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Update knowledge_graph_embedding.md #31

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2 changes: 1 addition & 1 deletion docs/use_cases/knowledge_graph_embedding.md
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Expand Up @@ -20,7 +20,7 @@ The problem with KGs is that they are usually incomplete. Edges that should be p

> … in Freebase, 93.8% of people have no place of birth, and [78.5% have no nationality](https://aclanthology.org/P09-1113.pdf), [about 68% of people do not have any profession](https://dl.acm.org/doi/abs/10.1145/2566486.2568032), while, in Wikidata, [about 50% of artists have no date of birth](https://arxiv.org/abs/2207.00143), and only [0.4% of known buildings have information about height](https://dl.acm.org/doi/abs/10.1145/3485447.3511932).

The imperfections of KGs can lead to negative outcomes. For example, in recommendations systems, KG incompleteness can result in **limited or biased recommendations**; on Q&A tasks, KG incompleteness can yield **substantively and contextually incomplete or inaccurate answers to queries**.
The **imperfections of KGs** can lead to negative outcomes. For example, in recommendations systems, KG incompleteness can result in **limited or biased recommendations**; on Q&A tasks, KG incompleteness can yield **substantively and contextually incomplete or inaccurate answers to queries**.

Fortunately, KGEs can help solve problems that plague KGs.

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