Voice-driven Knowledge Graph Journey with Neo4j and Amazon Alexa
In 2016, 25% of web searches on Android were made by voice and this percentage is predicted to double by 2018. From Amazon Alexa to Google Home, smartwatches and in-car systems, touch is no longer the primary user interface. In this talk, Alessandro and Christophe will demonstrate how graphs and machine learning are used to create an extracted and enriched graph representation of knowledge from text corpus and other data sources. This representation will then be used to map user intents made by voice to an entry point in this Neo4j backed knowledge graph. Every user interaction will then have to be taken into account at any further steps and we will highlight why graphs are an ideal data structure for keeping an accurate representation of a user context in order to avoid what is called machine or bot amnesia. The speakers will then conclude the session by explaining about how recommendations algorithms are used to predict next steps of the user’s journey.
Spring Data Neo4j: Graph Power Your Enterprise Apps
A few weeks ago Spring Data Neo4j version 5 was released as part of the Spring Data 2.0 release train. Time to present the Spring way to work with Neo4j and introduce the latest features SDN 5 and its supporting library Neo4j-OGM 3 provide. The talk will also give an overview of the overall architecture and shows examples how to build modern, compact back-ends and web-applications using Spring Data Neo4j. Of course we will give a glance of what the future will bring to Spring Data Neo4j.
Relevant Search Leveraging Knowledge Graphs with Neo4j
Neo4j as a viable tool in a relevant search ecosystem demonstrating that it offers not only a suitable model for representing several complex data, like text, user models, business goal, and context information but also providing efficient ways for navigating this data in real time. Moreover at an early stage in the “search improvement process” Neo4j can help relevance engineers to identify salient features describing the content, the user or the search query, later will be helpful to find a way to instruct the search engine about those features through extraction and enrichment.
Moreover, the talk demonstrates how the graph model can provide the right support for all the components of the relevant search and concludes with the presentation of a complete end-to-end infrastructure for providing relevant search in a real use case. It will show how it is integrated with other tools like Elasticsearch, Apache Kafka, Stanford NLP, OpenNLP, Apache Spark.
Real-Time Recommendations and the Future of Search
From user preferences and location to time of day and weather, complex context representations have been the key to delivering personalised content. Graph databases excel at dealing with large amounts of complex data and therefore, they have been at the core of many modern real-time recommendation systems. In the near future, graph databases will play an equally important role in search personalisation.
Neo4j Online Meetup #30: Spring Data Neo4j 5 and OGM3
SDN is a Spring Data project for Neo4j. It uses Neo4j-OGM under the hood (very much like Spring Data JPA uses JPA) and provides functionality known from the Spring Data world like repositories, derived finders or auditing. Neo4j recently released Spring Data 2.0 (Kay) / Spring Data Neo4j 5.0 and in this session we’ll show some of the new cool features. This release contains support for dynamic properties, schema based loading, field access only, and more.
Graph Database Prototyping made easy with Graphgen
Graphgen aims at helping people prototyping a graph database, by providing a visual tool that ease the generation of nodes and relationships with a Cypher DSL. Many people struggle with not only creating a good graph model of their domain but also with creating sensible example data to test hypotheses or use-cases. Graphgen aims at helping people with no time but a good enough understanding of their domain model, by providing a visual dsl for data model generation which borrows heavily on Neo4j Cypher graph query language. The ascii art allows even non-technical users to write and read model descriptions/configurations as concise as plain english but formal enough to be parseable. The underlying generator combines the DSL inputs (structure, cardinalities and amount-ranges) and combines them with a comprehensive fake data generation library to create real-world-like datasets of medium/arbitrary size and complexity. Users can create their own models combining the basic building blocks of the dsl and share their data-descriptions with others with a simple link.
Building High Performance Applications with Spring Data Neo4j 4
Vince Bickers, Principal Consultant at GraphAware and main contributor to Spring Data Neo4j, gives an update on the release of the new version of SDN.
Knowledge Graph Search with Elasticsearch — Luanne Misquitta and Alessandro Negro, GraphAware
In this talk, Luanne will share insights about the business value of knowledge graphs and their contribution to relevant search in an e-commerce domain for a Neo4j customer. With text search and catalog navigation being the entry point of users to the system and in fact, driving revenue, the talk will explain the challenges of relevant search and how graph models address them. Dr. Alessandro will then talk about various techniques used for information extraction and graph modelling. He will also demonstrate how to seamlessly introduce knowledge graphs into an existing infrastructure and integrate with other tools such as ElasticSearch, Kafka, Apache Spark, OpenNLP and Stanford NLP.
Podcast Interview with Nicolas Mervaillie, GraphAware
Here’s another great interview with a long time Graphista that has done a lot of really interesting work in our French community, and is now having lots of graph-fun at GraphAware: Nicolas Mervaillie.
Machine Learning Powered by Graphs
Speaker: Dr. Alessandro Negro, Chief Scientist at GraphAware. September 20th 2017 at Westin Hotel, Abu Dhabi UAE.