Archer: Query-Driven Machine Learning
In the Archer project we develop techniques for adapting analytics in response to a query as opposed to general computation mozilla firefox deutsch herunterladen. Instead of doing a
SELECT * FROM Table, as with typical machine learning problems, we are integrating selection style queries,
SELECT * FROM Table WHERE X, into typical analytics microsoft word 2010 gratis downloaden.
Knowledge Base Acceleration
Wikipedia is the go to knowledge base for information on events, people and scores of other topics Download movies for free without registration. Wikipedia is collaboratively edited but the number of editors is far below the number of entities so it often takes a long time for important information to be added to the knowledge base sierra mac.
Knowledge Base Acceleration (KBA) task reads streams of documents and recommends documents to be cited by knowledge base pages photoshop elements 9 free german full. Several issues are involved with this tasks:
- Many documents in the stream are not relevant, millions of these documents must be filtered amazon prime videos downloaden macbook.
- Some document refer to the different entities of the same name. It is important to understand what entity a document it referring too.
- Some information is not sufficient for citation hörbuch kinder herunterladen. Event may have happened, but they may not be notable enough to be included in the knowledge base.
In this work we attempt to filter a stream of document and suggest pieces of information to be added to a set of Wikipedia entities windows desktop icons.
Query-Driven Entity Resolution
Entity resolution (ER) is the process of determining records (mentions) in a database that correspond to the same real-world entity herunterladen. Leading ER systems solve this problem by resolving every record in the database; however, for large datasets this is an expensive process. Moreover, such approaches are wasteful because in practice, users are interested in only one or a small subset of the entities mentioned in the database Download youtube on ipad. In this work, we introduce new classes of SQL queries involving ER operators — selection-driven ER and join-driven ER. We develop novel variations of Metropolis Hastings algorithm and introduce selectivity-based scheduling algorithms to support the two classes of ER queries.