Conversational Bibliographic Search is a conversational search engine designed to support users in retrieving scientific papers and authors within the domain of computer science. It enables natural language-based searches for data within the dblp computer science bibliography, which is enriched with data from Semantic Scholar. The system presents a novel user interface and bridges the gap between keyword-based search engines, faceted search systems, and generative conversational approaches. It enables users to access the latest publications, formulate intricate queries, as often required in scholarly research, and engage in multi-turn conversations to discover the most relevant results. Users can iteratively refine their queries and ask follow-up questions. Conversational Bibliographic Search actively supports the search process by posing clarification questions and providing suggestions.

Finding experts, publications, and topics is a daily task not only of every scientist and student but also for journalists and people who search for sources when consuming information. To support this process, we aim to develop a conversational search engine with which it is possible to search for experts interactively and to explore interesting publications and topics where existing tools reach their limits. An important aspect of the search is that the search query is formulated in such a way that it leads to the desired result. However, formulating a query by a user or understanding a query by a system are challenging tasks. For example, when a query is formulated too unspecific, the search results might not entirely cover the information need whereby small further pieces of information can help immensely. Current systems do little to accurately understand the user’s search intent and offer little support during the search process. Thus, we designed an interactive search engine which runs in a chat window, so that the query can be specified over several turns until the desired search results are obtained. The search engine initiates the conversation by asking the user what they want to search for. The user answers in natural language or can choose adequate answers suggested by the system. The conversation continues until the user has fulfilled their search need or wants to start the conversation from the beginning in order to perform a new search.

In almost every area of research, it is necessary to find experts and publications on a topic. However, finding experts and publications is a difficult task not only for computers, but also for humans. For example, searching for experts, a user often enters a topic into a search engine, which then checks which people have published on that topic. A problem arises when a user does not make their query specific enough which can happen intentionally, e.g. when the user is doing a navigational search, or unintentionally, e.g., when the user lacks knowledge. As a result, the quality of the search results may not be very high and the best results may not be found. Current and widely used search engines for bibliographic metadata, such as dblp[2], ResearchGate, Google Scholar or Semantic Scholar allow only keyword-based searches. Kreutz et al.[1] presented SchenQL, a query language for bibliographic metadata that allows users to formulate their queries more easily and precisely than SQL. However, it requires training to understand the language and is not as easy for non-experts to use e.g. Google Scholar.

With the Web augmenting every day and computers increasingly getting more powerful, research in the field of computational argumentation becomes more and more important. One of its research branches is argument retrieval, which aims at finding and presenting users the best arguments for their queries. Several systems already exist for this purpose, all having the same goal but reaching it in different ways. In line with existing work, an argument consists of a claim supported or attacked by a premise. Now that argument retrieval has become a separate task in the CLEF lab Touché, displaying the ranking is becoming increasingly important.