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Use Case: Knowledge Graph & RT Computing

Knowledge graph is a term that's populated by Google as the search engine giant was building and improving the world's largest search infrastructures. The rationale behind Google is simple -- every search is driven by an intention, the keyword being searched is the surface of such intention, but the results returned and orchestrated by the search engine can be elevated with the help of knowledge graph instead of a mere page-rank of the matching URLs (of course the whole ranking thing can be further complicated with AdWords and things like that... and in the case of Baidu, the whole ranking system can be scrambled so much by factors like which advertiser is paying more for the top spots and user experience can be further damaged consequentially).

A GP-LP-Investment-Competition Knowledge Graph Web GUI

Building a general knowledge graph takes a lot of time, effort and ability to structure the knowledge system, and to visualize it and compute within the graph, especially when it grows super large, the challenge for deep graph search is simply unthinkable. Note that: PageRank is considered highly distributed yet very shallow (1-to-2 hop deep) graph computing.

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