type. We further develop a method for constructing a knowledge base of entity-oriented searchintents; and (3) Task-based search: we design a probabilistic generative framework for task-based
query suggestion, and principledly estimate each of its components. Furthermore, we introduce
the problems of query-based task recommendation and mission-based task recommendation, andestablish respective methods as suitable baselines.
Awarded by: University of Stavanger, Norway
Supervised by: Krisztian Balog (University of Stavanger)
Available at: http://bit.ly/dariogarigliotti-thesis
ACM SIGIR Forum1

Vol. 54 No. 1 June 2020Selected Publications
Darı́o Garigliotti, Faegheh Hasibi, and Krisztian Balog. Identifying and exploiting target entity
type information for ad hoc entity retrieval. Information Retrieval Journal, 22(3-4):285–323,
2019.
Darı́o Garigliotti and Krisztian Balog. IntentsKB: A Knowledge Base of Entity-Oriented Search
Intents. In Proceedings of the 27th ACM International Conference on Information and Knowledge Management (CIKM ’18), pages 1659–1662, 2018a.
Darı́o Garigliotti and Krisztian Balog. Towards an understanding of entity-oriented search intents.
In European Conference on Information Retrieval (ECIR ’18), pages 644–650. Springer, 2018b.
Darı́o Garigliotti and Krisztian Balog. On Type-Aware Entity Retrieval. In Proceedings of theACM SIGIR International Conference on Theory of Information Retrieval (ICTIR ’17), pages
27–34, 2017a.
Darı́o Garigliotti and Krisztian Balog. Generating Query Suggestions to Support Task-BasedSearch. In Proceedings of the 40th International ACM SIGIR Conference on Research andDevelopment in Information Retrieval (SIGIR ’17), pages 1153–1156, 2017b.

ACM SIGIR Forum2Vol. 54 No. 1 June 2020