According to Michael Atkin, Director of EKG foundation, knowledge graphs have changed the game in terms of helping companies move away from relational databases and leverage the power of natural language processing, semantic comprehension, and machine learning to improve the rightful utilization of data.
Knowledge graphs are essential for establishing AI-powered semantic applications that can help you discover facts from your content, data, and organizational knowledge which would otherwise go ignored. By fundamentally understanding the way all data relates throughout the organization, graphs offer an added dimension of context which informs everything from initial data discovery to flexible analytics, he states.
Let’s understand how enterprise knowledge graphs (EKGs), and graph technology, in general, can help businesses manage their data effectively. We’ll also look at various real-time use-cases of graph technology.
Let’s dive in!
With the omnipresence of organizational silos and independent LOBs, the legacy enterprise infrastructure for managing data is not efficient. The data silos, when consolidated with external standards for glossaries, entity & relationship, databases, and metadata repositories, lead to incongruent data. Because of this, it becomes difficult to align the silos. As a result, organizations end up with data that is hard to access, blend, analyze and use, impeding application development, data science, analytics, process automation, reporting, and compliance.
With enterprise knowledge graphs, you acquire data that is integrated and linked rather than data that is siloed. As a result, organizations become more efficient because ontologies remain standardized and reusable.
Once the data silos are unified, you need to understand where it fits into your ecosystem. Let’s glance through some of the industry use cases of graph technology.
Cisco: Real-time Graph Analysis of documents saved over 4 million employee hours. To assign metadata to the large collection of Cisco’s historical documents, they transformed pdf and Microsoft Word file types into a Latent Dirichlet Allocation (LDA) format so the documents could be clustered by large data platforms.
Lyft: According to Tamika Tannis, 90% of Data Scientists are using knowledge graphs for routine tasks. KGs have increased productivity for their entire data science division by 30%
AstraZeneca: Joseph Roemer, Senior Director, IT insights & Analytics stated that they used graph algorithms to determine journey types and patterns of patients and then identify others having close or similar behavior.
Caterpillar: Employed graph technology to create a logical form of knowledge. The team created a data architecture that ingests text via an open-source NLP toolkit, which uses Python to combine sentences into strings, correct boundaries, and eliminate noise in the text. Data can also be imported from both SAP ERP and non-SAP ERP systems.
Hästens: Leveraged knowledge graphs to automate and streamline the management of requests for its product catalog, drastically cutting the time between order and delivery.
NASA: Chief Knowledge Architect, David Meza says that using graph technologies helped them eliminate some issues from the Apollo project, saving almost 2 years of work and 1 Million dollars of taxpayer funds.
Corporates can reap rewards by switching from traditional relational databases to knowledge graphs: capturing the knowledge from data and relations between the concepts. Since it focuses on concepts rather than precise data formats, semantic modeling avoids the problem of hard-coded premises. Even when data travels across organizational boundaries, users spontaneously understand what it represents, enabling effective reuse across systems and processes.
Thus, understanding the transition from relational database architecture to a semantic and ontology-oriented framework like knowledge graphs is important.
The majority of corporate data is kept in a relational structure and is accessible using the widely used but rigid SQL language, whereas in knowledge graph data is stored in a manner that emphasizes on terms and building their relationships.
In order to benefit from knowledge graphs, organizations must invest in new infrastructure, data must be transformed and new skills must be learned, says Amit Weitzner, co-founder Timbr.ai. You need to identify the infrastructure that is ideal for your organizational construct. An ideal infrastructure, however, can be divided into 3 components: graph storage, graph query API, and storage mutator.
Graph Storage: On-prem relational data store as the fundamental database, on top of which you can implement a node & edge store which enables you to perform basic CRUD operations on nodes (entities) and edges (relationships), instead of dealing with conventional relational schema.
Graph Query API: In the knowledge graph API module, in addition to CRUD endpoints for nodes and edges, provide a graph query endpoint. You may traverse the network using a graph query by defining a path, which is a series of edge types and data filters that starts at a certain node and returns the traversed subgraph in a structured format. A recursive interface is included in the graph query API.
Storage Mutator: You need to constantly import data to the graph storage and propagate these mutations downstream for which you can build a storage mutator.
Architecture of the knowledge graph infrastructure
Thus, knowledge graphs are crucial for a substantial shift towards a data-centric approach. The shift must involve rethinking the existing software architecture and making it more data-driven and declarative
Knowledge graphs have revolutionized the way data is stored and processed by corporations. They have a deep impact on machine learning and AI training, eventually speeding up the learning process for machines.
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