Data Management for Emerging Problems in Large NetworksSpeaker: Arijit Khan – Aalborg, Denmark
Topic(s): Information Systems, Search, Information Retrieval, Database Systems, Data Mining, Data Science
AbstractGraphs are widely used in many application domains, including social networks, knowledge graphs, biological networks, software collaboration, geo-spatial road networks, interactive gaming, among many others. One major challenge for graph querying and mining is that non-professional users are not familiar with the complex schema and information descriptions. It becomes hard for users to formulate a query (e.g., SPARQL or exact subgraph pattern) that can be properly processed by the existing systems. As an example, Freebase that powers Google’s knowledge graph alone has over 22 million entities and 350 million relationships in about 5428 domains. Before users can query anything meaningful over this data, they are often overwhelmed by the daunting task of attempting to even digest and understand it. Without knowing the exact structure of the data and the semantics of the entity labels and their relationships, can we still query them and obtain the relevant results? In this talk, I shall give an overview of our user-friendly, embedding-based, scalable techniques for querying big graphs, including heterogeneous knowledge graphs.
About this LectureNumber of Slides: 40
Duration: 60 minutes
Languages Available: English
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