1) Knowledge graphs are graphs that are enriched with data over time, resulting in graphs that capture more detail and context about real world entities and their relationships. This allows the information in the graph to be meaningfully searched.
2) In Neo4j, knowledge graphs are built by connecting diverse data across an enterprise using nodes, relationships, and properties. Tools like natural language processing and graph algorithms further enrich the data.
3) Cypher is Neo4j's graph query language that allows users to search for graph patterns and return relevant data and paths. This reveals why certain information was returned based on the context and structure of the knowledge graph.
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
- Learn to understand what knowledge graphs are for
- Understand the structure of knowledge graphs (and how it relates to taxonomies and ontologies)
- Understand how knowledge graphs can be created using manual, semi-automatic, and fully automatic methods.
- Understand knowledge graphs as a basis for data integration in companies
- Understand knowledge graphs as tools for data governance and data quality management
- Implement and further develop knowledge graphs in companies
- Query and visualize knowledge graphs (including SPARQL and SHACL crash course)
- Use knowledge graphs and machine learning to enable information retrieval, text mining and document classification with the highest precision
- Develop digital assistants and question and answer systems based on semantic knowledge graphs
- Understand how knowledge graphs can be combined with text mining and machine learning techniques
- Apply knowledge graphs in practice: Case studies and demo applications
This workshop presentation from Enterprise Knowledge team members Joe Hilger, Founder and COO, and Sara Nash, Technical Analyst, was delivered on June 8, 2020 as part of the Data Summit 2020 virtual conference. The 3-hour workshop provided an interdisciplinary group of participants with a definition of what a knowledge graph is, how it is implemented, and how it can be used to increase the value of your organization’s datas. This slide deck gives an overview of the KM concepts that are necessary for the implementation of knowledge graphs as a foundation for Enterprise Artificial Intelligence (AI). Hilger and Nash also outlined four use cases for knowledge graphs, including recommendation engines and natural language query on structured data.
Slides: Knowledge Graphs vs. Property GraphsDATAVERSITY
We are in the era of graphs. Graphs are hot. Why? Flexibility is one strong driver: Heterogeneous data, integrating new data sources, and analytics all require flexibility. Graphs deliver it in spades.
Over the last few years, a number of new graph databases came to market. As we start the next decade, dare we say “the semantic twenties,” we also see vendors that never before mentioned graphs starting to position their products and solutions as graphs or graph-based.
Graph databases are one thing, but “Knowledge Graphs” are an even hotter topic. We are often asked to explain Knowledge Graphs.
Today, there are two main graph data models:
• Property Graphs (also known as Labeled Property Graphs)
• RDF Graphs (Resource Description Framework) aka Knowledge Graphs
Other graph data models are possible as well, but over 90 percent of the implementations use one of these two models. In this webinar, we will cover the following:
I. A brief overview of each of the two main graph models noted above
II. Differences in Terminology and Capabilities of these models
III. Strengths and Limitations of each approach
IV. Why Knowledge Graphs provide a strong foundation for Enterprise Data Governance and Metadata Management
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine LearnNeo4j
At Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform.
We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data.
Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines. Read about their use cases!
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Maps and Meaning: Graph-based Entity Resolution in Apache Spark & GraphXDatabricks
Data integration and the automation of tedious data extraction tasks are the fundamental building blocks of a data-driven organizations and are overlooked or underestimated at times. Aside from data extraction, scraping and ETL tasks, entity resolution is a crucial step in successfully combining datasets. The combination of data sources is usually what provides richness in features and variance. Building an expertise in entity resolution is important for data engineerings to successfully combine data sources. Graph-based entity resolution algorithms have emerged as a highly effective approach.
This talk will present the implementation of a graph-bases entity resolution technique in GraphX and in GraphFrames respectively. Working from concept, through how to implement the algorithm in Spark, the technique will also be illustrated by walking through a practical example. The technique will exhibit an example where efficacy can be achieved based on simple heuristics, and at the same time map a path to a machine-learning assisted entity resolution engine with a powerful knowledge graph at its center.
The role of ML can be found upstream in building the graph, for example by using classification algorithms in determining the link strength between nodes based on data, or downstream where dimensionality reduction can play a role in clustering and reduce the computational load in the resolution stage. The audience will leave with a clear picture of a scalable data pipeline performing entity resolution effectively and a thorough understanding of the internal mechanism, ready to apply it to their use cases.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
At Data-centric Architecture Forum 2020 Thomas Cook, our Sales Director of AnzoGraph DB, gave his presentation "Knowledge Graph for Machine Learning and Data Science". These are his slides.
Graph databases provide the ability to quickly discover and integrate key relationships between enterprise data sets. Business use cases such as recommendation engines, social networks, enterprise knowledge graphs, and more provide valuable ways to leverage graph databases in your organization. This webinar will provide an overview of graph database technologies, and how they can be used for practical applications to drive business value.
The Enterprise Knowledge Graph is a disruptive platform that combines emerging Big Data and Graph technologies to reinvent knowledge management inside organizations. This platform aims to organize and distribute the organization’s knowledge, and making it centralized and universally accessible to every employee. The Enterprise Knowledge Graph is a central place to structure, simplify and connect the knowledge of an organization. By removing complexity, the knowledge graph brings more transparency, openness and simplicity into organizations. That leads to democratized communications and empowers individuals to share knowledge and to make decisions based on comprehensive knowledge. This platform can change the way we work, challenge the traditional hierarchical approach to get work done and help to unleash human potential!
Property graph vs. RDF Triplestore comparison in 2020Ontotext
This presentation goes all the way from intro "what graph databases are" to table comparing the RDF vs. PG plus two different diagrams presenting the market circa 2020
Graphs in Retail: Know Your Customers and Make Your Recommendations Engine LearnNeo4j
At Neo4j we believe that “Graphs Are Everywhere”. In this session, we’ll be exploring graphs within the Retail industry. We’ll discuss a range of data that are commonly available within a retail organisation, both online and “brick and mortar". We’ll illustrate some graphs which can be created by linking together different elements of that data and discuss the retail use cases those graphs can enable and transform.
We’ll specifically focus on use cases like Personalised Recommendations (with a live demo), Supply Chain Management, Logistics, and Customer 360. We'll also look at some relevant graph algorithms and talk about opportunities for integration with Artificial Intelligence/Machine Learning technologies, which can be used along with Neo4j to generate new value using retail data.
Walmart, Wobi, and others already deploy Neo4j for use cases like price comparison or real-time contextual and learning recommendation engines. Read about their use cases!
Relational databases were conceived to digitize paper forms and automate well-structured business processes, and still have their uses. But RDBMS cannot model or store data and its relationships without complexity, which means performance degrades with the increasing number and levels of data relationships and data size. Additionally, new types of data and data relationships require schema redesign that increases time to market.
A native graph database like Neo4j naturally stores, manages, analyzes, and uses data within the context of connections meaning Neo4j provides faster query performance and vastly improved flexibility in handling complex hierarchies than SQL.
Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge ...Jeff Z. Pan
Tutorial on "Linked Data and Knowledge Graphs -- Constructing and Understanding Knowledge Graphs" presented at the 4th Joint International Conference on Semantic Technologies (JIST2014)
Maps and Meaning: Graph-based Entity Resolution in Apache Spark & GraphXDatabricks
Data integration and the automation of tedious data extraction tasks are the fundamental building blocks of a data-driven organizations and are overlooked or underestimated at times. Aside from data extraction, scraping and ETL tasks, entity resolution is a crucial step in successfully combining datasets. The combination of data sources is usually what provides richness in features and variance. Building an expertise in entity resolution is important for data engineerings to successfully combine data sources. Graph-based entity resolution algorithms have emerged as a highly effective approach.
This talk will present the implementation of a graph-bases entity resolution technique in GraphX and in GraphFrames respectively. Working from concept, through how to implement the algorithm in Spark, the technique will also be illustrated by walking through a practical example. The technique will exhibit an example where efficacy can be achieved based on simple heuristics, and at the same time map a path to a machine-learning assisted entity resolution engine with a powerful knowledge graph at its center.
The role of ML can be found upstream in building the graph, for example by using classification algorithms in determining the link strength between nodes based on data, or downstream where dimensionality reduction can play a role in clustering and reduce the computational load in the resolution stage. The audience will leave with a clear picture of a scalable data pipeline performing entity resolution effectively and a thorough understanding of the internal mechanism, ready to apply it to their use cases.
This is Part 4 of the GoldenGate series on Data Mesh - a series of webinars helping customers understand how to move off of old-fashioned monolithic data integration architecture and get ready for more agile, cost-effective, event-driven solutions. The Data Mesh is a kind of Data Fabric that emphasizes business-led data products running on event-driven streaming architectures, serverless, and microservices based platforms. These emerging solutions are essential for enterprises that run data-driven services on multi-cloud, multi-vendor ecosystems.
Join this session to get a fresh look at Data Mesh; we'll start with core architecture principles (vendor agnostic) and transition into detailed examples of how Oracle's GoldenGate platform is providing capabilities today. We will discuss essential technical characteristics of a Data Mesh solution, and the benefits that business owners can expect by moving IT in this direction. For more background on Data Mesh, Part 1, 2, and 3 are on the GoldenGate YouTube channel: https://www.youtube.com/playlist?list=PLbqmhpwYrlZJ-583p3KQGDAd6038i1ywe
Webinar Speaker: Jeff Pollock, VP Product (https://www.linkedin.com/in/jtpollock/)
Mr. Pollock is an expert technology leader for data platforms, big data, data integration and governance. Jeff has been CTO at California startups and a senior exec at Fortune 100 tech vendors. He is currently Oracle VP of Products and Cloud Services for Data Replication, Streaming Data and Database Migrations. While at IBM, he was head of all Information Integration, Replication and Governance products, and previously Jeff was an independent architect for US Defense Department, VP of Technology at Cerebra and CTO of Modulant – he has been engineering artificial intelligence based data platforms since 2001. As a business consultant, Mr. Pollock was a Head Architect at Ernst & Young’s Center for Technology Enablement. Jeff is also the author of “Semantic Web for Dummies” and "Adaptive Information,” a frequent keynote at industry conferences, author for books and industry journals, formerly a contributing member of W3C and OASIS, and an engineering instructor with UC Berkeley’s Extension for object-oriented systems, software development process and enterprise architecture.
How Graph Databases used in Police Department?Samet KILICTAS
This presentation delivers basics of graph concept and graph databases to audience. It clearly explains how graph databases are used with sample use cases from industry and how it can be used for police departments. Questions like "When to use a graph DB?" and "Should I solve a problem with Graph DB?" are answered.
Social Network Analysis Introduction including Data Structure Graph overview. Doug Needham
Social Network Analysis Introduction including Data Structure Graph overview. Given in Cincinnati August 18th 2015 as part of the DataSeed Meetup group.
Dynamic Search Using Semantics & StatisticsPaul Hofmann
This presentation shows 3 applications of successfully combining semantics and statistics for text mining and interactive search.
1) We predict the Lehman bankruptcy using statistical topic modeling, SAP Business Objects entity extraction and associative memories (powered by Saffron Technologies).
2) We semi-automatically handle service requests at Cisco using knowledge extraction and knowledge reuse.
3) We discover user intent for interactive retrieval. User intent is defined as a latent state. The observations of this latent state are the reformulated query sequence, and the retrieved documents, together with the positive or negative feedback provided by the user. Demo shows recognizing user’s intent for health care search.
Kelly technologies is the best data science training institute in hyderabad.We provide our trainings by industrial real time experts so that our students know about real time market technology.
Atelier - Architecture d’applications de Graphes - GraphSummit ParisNeo4j
Atelier - Architecture d’applications de Graphes
Participez à cet atelier pratique animé par des experts de Neo4j qui vous guideront pour découvrir l’intelligence contextuelle. En utilisant un jeu de données réel, nous construirons étape par étape une solution de graphes ; de la construction du modèle de données de graphes à l’exécution de requêtes et à la visualisation des données. L’approche sera applicable à de multiples cas d’usages et industries.
Atelier - Innover avec l’IA Générative et les graphes de connaissancesNeo4j
Atelier - Innover avec l’IA Générative et les graphes de connaissances
Allez au-delà du battage médiatique autour de l’IA et découvrez des techniques pratiques pour utiliser l’IA de manière responsable à travers les données de votre organisation. Explorez comment utiliser les graphes de connaissances pour augmenter la précision, la transparence et la capacité d’explication dans les systèmes d’IA générative. Vous partirez avec une expérience pratique combinant les relations entre les données et les LLM pour apporter du contexte spécifique à votre domaine et améliorer votre raisonnement.
Amenez votre ordinateur portable et nous vous guiderons sur la mise en place de votre propre pile d’IA générative, en vous fournissant des exemples pratiques et codés pour démarrer en quelques minutes.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
Neo4j - Product Vision and Knowledge Graphs - GraphSummit ParisNeo4j
Dr. Jesús Barrasa, Head of Solutions Architecture for EMEA, Neo4j
Découvrez les dernières innovations de Neo4j, et notamment les dernières intégrations cloud et les améliorations produits qui font de Neo4j un choix essentiel pour les développeurs qui créent des applications avec des données interconnectées et de l’IA générative.
SOPRA STERIA - GraphRAG : repousser les limitations du RAG via l’utilisation ...Neo4j
Romain CAMPOURCY – Architecte Solution, Sopra Steria
Patrick MEYER – Architecte IA Groupe, Sopra Steria
La Génération de Récupération Augmentée (RAG) permet la réponse à des questions d’utilisateur sur un domaine métier à l’aide de grands modèles de langage. Cette technique fonctionne correctement lorsque la documentation est simple mais trouve des limitations dès que les sources sont complexes. Au travers d’un projet que nous avons réalisé, nous vous présenterons l’approche GraphRAG, une nouvelle approche qui utilise une base Neo4j générée pour améliorer la compréhension des documents et la synthèse d’informations. Cette méthode surpasse l’approche RAG en fournissant des réponses plus holistiques et précises.
ADEO - Knowledge Graph pour le e-commerce, entre challenges et opportunités ...Neo4j
Charles Gouwy, Business Product Leader, Adeo Services (Groupe Leroy Merlin)
Alors que leur Knowledge Graph est déjà intégré sur l’ensemble des expériences d’achat de leur plateforme e-commerce depuis plus de 3 ans, nous verrons quelles sont les nouvelles opportunités et challenges qui s’ouvrent encore à eux grâce à leur utilisation d’une base de donnée de graphes et l’émergence de l’IA.
GraphSummit Paris - The art of the possible with Graph TechnologyNeo4j
Sudhir Hasbe, Chief Product Officer, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Dr. Sean Tan, Head of Data Science, Changi Airport Group
Discover how Changi Airport Group (CAG) leverages graph technologies and generative AI to revolutionize their search capabilities. This session delves into the unique search needs of CAG’s diverse passengers and customers, showcasing how graph data structures enhance the accuracy and relevance of AI-generated search results, mitigating the risk of “hallucinations” and improving the overall customer journey.
GraphSummit Singapore | The Future of Agility: Supercharging Digital Transfor...Neo4j
Leonard Jayamohan, Partner & Generative AI Lead, Deloitte
This keynote will reveal how Deloitte leverages Neo4j’s graph power for groundbreaking digital twin solutions, achieving a staggering 100x performance boost. Discover the essential role knowledge graphs play in successful generative AI implementations. Plus, get an exclusive look at an innovative Neo4j + Generative AI solution Deloitte is developing in-house.
Maruthi Prithivirajan, Head of ASEAN & IN Solution Architecture, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
GraphSummit Singapore | The Art of the Possible with Graph - Q2 2024Neo4j
Neha Bajwa, Vice President of Product Marketing, Neo4j
Join us as we explore breakthrough innovations enabled by interconnected data and AI. Discover firsthand how organizations use relationships in data to uncover contextual insights and solve our most pressing challenges – from optimizing supply chains, detecting fraud, and improving customer experiences to accelerating drug discoveries.
Sudheer Mechineni, Head of Application Frameworks, Standard Chartered Bank
Discover how Standard Chartered Bank harnessed the power of Neo4j to transform complex data access challenges into a dynamic, scalable graph database solution. This keynote will cover their journey from initial adoption to deploying a fully automated, enterprise-grade causal cluster, highlighting key strategies for modelling organisational changes and ensuring robust disaster recovery. Learn how these innovations have not only enhanced Standard Chartered Bank’s data infrastructure but also positioned them as pioneers in the banking sector’s adoption of graph technology.
GraphAware - Transforming policing with graph-based intelligence analysisNeo4j
Petr Matuska, Sales & Sales Engineering Lead, GraphAware
Western Australia Police Force’s adoption of Neo4j and the GraphAware Hume graph analytics platform marks a significant advancement in data-driven policing. Facing the challenges of growing volumes of valuable data scattered in disconnected silos, the organisation successfully implemented Neo4j database and Hume, consolidating data from various sources into a dynamic knowledge graph. The result was a connected view of intelligence, making it easier for analysts to solve crime faster. The partnership between Neo4j and GraphAware in this project demonstrates the transformative impact of graph technology on law enforcement’s ability to leverage growing volumes of valuable data to prevent crime and protect communities.
GraphSummit Stockholm - Neo4j - Knowledge Graphs and Product UpdatesNeo4j
David Pond, Lead Product Manager, Neo4j
Get an inside look at the latest Neo4j innovations that enable relationship-driven intelligence at scale. Learn more about the newest cloud integrations and product enhancements that make Neo4j an essential choice for developers building apps with interconnected data and generative AI.
Shirley Bacso, Data Architect, Ingka Digital
“Linked Metadata by Design” represents the integration of the outcomes from human collaboration, starting from the design phase of data product development. This knowledge is captured in the Data Knowledge Graph. It not only enables data products to be robust and compliant but also well-understood and effectively utilized.
Your enemies use GenAI too - staying ahead of fraud with Neo4jNeo4j
Delivered by Michael Down at Gartner Data & Analytics Summit London 2024 - Your enemies use GenAI too: Staying ahead of fraud with Neo4j.
Fraudsters exploit the latest technologies like generative AI to stay undetected. Static applications can’t adapt quickly enough. Learn why you should build flexible fraud detection apps on Neo4j’s native graph database combined with advanced data science algorithms. Uncover complex fraud patterns in real-time and shut down schemes before they cause damage.
BT & Neo4j _ How Knowledge Graphs help BT deliver Digital Transformation.pptxNeo4j
Delivered by Sreenath Gopalakrishna, Director of Software Engineering at BT, and Dr Jim Webber, Chief Scientist at Neo4j, at Gartner Data & Analytics Summit London 2024 this presentation examines how knowledge graphs and GenAI combine in real-world solutions.
BT Group has used the Neo4j Graph Database to enable impressive digital transformation programs over the last 6 years. By re-imagining their operational support systems to adopt self-serve and data lead principles they have substantially reduced the number of applications and complexity of their operations. The result has been a substantial reduction in risk and costs while improving time to value, innovation, and process automation. Future innovation plans include the exploration of uses of EKG + Generative AI.
Workshop: Enabling GenAI Breakthroughs with Knowledge Graphs - GraphSummit MilanNeo4j
Look beyond the hype and unlock practical techniques to responsibly activate intelligence across your organization’s data with GenAI. Explore how to use knowledge graphs to increase accuracy, transparency, and explainability within generative AI systems. You’ll depart with hands-on experience combining relationships and LLMs for increased domain-specific context and enhanced reasoning.
Elevating Tactical DDD Patterns Through Object CalisthenicsDorra BARTAGUIZ
After immersing yourself in the blue book and its red counterpart, attending DDD-focused conferences, and applying tactical patterns, you're left with a crucial question: How do I ensure my design is effective? Tactical patterns within Domain-Driven Design (DDD) serve as guiding principles for creating clear and manageable domain models. However, achieving success with these patterns requires additional guidance. Interestingly, we've observed that a set of constraints initially designed for training purposes remarkably aligns with effective pattern implementation, offering a more ‘mechanical’ approach. Let's explore together how Object Calisthenics can elevate the design of your tactical DDD patterns, offering concrete help for those venturing into DDD for the first time!
The Metaverse and AI: how can decision-makers harness the Metaverse for their...Jen Stirrup
The Metaverse is popularized in science fiction, and now it is becoming closer to being a part of our daily lives through the use of social media and shopping companies. How can businesses survive in a world where Artificial Intelligence is becoming the present as well as the future of technology, and how does the Metaverse fit into business strategy when futurist ideas are developing into reality at accelerated rates? How do we do this when our data isn't up to scratch? How can we move towards success with our data so we are set up for the Metaverse when it arrives?
How can you help your company evolve, adapt, and succeed using Artificial Intelligence and the Metaverse to stay ahead of the competition? What are the potential issues, complications, and benefits that these technologies could bring to us and our organizations? In this session, Jen Stirrup will explain how to start thinking about these technologies as an organisation.
Le nuove frontiere dell'AI nell'RPA con UiPath Autopilot™UiPathCommunity
In questo evento online gratuito, organizzato dalla Community Italiana di UiPath, potrai esplorare le nuove funzionalità di Autopilot, il tool che integra l'Intelligenza Artificiale nei processi di sviluppo e utilizzo delle Automazioni.
📕 Vedremo insieme alcuni esempi dell'utilizzo di Autopilot in diversi tool della Suite UiPath:
Autopilot per Studio Web
Autopilot per Studio
Autopilot per Apps
Clipboard AI
GenAI applicata alla Document Understanding
👨🏫👨💻 Speakers:
Stefano Negro, UiPath MVPx3, RPA Tech Lead @ BSP Consultant
Flavio Martinelli, UiPath MVP 2023, Technical Account Manager @UiPath
Andrei Tasca, RPA Solutions Team Lead @NTT Data
Enhancing Performance with Globus and the Science DMZGlobus
ESnet has led the way in helping national facilities—and many other institutions in the research community—configure Science DMZs and troubleshoot network issues to maximize data transfer performance. In this talk we will present a summary of approaches and tips for getting the most out of your network infrastructure using Globus Connect Server.
Removing Uninteresting Bytes in Software FuzzingAftab Hussain
Imagine a world where software fuzzing, the process of mutating bytes in test seeds to uncover hidden and erroneous program behaviors, becomes faster and more effective. A lot depends on the initial seeds, which can significantly dictate the trajectory of a fuzzing campaign, particularly in terms of how long it takes to uncover interesting behaviour in your code. We introduce DIAR, a technique designed to speedup fuzzing campaigns by pinpointing and eliminating those uninteresting bytes in the seeds. Picture this: instead of wasting valuable resources on meaningless mutations in large, bloated seeds, DIAR removes the unnecessary bytes, streamlining the entire process.
In this work, we equipped AFL, a popular fuzzer, with DIAR and examined two critical Linux libraries -- Libxml's xmllint, a tool for parsing xml documents, and Binutil's readelf, an essential debugging and security analysis command-line tool used to display detailed information about ELF (Executable and Linkable Format). Our preliminary results show that AFL+DIAR does not only discover new paths more quickly but also achieves higher coverage overall. This work thus showcases how starting with lean and optimized seeds can lead to faster, more comprehensive fuzzing campaigns -- and DIAR helps you find such seeds.
- These are slides of the talk given at IEEE International Conference on Software Testing Verification and Validation Workshop, ICSTW 2022.
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2. Heatmap utilization for testing
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4. Demo
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Knowledge Graphs - The Power of Graph-Based Search
1. Knowledge Graphs:
The Power of Graph-Based Search
Petra Selmer
Query Languages Standards & Research Group
petra.selmer@neo4j.com
19 September 2018
2. The property graph data model
Introduction to Neo4j
Knowledge Graphs
Background
What is a Knowledge Graph?
How do they work in Neo4j?
Tools and techniques: a quick tour
Graph Search through Cypher
Outline
4. Underlying construct is a graph
Nodes: represent entities of interest (vertices)
Relationships: connections between nodes (edges)
• Relate nodes by type and direction
• Add structure to the graph
• Provide semantic context for nodes
Properties: key-value pairs (map) representing the data
The property graph data model
5. A node may have 0 or more labels
• E.g. Person, Appliance, Teacher
A relationship must have a type and
direction
• E.g. KNOWS, LIKES
A node or relationship may have 0 or
more properties
• E.g. name: ‘John’
The property graph data model
8. NASA
Knowledge repository for previous missions - root cause analysis
Panama Papers
How was money flowing through companies and individuals?
Some well-known use cases
10. Neo4j is an enterprise-grade native graph platform enabling you to:
• Store, reveal and query connections within your data
• Traverse and analyze any level of depth in real time
• Add context and connect new data on the fly
Consumers of connected data
15. Organizations have difficulty maintaining their corporate memory
owing to a variety of reasons:
• Growth which drives need for new and continuous education
• Turnover where long-term knowledge is lost
• Ageing infrastructures and siloed information
The Knowledge Graph problem
16. Lack of knowledge sharing slows project progress, and creates
inconsistencies even among team members.
Organizations don’t know what they don’t know, nor do they know what
they know.
Data scientists, and therefore the organization, are slow to recognize or
react to changing market conditions, so they miss opportunities to
innovate
Bad information is spread inadvertently which erodes corporate trust
Negative consequences
17. The next wave of competitive advantage will be all about using
connections to identify and build knowledge
Knowledge Graphs in the Age of Connections
20. Enriching graphs with more and more data (raw or derived) over time...
...resulting in a graph that has more detail, context, truth, intelligence
and semantics...
21. Enriching graphs with more and more data (raw or derived) over time...
...resulting in a graph that has more detail, context, truth, intelligence
and semantics…
...capturing the real world (your ecosystem) more precisely and more
comprehensively...
22. Enriching graphs with more and more data (raw or derived) over time...
...resulting in a graph that has more detail, context, truth, intelligence
and semantics…
...capturing the real world (your ecosystem) more precisely and more
comprehensively…
...so that the information captured in the graph can be searched for in
a meaningful way...
23. Enriching graphs with more and more data (raw or derived) over time...
...resulting in a graph that has more detail, context, truth, intelligence
and semantics…
...capturing the real world (your ecosystem) more precisely and more
comprehensively…
...so that the information captured in the graph can be searched for in
a meaningful way…
...yielding KNOWLEDGE, both directly and indirectly (new insights are
discovered)
24. A 360 degree-view of:
• any entity of interest,
• auxiliary entities,
• and the processes that surround these
Example:
• Customers
• Products, orders, reviews, delivery, …
• Purchasing, fulfilment, order management, shipping, …
Knowledge Graphs provide:
25. Give answers
about things it knows about
Explain why and how the answer was returned
from the context and structure within the graph
Knowledge Graphs can:
35. Information, especially in analytics, research departments and
customer service should have a searchable, consistent repository,
or representation of a repository, from which to store and draw
institutional knowledge.
Corporations who maintain a knowledge graph will develop higher
degrees of consistency across all areas of business.
The goals
36. Institutional memory requires a solution that can integrate diverse data sets, often in text
due to the legacy nature of that information and return “context” as a result
Connections and relationships, cause and effect correlation needs to be materialized
and persisted permanently
All information must be indexed, searchable and shareable
The solution must be agile, easily expandable and adaptable to changing business
conditions
The solution needs to be a combination of text-based NLP, ElasticSearch and Graphs.
Information must be easy to visualize and leverage in your processes and workflows
What’s required to get there
39. NLP (Natural Language Processing): understanding human
language (for the purposes of information extraction)
Parsing unstructured data (usually text) to extract entities, their
attributes or properties and the relationships between entities
Named entity recognition: identify entities and categorise them;
e.g. “Daniel Craig” is a Person and an Actor
Tools & techniques: enriching the data
40. NLP (Natural Language Processing): [continued…]
Entity disambiguation: how to determine which entity is being
referred to; e.g. “Amazon” can be the rainforest in South America,
or the online store
Sentiment analysis: identifying the mood/attitude/emotion of a
statement
Tools & techniques: enriching the data
41. Provenance and lineage of data:
• How/from where the data was derived or obtained
• This adds to data veracity and quality
Tools & techniques: enriching the data
42. Inferencing / deriving new facts from machine learning
APOC: ~450 procedures and user-defined functions helping with data integration,
graph algorithms, data conversions etc
https://github.com/neo4j-contrib/neo4j-apoc-procedures
Graph algorithms library (highly-parallelized & efficient): centralities (PageRank,
betweenness…), community detection, path finding etc
https://github.com/neo4j-contrib/neo4j-graph-algorithms
In constant development; links with academic institutions for cutting-edge
optimization techniques
Tools & techniques: enriching the data
43. Inferencing / deriving new facts: deductive method (from a
knowledge base, rules engine or ontology)
Rules:
If a node X has
(i) a :Person label and
(ii) there is an outgoing TEACHES relationship,
then add a :Teacher label to X
Tools & techniques: enriching the data
44. Inferencing / deriving new facts: deductive method [continued..]
Use external data sources to obtain new facts; e.g. hook into a
thesaurus to deduce/derive synonyms (ConceptNet, WordNet, ..)
Use the graph to deduce a set of rules (or begin with a seed set,
using ML) which enrich the graph, and subsequently enrich the
rules as more data gets added to the graph: feedback loop
Tools & techniques: enriching the data
46. A declarative graph pattern matching language for property
graphs
SQL-like syntax
DQL for reading data (focus of this section)
DML for creating, updating and deleting data
DDL for creating constraints and indexes
Relationship-centric querying
Recursive querying
Variable-length relationship chains
Returning paths
Introducing Cypher
This is at the
heart of Cypher
http://www.opencypher.org/cips
47. Searching for (matching) graph patterns
Specifying the
pattern to match Specifying the data
to be returned
48. // Pattern description (ASCII art)
MATCH (me:Person)-[:FRIEND]->(friend)
// Filtering with predicates
WHERE me.name = ‘Frank Black’ AND friend.age > me.age
// Projection of expressions
RETURN toUpper(friend.name) AS name, friend.title AS title
// Order results
ORDER BY name, title DESC
Variable-length relationship patterns
MATCH (me)-[:FRIEND*]-(foaf) // Traverse 1 or more FRIEND relationships
MATCH (me)-[:FRIEND*2..4]-(foaf) // Traverse 2 to 4 FRIEND relationships
// Path binding returns all paths (p)
MATCH p = (a)-[:ONE]-()-[:TWO]-()-[:THREE]-()
// Each path is a list containing the constituent nodes and relationships, in order
RETURN p
DQL: reading data
Input: a property graph
Output: a table
This tells you why the answer was returned (going
back to the context a Knowledge Graph provides)
50. Cypher today: a single graph model
Graph Database System
(e.g. a cluster)
The (single) Graph
Application Server
Client 1
Client 2
Client 3
51. Cypher: multiple graphs model
Graph Database System
(e.g. a cluster)
Multiple Graphs
Application Server
Client 1
Client 2
Client 3
52. Accept multiple graphs and a table as input
Return multiple graphs and a table
Chain queries to form a query pipeline
Subquery support
Use cases: create new graphs (either from scratch or from existing
graphs), combining and transforming graphs from multiple
sources, views, access control, snapshot graphs, roll-up and
drill-down operations, ...
Multiple graphs & query composition
53. The output of one query can be used as
the input to another
Organize a query into multiple parts
Extract parts of a query to a view for re-use
Replace parts of a query without affecting
other parts
Build complex workflows programmatically
Query composition
55. “Patterns of patterns”
Regular path queries (RPQs)
Long academic history
Can specify a path by means of a regular expression over the relationship types
(allowing for nesting of patterns)
X, knows.(likes.eats|drinks)+, Y
Find a path whose edge labels conform to the regular expression, starting at
node X and ending at node Y
Complex path patterns
Concatenation
a.b - a is followed by b
Alternation
a|b - either a or b
Transitive closure
* - 0 or more
+ - 1 or more
{m, n} - at least m, at most n
Optionality:
? - 0 or 1
Grouping/nesting
() - allows nesting/defines scope
56. Find complex connections
Increase in the need to express “nested patterns”; in particular:
(a.b)*
Property graph data model:
Properties need to be considered
Node labels need to be considered
Specifying a cost for paths (ordering and comparing)
Path Pattern Queries in Cypher
https://github.com/thobe/openCypher/blob/rpq/cip/1.accepted/CIP2017-02-06-Path-Patterns.adoc
57. PATH PATTERN
older_friends = (a)-[:FRIEND]-(b) WHERE b.age >
a.age
MATCH p=(me)-/~older_friends+/-(you)
WHERE me.name = $myName AND you.name = $yourName
RETURN p AS friendship
Path Pattern: example
59. MATCH (p:Person {name: Jack})-[r1:FRIEND]-()-[r2:FRIEND]-(friend_of_a_friend)
RETURN friend_of_a_friend.name AS fofName
+---------+
| fofName |
+---------+
| “Tom” |
+---------+
Cypher today
:Person
{ name : Jack }
:Person
{ name : Anne }
:Person
{ name : Tom }
:FRIEND :FRIEND
Usefulness proven in practice over
multiple industrial verticals
r1 and r2 may not be
bound to the same
relationship within the
same pattern
Rationale was to avoid potentially
returning infinite results for varlength
patterns when matching graphs
containing cycles (this would have been
different if we were just checking for the
existence of a path)
r1 r2
60. All the *morphisms
All forms are valid in different
scenarios
The user can configure which
semantics they wish to use at
a query level
61. Configurable pattern quantifiers controlling how many matches are returned
ANY At most one match (existence checking)
EACH All matches
Configurable pattern length restrictions limits the length and nature of
matches
SHORTEST Consider only shortest path (determined by length of path)
CHEAPEST Consider only cheapest path (determined by cost function in Path Pattern)
UNRESTRICTED
Pattern quantifiers and length restrictions
63. Principle of schema optionality
Extending the schema to ‘tighten up’ the data model
Some examples:
• Endpoint requirements; e.g. an :OWNS relationship may only end at a
node labelled with either :Vehicle or :Building
• Cardinality constraints; e.g. a :KNOWS relationship must occur no more
than 3 times between any two :Person-labelled nodes
Constraints