The Business of Facebook: Social Graphs, Applications, & The Future of the Web

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Dave McClure

LinkedIn Founder/Chairman Reid Hoffman's keynote presentation at Graphing Social Patterns 2007

Business Technologie

The Business of Facebook Applications: Social Graphs, Applications, and the Future of the Web Graphing Social Patterns Reid Hoffman, 9 Oct 2007

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by Lukas Masuch, Henning Muszynski and Benjamin Raethlein 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!

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Presentation of the Semantic Knowledge Graph research paper at the 2016 IEEE 3rd International Conference on Data Science and Advanced Analytics (Montreal, Canada - October 18th, 2016) Abstract—This paper describes a new kind of knowledge representation and mining system which we are calling the Semantic Knowledge Graph. At its heart, the Semantic Knowledge Graph leverages an inverted index, along with a complementary uninverted index, to represent nodes (terms) and edges (the documents within intersecting postings lists for multiple terms/nodes). This provides a layer of indirection between each pair of nodes and their corresponding edge, enabling edges to materialize dynamically from underlying corpus statistics. As a result, any combination of nodes can have edges to any other nodes materialize and be scored to reveal latent relationships between the nodes. This provides numerous benefits: the knowledge graph can be built automatically from a real-world corpus of data, new nodes - along with their combined edges - can be instantly materialized from any arbitrary combination of preexisting nodes (using set operations), and a full model of the semantic relationships between all entities within a domain can be represented and dynamically traversed using a highly compact representation of the graph. Such a system has widespread applications in areas as diverse as knowledge modeling and reasoning, natural language processing, anomaly detection, data cleansing, semantic search, analytics, data classification, root cause analysis, and recommendations systems. The main contribution of this paper is the introduction of a novel system - the Semantic Knowledge Graph - which is able to dynamically discover and score interesting relationships between any arbitrary combination of entities (words, phrases, or extracted concepts) through dynamically materializing nodes and edges from a compact graphical representation built automatically from a corpus of data representative of a knowledge domain.

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Relational databases power most applications, but new use-cases have requirements that they are not well suited for. That's why new approaches like graph databases are used to handle join-heavy, highly-connected and realtime aspects of your applications. This talk compares relational and graph databases, show similarities and important differences. We do a hands-on, deep-dive into ease of data modeling and structural evolution, massive data import and high performance querying with Neo4j, the most popular graph database. I demonstrate a useful tool which makes data import from existing relational databases with a non-denormalized ER-model a "one click"-experience. Which leaves biggest challenge for people coming from a relational background is to adapt some of their existing database experience to new ways of thinking.

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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!

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We live in an era where the world is more connected than ever before and the trajectory is such that data relationships will only continue to increase with no signs of slowing down. Connected data is the key to your business succeeding and growing in today’s connected world. Leading enterprises will be the ones that utilize relationship-centric technologies to leverage connections from their internal operations and supply chain to their customer and user interactions. This ability to utilize connected data to understand all the nuanced relationships within their organization will propel them forward as they act on more holistic insights. Every organization needs a knowledge graph because connected data is an essential foundation to advancing business. Knowledge graphs provide: - Increased visibility between internal groups - Efficiency gains - Cross-functional data collaboration - Core complete and reliable business insights - Better customer engagement The live presentation and discussion can be found here: https://youtu.be/7vBdlXzhs_4 Additional reading on why connected data is beneficial: https://www.graphgrid.com/why-connected-data-is-more-useful/ Connected data solutions available by Benjamin and his team via GraphGrid and AtomRain: https://www.graphgrid.com and https://www.atomrain.com

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Magazine Luiza, one of the largest retail chains in Brazil, developed an in-house product recommendation system, built on top of a large knowledge Graph. AWS resources like Amazon EC2, Amazon SQS, Amazon ElastiCache and others made it possible for them to scale from a very small dataset to a huge Cassandra cluster. By improving their big data processing algorithms on their in-house solution built on AWS, they improved their conversion rates on revenue by more than 25 percent compared to market solutions they had used in the past.

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