dc.contributor.author |
Agarwal, Manoj |
|
dc.contributor.other |
7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD) |
|
dc.coverage.spatial |
India |
|
dc.date.accessioned |
2024-01-17T15:23:10Z |
|
dc.date.available |
2024-01-17T15:23:10Z |
|
dc.date.issued |
2024-01-04 |
|
dc.identifier.citation |
Agarwal, Manoj, "Building knowledge graph for products at web scale", in the 7th Joint International Conference on Data Science & Management of Data (11th ACM IKDD CODS and 29th COMAD), Bangalore, IN, Jan. 4-7, 2024. |
|
dc.identifier.uri |
https://doi.org/10.1145/3632410.3633292 |
|
dc.identifier.uri |
https://repository.iitgn.ac.in/handle/123456789/9678 |
|
dc.description.abstract |
A knowledge graph is the key to entity search as it can store the factual entity related information in a structured manner without the rigidity of a fixed schema. Both Google and Bing have web scale knowledge graphs and for a large fraction of web queries knowledge graph is invoked. E-commerce search is primarily an entity search. Therefore, building a Knowledge Graph is the key to improve the eCommerce search in many ways. However, building it at web scale is a highly challenging problem. It is an equally or even more challenging problem to build the knowledge graph for products. In this tutorial, we present state-of-the-art work to address some of the key challenges to build the knowledge graph as well as our methodology to build a product graph at web scale for Microsoft-Shopping, containing a few billion facts. |
|
dc.description.statementofresponsibility |
by Manoj Agarwal |
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dc.language.iso |
en_US |
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dc.publisher |
Association for Computing Machinery |
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dc.subject |
Product graph |
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dc.subject |
Knowledge graph |
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dc.subject |
Semantic search |
|
dc.subject |
Faceted search |
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dc.subject |
Taxonomy |
|
dc.title |
Building knowledge graph for products at web scale |
|
dc.type |
Conference Paper |
|