MiKroloG: The Microdata Knowledge Graph

Searching the web has changed our daily lives, documents on the web containing a list of keywords can be found in a snap. Then, users wanted to find Things, not strings. Thanks to knowledge graphs (KG), users who request movies of James Cameron receive a list of movies where James Cameron and his movies are Things, i.e. entities defined in the KG. However, searching the web offers diversity with noise, while searching KG just returns exact knowledge. The main issue is to bring back diversity from knowledge, i.e. search the web with Things. For instance, we may search the websites selling “James Cameron movies” ordered by price and ratings. This query first retrieves a collection of things, i.e. “James Cameron movies”, then asks for “commercial websites” that refer to these things, i.e. we searched the web with Things. Finding web pages with Things requires a close connection between the web of documents and Knowledge Graphs. Currently, this connection is partially powered by the embedding of microdata in web pages.  In MiKroloG, we aim to search the web with Things. To achieve this, we have three main scientific challenges: entity matching to connect web pages and Things, query processing and ranking,  and the end-user query interface.

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