"The longer you can look back, the farther you can look forward." - Winston Churchill With the abundant availability of information one can mine from the Web today, there is increasing interest to develop a complete understanding of the history of an entity (i.e., a person, a company, a music genre, a country, etc.) (see, for example) and to depict trends over time. This, however, remains a largely difficult and manual task despite more than a couple of decades of research in the areas of temporal databases and data integration. The difficulty to create a comprehensive understanding of entities over time largely stems from the lack of (explicit) temporal data, and tools for interpreting such data even if they were available. Ideally, we would like to develop a time machine for information, where one can easily and incrementally ingest temporal data to form a more and more comprehensive understanding of entities over time, search and query facts for a particular time period, understand trending patterns over time, and perform analytics that would allow one to, for example, understand the prevalent "knowledge" in the previous decade. In this tutorial, we describe the techniques critical in building such a time machine for information, and discuss how far (or close) we are in achieving this goal.
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