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    Knowledge Graphs: Moving Data Beyond Spreadsheets and Tables

    21 min
    |
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    May 11, 2026
    TechnologyAIBusiness

    Explore how knowledge graphs move beyond rigid spreadsheets to capture complex data relationships and context, mirroring how the human brain processes information.

    Knowledge Graphs: Moving Data Beyond Spreadsheets and Tables

    Best quote from Knowledge Graphs: Moving Data Beyond Spreadsheets and Tables

    “

    A knowledge graph isn’t just a fancy database—it’s a way of representing knowledge that mirrors how the human brain works, by focusing on the relationships between things rather than just the things themselves.

    ”

    This audio lesson was created by a BeFreed community member

    Input question

    I want to learn about knowledge graphs, semantic data, reasoning engines and their uses.

    Host voices
    Lenaplay
    Milesplay
    Learning style
    Deep
    Knowledge sources
    Knowledge Graphs
    link
    https://kgbook.org/
    Reasoning — GraphDB 10.4 documentation
    link
    https://graphdb.ontotext.com/documentation/10.4/reasoning.html
    Inference Engine | Stardog Documentation Latest
    link
    https://docs.stardog.com/inference-engine
    Knowledge Graph Reasoning Made Simple [3 Technical Methods]
    link
    https://spotintelligence.com/2024/02/05/knowledge-graph-reasoning/
    Leading Pharmaceutical Company: Accelerating Regulatory Authority Response Process with AI-Powered Semantic Similarity Search
    link
    https://graphwise.ai/success-story/leading-accelerating-regulatory-authority-response-process-with-ai-powered-semantic-similarity-search/
    RDF Triple Stores vs. Property Graphs: What's the Difference?
    link
    https://neo4j.com/blog/knowledge-graph/rdf-vs-property-graphs-knowledge-graphs

    Frequently Asked Questions

    Knowledge graphs represent a shift from traditional data storage, which often relies on rigid, two-dimensional rows and columns. While spreadsheets and tables treat data as static lists in separate tabs, knowledge graphs focus on the relationships between entities. This approach moves beyond the 'flat world' problem of traditional databases by capturing the multidimensional reality of information, allowing for a more interconnected and natural way to manage complex data structures.

    Semantic data is crucial because it provides the context that traditional data strings lack. By focusing on the 'why' and the 'how' of information, semantic data allows systems to understand the meaning behind the numbers. This shift toward contextual data is what enables smarter search results and more predictive analytics, as it mirrors the human brain's ability to recognize how different pieces of information overlap and relate to one another.

    Knowledge graphs improve data relationships by treating the connections between data points as primary information rather than secondary attributes. Instead of storing data in isolated boxes, this method creates a web of knowledge that understands context. This allows organizations to see the 'forest' rather than just individual 'trees,' making it easier to identify complex patterns, such as predicting supply chain breaks or understanding intricate information architectures that traditional databases might miss.

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    "Reading used to feel like a chore. Now it’s just part of my lifestyle."

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    "Feels effortless compared to reading. I’ve finished 6 books this month already."

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    Part of a Learning Plan

    Study DMBOK2 for CDMP Certification

    Study DMBOK2 for CDMP Certification

    LEARNING PLAN

    Study DMBOK2 for CDMP Certification

    3 h 7 m•4 Episodes

    Key Takeaways

    1

    The End of the Spreadsheet Silo

    6:30
    2

    Nodes, Edges, and the Architecture of Meaning

    2:49
    3:05
    3:09
    3:24
    3:31
    4:00
    4:05
    4:31
    4:42
    5:04
    5:11
    5:29
    3

    The Reasoning Engine as a Digital Logic Gate

    5:38
    5:56
    6:03
    6:25
    6:30
    6:50
    6:58
    7:16
    7:24
    7:42
    3:09
    8:04
    4

    Searching for Meaning in a Sea of Strings

    8:10
    8:21
    8:27
    8:49
    6:30
    9:21
    9:31
    9:48
    9:58
    10:17
    10:27
    10:34
    5

    The Challenge of Curating the World

    10:42
    10:58
    11:04
    11:25
    11:35
    11:53
    6:30
    12:16
    12:24
    12:39
    12:46
    13:00
    6

    Where Knowledge Graphs Meet Artificial Intelligence

    13:07
    13:23
    10:27
    13:52
    13:56
    14:12
    6:30
    14:37
    14:47
    15:08
    15:15
    15:24
    7

    The Practical Playbook for a Graph-Based World

    15:35
    15:44
    15:48
    16:08
    16:10
    16:30
    6:30
    16:53
    17:03
    17:17
    17:25
    17:38
    8

    The Future of the Global Brain

    17:41
    17:54
    18:09
    18:23
    18:34
    18:49
    18:57
    19:11
    19:21
    19:31
    19:38
    9

    Reframing the Digital Landscape

    19:42
    20:01
    20:16
    20:27
    20:36
    20:50
    21:02
    21:08
    21:12

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