How to build knowledge graphs
create data may be less than 100% accurate. Knowledge graphs developed out of such data may have inaccuracies that get carried forward. Over time, cumulative inaccuracies can lead to significant errors. There is also the challenge of disambiguation, where the exact words or phrases carry different meanings, e.g., turkey can be a bird or a country. Hence, disambiguation of such data becomes necessary. Moreover, data can come in different languages, apart from being different in structure. Hence, it is crucial to translate it into one common language before further processing. Charting the way ahead Integrating data across the enterprise and supporting complex decisions, knowledge graphs will drive the next wave of technological advancement. And organizations eyeing scalable implementations to solve enterprise data challenges will adopt an agile approach toward knowledge graphs. Having said that, one must have a strong business case to create them, as knowledge graphs involve considerable time, expertise, and money. While large organizations with excessive data can build their graphs, data-driven medium and small enterprises can opt for open-source knowledge graphs and customize them based on their use case. This gives every organization the wherewithal to develop knowledge graphs that will prove valuable in their business decision-making.
Structured data in a business has a relational database and follows an enforced schema, while unstructured data has no fixed schema. Both the data are combined to extract entities and form relationships between them. These entities and their relationships conform to ontologies that define the schema of knowledge graphs. Ensuring data consistency and data model understanding, ontologies serve as the basis for instances of knowledge graphs. Knowledge graph development calls for a consider- able investment of time and money.
Steps
Collect all sources of data — structured and unstructured. B ased on ontologies, organize data to a common standard.
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Apply reasoning algorithm to derive new knowledge from the data and extract entities and relationships between the data. Apply semi-automatic and manual data validation methodologies to establish the correctness of the extracted knowledge.
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Challenges faced
With data growth, knowledge graphs need regular updating to cover incoming data. The algorithms used to
Knowledge graphs can easily capture a small amount of data. But as data scales up, they may need help to expand. Hence deploying knowledge graphs at scale becomes essential.
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