- This event has passed.
Neural Search Comes to Apache Solr [Berlin Buzzwords]
June 13 @ 4:00 pm - 4:40 pm
Berlin Buzzwords is a conference focused on open source software projects in the field of big data analysis, scalability, storage and searchability. It provides a platform for developers, engineers, IT architects, analysts and data scientists who are interested in information retrieval, the searchability of large amounts of data, NoSQL and big data processing.
APACHE LUCENE/SOLR COMMITTER
APACHE SOLR PMC MEMBER
Alessandro has been involved in designing and developing search-relevant solutions from the early stages of Apache Solr 1.4 and edismax query parser in 2010. Over the years he has worked on various projects aiming to build search solutions able to satisfy the user information needs, often integrating such solutions with machine learning and artificial intelligence technologies.
Neural Search Comes to Apache Solr: Approximate Nearest Neighbor, BERT and More!
The first integrations of machine learning techniques with search allowed to improve the ranking of your search results (Learning To Rank) – but one limitation has always been that documents had to contain the keywords that the user typed in the search box in order to be retrieved. For example, the query “tiger” won’t retrieve documents containing only the terms “panthera tigris”. This is called the vocabulary mismatch problem and over the years it has been mitigated through query and document expansion approaches.
Neural search is an Artificial Intelligence technique that allows a search engine to reach those documents that are semantically similar to the user’s query without necessarily containing those terms; it avoids the need for long lists of synonyms by automatically learning the similarity of terms and sentences in your collection through the utilisation of deep neural networks and numerical vector representation.
This talk explores the first Apache Solr official contribution about this topic, available from Apache Solr 9.0.
During the talk we will give an overview of neural search (Don’t worry – we will keep it simple!): we will describe vector representations for queries and documents, and how Approximate K-Nearest Neighbor (KNN) vector search works.
We will show how neural search can be used along with deep learning techniques (e.g, BERT) or directly on vector data, and how we implemented this feature in Apache Solr, giving usage examples!
Join us as we explore this new exciting Apache Solr feature and learn how you can leverage it to improve your search experience!