Last year, Forrester said “Knowledge graphs provide contextual windows into master data domains and the links between domains” (The Forrester Wave, Master Data Management).
Knowledge graphs are the key to providing the semantic and link analysis capabilities required by modern applications.
Providing relevant information to the user performing search queries or navigating a site is 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.
To add to the complexity, this search infrastructure must be introduced seamlessly into the existing platform, with access to relevant data flows to provide always up-to-date data. Moreover, it should allow for easy addition of new data sources to cater to new requirements, without affecting the entire system or the current relevance.
In all e-commerce sites, text search and catalog navigation are not only the entry points for users but they are also the main “salespeople.” Compared with web search engines, this use case has the advantage that the set of “items” to be searched is more controlled and regulated.
In this talk Luanne Misquitta 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.
Dr. Alessandro Negro is the Chief Scientist at GraphAware. He has been a long-time member of the graph community and he is the main author of the first-ever recommendation engine based on Neo4j. At GraphAware, he specialises in recommendation engines, graph-aided search, and NLP.
He has recently built an application using Neo4j and Elasticsearch aimed at personalising search results, utilizing several machine learning algorithms, natural language processing and ontology hierarchy. Before joining the team, Alessandro has gained over 10 years of experience in software development and spoke at many prominent conferences, such as JavaOne. Alessandro holds a Ph.D. in Computer Science from University of Salento.