Relevant Search Leveraging Knowledge Graphs with Neo4j
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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.
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Relevant Search Leveraging Knowledge Graphs with Neo4j
- 1. GraphAware® RELEVANT SEARCH LEVERAGING KNOWLEDGE GRAPHS WITH NEO4J Alessandro Negro Chief Scientist @ GraphAware graphaware.com @graph_aware, @AlessandroNegro
- 2. ‣ The rise of Knowledge Graphs ‣ Relevant Search ‣ Knowledge Graphs for e-Commerce ‣ Infrastructure ‣ Conclusions OUTLINE GraphAware®
- 3. “Knowledge graphs provide contextual windows into master data domains and the links between domains” KNOWLEDGE GRAPH CONNECTING THE DOTS GraphAware® The Forrester Wave, Master Data Management
- 4. THE RISE OF KNOWLEDGE GRAPHS GraphAware® E-Commerce ‣ Many data sources ‣ Marketing strategies ‣ Business goals ‣ Category hierarchies ‣ Searches Enterprise Networks ‣ Uncover new opportunities, hidden leads Finance ‣ Textual corpora such as financial documents contain a wealth of knowledge ‣ Structured knowledge of entities and relationships
- 5. Medicine & Health ‣ Dynamic ontologies where data is categorized and organised around people, places, things and events ‣ Patterns in disease progression, causal relations involving disease and symptoms, new relationships previously unrecognised Criminal Investigation & Intelligence ‣ Obfuscated information ‣ Traceability to sources of information GraphAware® THE RISE OF KNOWLEDGE GRAPHS
- 6. DATA SPARSITY PROBLEM GraphAware® Collaborative Filtering ‣ Cold Start Content Based Recommendation ‣ Missing Data ‣ Wrong Data Text Search ‣ User agnostic ‣ Relevant Search
- 8. RELEVANT SEARCH GraphAware® “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.”
- 10. KNOWLEDGE GRAPHS THE MODEL Search architecture must be able to handle highly heterogenous data Knowledge Graphs represent the information structure for relevant search Graphs are the right representation for: ‣ Information Extraction ‣ Recommendation Engines ‣ Context Representation ‣ Rule Engine
- 11. Critical aspects and peculiarities: ‣ Defined and controlled set of searchable Items ‣ Multiple category hierarchies ‣ Marketing strategy ‣ User feedback and interactions ‣ Supplier information ‣ Business constraints THE USE CASE E-COMMERCE GraphAware® → Text search and catalog navigation as Sales People
- 14. A graph centric approach THE DATA FLOW GraphAware® ‣ Async data ingestion ‣ Data Pipeline ‣ Single Neo4j Writer ‣ Microservice approach for isolation and scalability ‣ Event notification ‣ Multiple views exported into Elasticsearch
- 15. THE NEO4J ROLES GraphAware® ‣ Single source of truth ‣ Cleansing ‣ Fast access to connected data ‣ Query ‣ Knowledge Graph store ‣ Merging External Data ‣ Existing Data Augmentation
- 16. Natural Language Processing ‣ Unsupervised Topic Identification ‣ Word2Vec ‣ Clustering (Label Propagation) EXTERNALISE INTENSE PROCESSES GraphAware® Recommendation model building ‣ Content-Based ‣ Collaborative Filtering (internal and external)
- 17. Fast, Reliable and Easy-to-tune textual searches ‣ Multiple views for multiple scopes: ‣ Catalog Navigation and Search ‣ Faceting ‣ Product details page ‣ Product variants aggregation ‣ Autocomplete ‣ Suggestion THE ELASTICSEARCH ROLES GraphAware® → It is not used as a database
- 18. Any components of relevance-scoring calculation corresponding to a meaningful and measurable information Two techniques to control relevancy: ‣ Signal Modeling ‣ Ranking Function Note: balance precision and recall Multiple sources CRAFTING SIGNALS GraphAware®
- 19. → Users as a new source of information GraphAware® Profile-based personalisation: ‣ Explicit: Users provide profile information ‣ Implicit: Profile created from user interactions Behavioural-Based personalisation ‣ Focus on User-Item Interaction ‣ Make explicit the relationships among users and items PERSONALISING SEARCH Tying personalisation back to search ‣ Query-time personalisation ‣ Index-time personalisation
- 20. → Search for things, not for strings CONCEPT SEARCH GraphAware® Basic Approaches: ‣ Concept field (Manual Tagging) ‣ Synonyms Content Augmentation (ML based) ‣ Co-occurrence ‣ Latent Semantic Analysis ‣ Latent Dirichlet Allocation ‣ Word2Vec
- 22. Knowledge Graphs can ‣ store easy-to-query model ‣ gather data from multiple sources ‣ be easily extended Search Engines can ‣ provide fast, reliable and easy-to- tune textual search ‣ provide features like faceting, autocomplete CONCLUSION GraphAware® → By combining them, it is possible to offer an unlimited set of services to the end users