GraphAware Blog - Elasticsearch

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Relevant Search Leveraging Knowledge Graphs with Neo4j

05 May 2017 by Alessandro Negro Neo4j Elasticsearch Knowledge Graph Search NLP Recommendations

“Relevance is the practice of improving search results for users by satisfying their information needs in the context of a particular user experience, while balancing how ranking impacts business’s needs.” [1]Providing relevant information to the user performing search queries or navigating a site is always a complex task. It requires a huge set of data, a process of progressive improvements, and self-tuning parameters together with infrastructure that can support them.Such search infrastructure must be introduced seamlessly and smoothly into the existing platform, with access to all relevant data flows to provide always up-to-date data. Moreover, it should allow for easy...

Advanced Neo4j to Elasticsearch Replication

27 Jul 2016 by Christophe Willemsen Neo4j Elasticsearch Framework

During GraphConnect San Francisco 2015, we introduced the concept of Graph-Aided Search and released the first module providing Neo4j data replication to Elasticsearch.Some months later, the second part was released as an Elasticsearch plugin providing advanced personalized search using Neo4j as source of external knowledge, which, combined with the former module, constitutes a complete bidirectional integration with Neo4j, taking advantage of the strengths of both technologies.The first version of the neo4j-to-elasticsearch plugin had a simple approach for defining which nodes should be indexed. After a while the need for more flexibility arose.Based on our experience using the plugin and valuable...

Graph-Aided Search - The Rise of Personalised Content

20 Apr 2016 by Alessandro Negro, Christophe Willemsen Neo4j Cypher Recommendations Elasticsearch

In our previous blog postwe introduced the concept of Graph Aided Search. It refers to a personalised user experience during search where theresults are customised for each user based on information gathered about them (likes, friends, clicks, buying history, etc.).This information is stored in a graph database and processed using machine learning and/or graph analysis algorithms.A simple example is the LinkedIn search functionality. If we were typing “Michal” in the text input, it would obviouslyreturn people where the name matches and order them by full text relevancy with some fuzziness:Lucene-based search engines such as Elasticsearch and Solr offer impressive performance...

GraphConnect 2015 Talk

20 Nov 2015 by Michal Bachman Neo4j Conference Intermediate Search Elasticsearch

Last month, I had the pleasure of speaking at GraphConnect in San Francisco, introducing the Graph-Aided Search to alarge audience of Neo4j users and graph enthusiasts. For those who missed the conference, the recording and slides havenow been made available. Enjoy and get in touch with feedback / questions!VideoSlides Real-Time Recommendations and the Future of Search from GraphAware

Recommendations with Neo4j and Graph-Aided Search

30 Sep 2015 by Michal Bachman Neo4j Recommendations Search Elasticsearch

For the last couple of years, Neo4j has been increasingly popular as the technology of choice for people building real-time recommendation engines. Having been at the forefront of the graph movement through clientengagements and open-source software development, we have identified the next step in the natural evolution of graph-based recommendationengines. We call it Graph-Aided Search.Recommendations EverywhereAt first glance, it may seem that graph databases are only good for social networks but it has been proven over and overagain that the variety of domains and industries that need a graph database to store, analyse, and query connected datacould not be any...