Wikidata is a companion to Wikipedia that captures a substantial part of the information about most Wikipedia entities in machine-readable structured form. In addition to directly representing information from Wikipedia itself, Wikidata also cross-references how additional information about these entities can be accessed through APIs on hundreds of other websites.

Abstract Semantic markup, such as , allows providers on the Web to describe content using a shared controlled vocabulary. This markup is invaluable in enabling a broad range of applications, from vertical search engines, to rich snippets in search results, to actions on emails, to many others. In this paper, we focus on semantic markup for datasets, specifically in the context of developing a vertical search engine for datasets on the Web, Google’s Dataset Search. Dataset Search relies on to identify pages that describe datasets. While was the core enabling technology for this vertical search, we also discovered that we need to address the following problem: pages from 61% of internet hosts that provide markup do not actually describe datasets. We analyze the veracity of dataset markup for Dataset Search’s Web-scale corpus and categorize pages where this markup is not reliable. We then propose a way to drastically increase the quality of the dataset metadata corpus by developing a deep neural-network classifier that identifies whether or not a page with markup is a dataset page. Our classifier achieves 96.7% recall at the 95% precision point. This level of precision enables Dataset Search to circumvent the noise in semantic markup and to use the metadata to provide high quality results to users.

Today, many web sites offer third-party access to their data through web APIs. But manually encoding URLs with arbitrary endpoints, parameters, authentication handshakes, and pagination, among other things, makes API use challenging and laborious for programmers, and untenable for novices. In addition, each site offers its own idiosyncratic data model, properties, and methods that a new user must learn, even when the sites manage the same common types of information as many others.

Users have long struggled to extract and repurpose data from websites by laboriously copying or scraping content from web pages. An alternative is to write scripts that pull data through APIs. This provides a cleaner way to access data than scraping; however, APIs are effortful for programmers and nigh-impossible for non-programmers to use. In this work, we empower users to access APIs without programming. We evolve a schema for declaratively specifying how to interact with a data API. We then develop ScrAPIr: a standard query GUI that enables users to fetch data through any API for which a specification exists, and a second GUI that lets users author and share the specification for a given API. From a lab evaluation, we find that even non-programmers can access APIs using ScrAPIr, while programmers can access APIs 3.8 times faster on average using ScrAPIr than using programming.

Email remains a critical channel for communicating information in both personal and work accounts. The number of emails people receive every day can be overwhelming, which in turn creates challenges for efficient information management and consumption. Having a good estimate of the significance of emails forms the foundation for many downstream tasks (e.g. email prioritization); but determining significance at scale is expensive and challenging.

Email continues to be one of the most important means of online communication, leading to a number of challenges related to information overload and email management. To better understand email management practices in detail, we examine the distribution of visits to emails over time. During their lifetime, emails may be visited one or more times, and with each visit different actions may be taken. Emails that are revisited over time are especially interesting because they represent an opportunity to improve email management and search. In this paper, we present a large-scale log analysis of email revisitation, the activities that people perform on revisited email messages (e.g. responding to, organizing or deleting messages, and opening attachments), and the strategies they use to go back to these emails. We find that most emails have a short lifetime, with more than 33% having a lifetime of less than 5 minutes. We also find that deleting is the most common action taken on messages visited once, and that responding and organizing are more common for messages visited more than once. We complement the log analysis with a survey to understand the motivation behind revisits and the types of emails that are revisited. The survey results show that 73% of the visits are to find information (e.g. a link or document, instructions to perform a task, or answers to questions), while 20% of revisits are to respond to the email. Our findings have implications for designing email clients and intelligent agents that support both short- and long-term revisitation patterns.

Mavo is a small extension to the HTML language that empowers non-programmers to create simple web applications. Authors can mark up any normal HTML document with attributes that specify data elements that Mavo makes editable and persists. But while applications authored with Mavo allow users to edit individual data items, they do not offer any programmatic data actions that can act in customizable ways on large collections of data simultaneously or that modify data according to a computation. We explore an extension to the Mavo language that enables non-programmers to author these richer data update actions. We show that it lets authors create a more powerful set of applications than they could previously, while adding little additional complexity to the authoring process. Through user evaluations, we assess how closely our data update syntax matches how novice authors would instinctively express such actions, and how well they are able to use the syntax we provided.