SKG4J targeted contributions at the interface between Artificial Intelligence, Data Management and its implications for journalistic practice. The first version of the workshop accepted three submissions with topics emphasising the complementary ...

Users of Web search engines reveal their information needs through queries and clicks, making click logs a useful asset for information retrieval. However, click logs have not been publicly released for academic use, because they can be too revealing of ...

In this paper, we implement and publicly share a configurable software workflow and a collection of gold standard datasets for training and evaluating supervised query refinement methods. Existing datasets such as AOL and MS MARCO, which have been ...

Search clarification has recently attracted much attention due to its applications in search engines. It has also been recognized as a major component in conversational information seeking systems. Despite its importance, the research community still ...

Pre-trained language models have achieved great success in a wide variety of natural language processing (NLP) tasks, while the superior performance comes with high demand in computational resources, which hinders the application in low-latency ...

Many recommendation systems use users' attributes to retrieve documents before ranking. Instead of using all attributes, this work explores algorithms that choose a subset, in order to achieve higher precision. We propose a model that forecasts the ...

Platform ecosystems have witnessed an explosive growth by facilitating interactions between consumers and suppliers. Search systems powering such platforms play an important role in surfacing content in front of users. To maintain a healthy, sustainable ...

As a concise form of user reviews, tips have unique advantages to explain the search results, assist users' decision making, and further improve user experience in vertical search scenarios. Existing work on tip generation does not take query into ...

As the largest professional network, LinkedIn hosts millions of user profiles and job postings. Users effectively find what they need by entering search queries. However, finding what they are looking for can be a challenge, especially if they are ...

Large-scale pre-trained models have attracted extensive attention in the research community and shown promising results on various tasks of natural language processing. However, these pre-trained models are memory and computation intensive, hindering ...

The performance of query processing has always been a concern in the field of information retrieval. Dynamic pruning algorithms have been proposed to improve query processing performance in terms of efficiency and effectiveness. However, a single ...

The ability of conversational query understanding (CQU) is indispensable to multi-turn QA. However, existing methods are data-driven and expensive to extend to new conversation domains, or under specific frameworks and hard to apply to other underlying ...

Web search engines are frequently used to access information about products. This has increased in recent times with the rising popularity of e-commerce. However, there is limited understanding of what users search for and their intents when it comes to ...

In the top-k threshold estimation problem, given a query q, the goal is to estimate the score of the result at rank k. A good estimate of this score can result in significant performance improvements for several query processing scenarios, including ...

Commercial search engines generally maintain hundreds of thousands of machines equipped with large sized DRAM in order to process huge volume of user queries with fast responsiveness, which incurs high hardware cost since DRAM is very expensive. ...

Research activity spanning more than five decades has led to index organizations, compression schemes, and traversal algorithms that allow extremely rapid response to ranked queries against very large text collections. However, little attention has been ...

Query understanding is a fundamental problem in information retrieval (IR), which has attracted continuous attention through the past decades. Many different tasks have been proposed for understanding users' search queries, e.g., query classification or ...

Building a question-answering agent currently requires large annotated datasets, which are prohibitively expensive. This paper proposes Schema2QA, an open-source toolkit that can generate a Q&A system from a database schema augmented with a few ...

Argument search engines identify, extract, and rank the most important arguments for and against a given controversial topic. A number of such systems have recently been developed, usually focusing on classic information retrieval ranking methods that ...