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Session Sargur Srihari
Duration 1 hour
Date/Time 11 March 2020 16:00 GMT
9:00am PDT/12:00pm EDT
4:00pm GMT/5:00pm CET
Convener KenBaclawski
Track How

Contents

Knowledge graphs, closely related to ontologies and semantic networks, have emerged in the last few years to be an important semantic technology and research area. As structured representations of semantic knowledge that are stored in a graph, KGs are lightweight versions of semantic networks that scale to massive datasets such as the entire World Wide Web. Industry has devoted a great deal of effort to the development of knowledge graphs, and they are now critical to the functions of intelligent virtual assistants such as Siri and Alexa. Some of the research communities where KGs are relevant are Ontologies, Big Data, Linked Data, Open Knowledge Network, Artificial Intelligence, Deep Learning, and many others.     (2A)

Agenda     (2B)

A knowledge graph is based on Subject-Predicate-Object (SPO) triples. The SPO triples are combined to form a graph where nodes represent entities (E) that consist of subjects and objects while directed edges represent relationships (R).     (2B2)

Knowledge Graphs typically adhere to some deterministic rules, such as type constraints and transitivity. They also include “softer” statistical patterns or regularities, which are not universally true but nevertheless have useful predictive power.     (2B3)

Probabilistic knowledge graphs incorporate statistical models for relational data. Triples are assumed to be incomplete and noisy. The joint distribution is modeled from a subset D ⊆ExRxE x{0,1} of observed triples.     (2B4)

There are two main types of models: latent feature models and Markov random fields (MRFs). Latent feature models can be trained using deep learning. MRFs can be derived from Markov Logic Representations of facts in a database. The talk will describe learning and inference using probabilistic knowledge graphs.     (2B5)

Conference Call Information     (2C)

Attendees     (2D)

Proceedings     (2E)

Resources     (2F)

Previous Meetings     (2G)


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