Dimitris Paparas
I am a Software Engineer at Google Research where I am a member of the Dataset Search team. Our goal is to enable users find datasets stored across the Web through a simple keyword search. Prior to that, I was with Google Cloud where I designed and implemented algorithms that manage the deployment, upgrade, and decommission of machines in Google's data centers. Before joining Google, I was a Postdoctoral Research Scientist at the University of Wisconsin-Madison.
I obtained a PhD in Theoretical Computer Science from Columbia University and a BSc in Computer Science and Telecommunications followed by a MSc in Theoretical Computer Science, both from the University of Athens, Greece.
I obtained a PhD in Theoretical Computer Science from Columbia University and a BSc in Computer Science and Telecommunications followed by a MSc in Theoretical Computer Science, both from the University of Athens, Greece.
Authored Publications
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Dataset or Not? A study on the veracity of semantic markup for dataset pages
Tarfah Alrashed
Omar Benjelloun
20th International Semantic Web Conference (ISWC 2021) (to appear)
Preview abstract
Semantic markup, such as Schema.org, 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 Schema.org to identify pages that describe datasets. While Schema.org 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 Schema.org/Dataset 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 Schema.org/Dataset 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.
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