Abstract. The ever growing number of textual historical collections calls for methods that can meaningfully connect and explore these. Different collections offer different perspectives, expressing views at the time of writing or even a subjective view of the author. We propose to connect heterogeneous digital collections through temporal references found in documents as well as their textual content. We evaluate our approach and find that it works very well on digitalnative collections. Digitized collections pose interesting challenges and with improved preprocessing our approach performs well. We introduce a novel search interface to explore and analyze the connected collections that highlights different perspectives and requires little domain knowledge. In our approach, perspectives are expressed as complex queries. Our approach supports humanity scholars in exploring collections in a novel way and allows for digital collections to be more accessible by adding new connections and new means to access collections.

Domain specialists such as council members may benefit from specialised search functionality, but it is unclear how to formalise the search requirements when developing a search system. We adapt a faceted task model for the purpose of characterising the tasks of a target user group. We first identify which task facets council members use to describe their tasks, then characterise council member tasks based on those facets. Finally, we discuss the design implications of these tasks for the development of a search engine.Based on two studies at the same municipality we identified a set of task facets and used these to characterise the tasks of council members. By coding how council members describe their tasks we identified five task facets: the task objective, topic aspect, information source, retrieval unit, and task specificity. We then performed a third study at a second municipality where we found our results were consistent.We then discuss design implications of these tasks because the task model has implications for 1) how information should be modelled, and 2) how information can be presented in context, and it provides implicit suggestions for 3) how users want to interact with information.Our work is a step towards better understanding the search requirements of target user groups within an organisation. A task model enables organisations developing search systems to better prioritise where they should invest in new technology.

We adapt previous literature on search tasks for developing a domain-specific search engine that supports the search tasks of policy workers. To characterise the search tasks we conducted two rounds of interviews with policy workers at the municipality of Utrecht, and found that they face different challenges depending on the complexity of the task. During simple tasks, policy workers face information overload and time pressures, especially during web-based searches. For complex tasks, users prefer finding domain experts within their organisation to obtain the necessary information, which requires a different type of search functionality. To support simple tasks, we developed a web search engine that indexes web pages from authoritative sources only. We tested the hypothesis that users prefer expert search over web search for complex tasks and found that supporting complex tasks requires integrating functionality that enables finding internal experts within the broader web search engine. We constructed representative tasks to evaluate the proposed system’s effectiveness and efficiency, and found that it improved user performance. The search functionality developed could be standardised for use by policy workers in different municipalities within the Netherlands.

We explore how to generate effective queries based on search tasks. Our approach has three main steps: 1) identify search tasks based on research goals, 2) manually classify search queries according to those tasks, and 3) compare three methods to improve search rankings based on the task context. The most promising approach is based on expanding the user's query terms using task terms, which slightly improved the NDCG@20 scores over a BM25 baseline. Further improvements might be gained if we can identify more specific search tasks.