Introduction to Graph Analytics and Knowledge Graph
Graph analytics is a new field of data analytics that helps businesses leverage their model by adopting a variety of its algorithms to identify the best solutions for their challenges. Each algorithm analyzes connections uniquely, revealing new information. They reveal what's going on in a network, such as who has the most influence, is well-connected, member of a sub-network, etc.
Studies show that many organizations embraced graph-related technology between 2019 and 2020. According to one such report, the worldwide industry is expected to expand from $584 million in 2019 to $2,522 million by 2024. This was at a Compound Annual Growth Rate (CAGR) of 34.0% during the forecast period.
Major vendors in the global graph analytics market include Microsoft (US), IBM (US), AWS (US), Oracle (US), Neo4j (US), TigerGraph (US), Cray (US), and DataStax (US).
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A knowledge graph, also known as a semantic network, depicts the relationship between real-world entities such as objects, events, situations, or concepts. Knowledge graphs can aid in data governance, fraud detection, knowledge management, search, chatbot, recommendation, and intelligent systems across multiple organizational units.
What is Graph Analytics?
It also known as Graph Algorithms, are analytic techniques used to determine the strength and the direction of connections between objects in a graph. It focuses on pairwise relationships between two objects at a given time and the graph's structural characteristics.
Types of Graph Analytics
There are four types of graph analytics:
This type of analysis looks at the connections between nodes in a graph. To put it another way, it finds the shortest path between two nodes.
It aids in comparing network connectivity by highlighting how strongly or weakly two nodes are linked. This is useful for determining how many edges flow into a node and how many flow out of it.
This method determines how essential a node is to the network's connectivity. It uses ranking to locate the most influential people in a social network or find the most popular web pages.
This is a distance and density-based analysis of relationships among groups used to locate groups of people who regularly engage with each other in a social network. It also identifies whether individuals are transient and can predict if the network will grow.
How does Graph Analytics work?
In graph analytics, we store, manage, and query data in the form of a graph, as the name implies. Our "entities" or "nodes" become vertices, while "edges" are associations that serve as communication lines between the various entities. Furthermore, an edge can be a line that runs in one or more directions, or it might be a line that runs in no direction at all. It also has a "weight" associated with it, representing the relationship's strength.
For instance, Knowledge Graph - A Powerful Data Science Technique for Mining Information from Text.
Use Cases of Graph Analytics
Graph analytics is required by every organization that relies on connected data analysis to make critical decisions.
Social Network Analysis
Businesses may identify influencers and decision-makers by using social network data using it, which is crucial information in sales. This information is needed to maximize sales efforts by negotiating with the relevant people. Social Network Analysis is excellent for screening surprising discoveries that can speed up the decision-making process and better engage prospective customers in the sales pipeline.
Social network analytics can be used in workforce analytics to discover trendsetters and social influencers who can encourage the workforce to accept initiatives, create impact, and even resolve sticky behavioral problems. Ensuring these influencers' buy-in and participation can also improve overall staff engagement.
Fraud detection and analysis
The study of interactions between different actors in a transaction is part of the fraud analysis process. This identifies entities inside a system that are possibly troublesome and vulnerable to fraud. It helps in detecting bad actors and implements countermeasures to prevent fraudulent behavior.
Graph analytics can also be used to detect illegal behavior and criminal activity. Law enforcement uses graph analysis to understand data that can identify malignant and benign activity by tracking phone calls, emails, persons visiting suspects in specific locations, and a network of monetary distribution.
Engines of Recommendation
Its application includes "You may also know" or "You may also like" recommendations in social media platforms and entertainment applications.
They use graph analytics to identify a stream or creator you might be interested in and then recommend content from that stream or creator to you on your feed.
The "You may also know" recommendations are usually backed by the school or institution you attended, the firm you worked for, or a mutual link.
Defense and National Security
Using graph analytics for national security and defense is controversial since it compromises citizens' privacy.
Governments analyze individual chat messages, online activities, and phone calls to identify and apprehend criminals and remove unnecessary suspicion from innocent civilians. From the government's perspective, this aids in strengthening national security by disrupting terrorist networks and preventing enemy assaults and crimes.
Optimizing system resources and maximizing utilization in computer and communication networks requires balancing loads. Analyzing network relationships enables the identification of overburdened resources and, as a result, the design of traffic reallocation to decrease risk and topology reconfiguration to optimize operations.
Utility firms that supply essential services like water, sewage, electricity, dams, and natural gas can employ network analysis to optimize resource usage and develop utility delivery systems that achieve maximum performance while reducing crucial component depletion.
Logistics organizations also utilize it to optimize routes. Graph analysis aids in the identification of the most optimal routes (by weighing numerous factors that influence travel) that can save money and maintain a smooth supply chain.
What is a Knowledge Graph?
It is a knowledge base that integrates data using a graph-structured data model or topology. They are frequently used to contain interconnected descriptions of entities, such as objects, events, situations, abstract concepts, and the semantics behind the terminology used.
Simply put, It converts your data into knowledge that machines can understand. But how to tell the difference between data and knowledge? Knowing this answer is key to understanding its definition of it.
The following are examples of real-world knowledge:
- Situational: the meaning changes depending on the scenario.
- Layered: the connections between concepts enable more complex comprehension.
- Changing: discoveries instantly change meanings.
These aspects of knowledge represent the context that data often lacks. Traditional data management systems fail when it comes to capturing context. These failures result in data mastering gridlock, delays in adding new data sources or properties, and missing datasets from analysis, all of which lead to mistrust. On the other hand, Knowledge Graphs are explicitly designed for the changing nature of knowledge. They provide a more adaptable foundation for digital operations by quickly accepting new data, definitions, and needs.
Importance of Knowledge Graph
Because being data-driven isn't enough – knowledge-driven businesses can act with full context and confidence in their judgments.
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Benefits of Knowledge Graph
The "graph" in Knowledge Graph refers to a method of arranging data that emphasizes relationships between the data points. Graph representation looks similar to a network of interconnected points. This contrasts to relational databases like Oracle or MySQL, which store data in tables. Relational systems are intended for business processes with a consistent data model.
The emphasis on stability leads to data integration rigidity which is problematic. Data integration platforms based on relational systems necessitate constant data transformation to ensure consistency across all entities. This produces a so-called "unified view" of data but lacks analytical flexibility since it excludes situational, layered, or dynamic reality representations.
A flexible data layer
It is an appropriate tool for enterprise data integration since it can make real-world context machine-understandable. Graphs' capacity to continually link concepts — without affecting the underlying data — is used to unify data instead of joining tables. As a result, data unification connects data silos and provides an organization with a flexible data layer.
How does a Knowledge Graph work?
The working architecture of knowledge graphs is listed below:
Domain knowledge is defined through ontologies, including definitions, relationships, and rules. It collects and organizes data into an ontology (or several ontologies) before making it available to enterprise applications.
Enhanced by Artificial Intelligence
The Inference Engine of the Knowledge Graph intelligently derives new knowledge from data and business logic. When there are competing definitions — for example, a Top Account in one database may be referred to as a Prospect or Churn Risk in another — you can declare in the data model that all of these entities are "Companies" and use inference to examine the relevant data.
Limitless data access
The data that a Knowledge Graph may access determines its power. Virtualization is essential for combining all relevant data into it and enriching it. Knowledge Graphs, in combination with virtualization, provide for complete flexibility in evaluating dependencies across various systems and data types. We can easily trace data lineage, detect connection and causation, and perform root cause and impact analysis.
It makes answering questions more easily. Queries and searches, natural language processing, machine learning techniques, visualization, and business intelligence are all simple to prepare.
Examples of Knowledge Graphs
- DBPedia: This project uses the structure of Wikipedia's infoboxes to construct a massive dataset of 4.58 objects (https://wiki.dbpedia.org/about) and an ontology with encyclopedic coverage of people, locations, films, books, organizations, species, diseases, and more. The Open Linked Data movement is built around this dataset. It has proven highly beneficial for businesses to use millions of crowdsourced entities to bootstrap their internal knowledge graphs.
- Google Knowledge Graph: With the announcement of its knowledge graph in 2012, Google popularised this concept.
- Wordnet: One of the most well-known lexical databases with definitions and synonyms for the English language. They are frequently used to improve the performance of natural language processing and search applications.
- Geonames: Users of the Geonames dataset have access to 25 million geographical entities and features.
Use cases of Knowledge Graph
Traditional graph or relational data architectures lack the access, context, and inferencing necessary to meet the arduous demands for innovating and monetizing advanced analytic solutions.
Data fabrics provide a more adaptable solution, allowing for the dynamic delivery of semantically enriched data. An Enterprise Knowledge Graph is a critical component in converting existing data infrastructure into a data fabric. This is accomplished by Stardog's platform, which employs a novel combination of graph, virtualization, and inference.
Data Lake Acceleration
enable data teams to improve analytics insights by streamlining data access and discovery
Unifies software and hardware assets with all relevant knowledge touched by these IT systems and applications, laying the groundwork for your operational risk practice
Graph analytics and its associated technologies can enable the analysis of data and relationships that would be extremely difficult or unachievable using standard analytics approaches. Graph analytics and graph approaches have grown in popularity in analyzing complex data interactions. It is feasible to draw insights in increasingly complicated ways using it, making it a vital tool for today's enterprises.
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