Machine Learning and Graphs X 2
26/02/2018 at 19:00, Betahaus, Berlin
Talk 1: Graph-Powered Machine Learning
Speaker: Alessandro Negro
Abstract: Graph-based machine learning is becoming an important trend in Artificial Intelligence, transcending a lot of other techniques. Using graphs as basic representation of data for ML purposes has several advantages: (i) the data is already modeled for further analysis, explicitly representing connections and relationships between things and concepts; (ii) graphs can easily combine multiple sources into a single graph representation and learn over them, creating Knowledge Graphs; (iii) improving computation performances and quality. The talk will discuss these advantages and present applications in the context of recommendation engines and natural language processing.
Bio: Dr. Alessandro Negro (https://twitter.com/alessandronegro?lang=en) is Chief Scientist at GraphAware. He has been a long-time member of the graph community and is the main author of the first-ever recommendation engine based on Neo4j. At GraphAware, he specializes in recommendation engines, graph-aided search, and NLP.
Talk 2: Knowledge Graphs and Chatbots with Neo4j and IBM Watson
Speaker: Christophe Willemsen
Abstract: Knowledge Graphs are becoming the de-facto solution for managing complex aggregated knowledge, and Neo4j is the leading platform for storing and querying connected data. In this talk, Christophe will describe a graph-centric cognitive computing pipeline and detail the process from the ingestion of unstructured text up to the generation of a knowledge graph, queryable using natural language through chatbots built with IBM Watson Conversation.
Bio: Christophe Willemsen (https://www.linkedin.com/in/christophe-willemsen-4a134a54/) is a Principal Consultant at GraphAware. He is an expert on the Neo4j graph database and the Cypher query language, and, as a software engineer, has been involved in many Neo4j projects. He is the author of the Neo4j driver for php and various Java extensions for Neo4j, available at https://github.com/graphaware.