Google Knowledge Graph | EPFL Graph Search

The Google Knowledge Graph is a knowledge base from which Google serves relevant information in an infobox beside its search results. This allows the user to see the answer in a glance, as an instant answer. The data is generated automatically from a variety of sources, covering places, people, businesses, and more. The information covered by Google's Knowledge Graph grew quickly after launch, tripling its data size within seven months (covering 570 million entities and 18 billion facts). By mid-2016, Google reported that it held 70 billion facts and answered "roughly one-third" of the 100 billion monthly searches they handled. By March 2023, this had grown to 800 billion facts on 8 billion entities. There is no official documentation of how the Google Knowledge Graph is implemented.
According to Google, its information is retrieved from many sources, including the CIA World Factbook and Wikipedia. It is used to answer direct spoken questions in Google Assistant and Google Home v

ROXANNE Research Platform: Automate criminal investigations

Maël Fabien, Petr Motlicek

Criminal investigations require manual intervention of several investigators and translators. However, the amount and the diversity of the data collected raises many challenges, and cross-border investigations against organized crime can quickly impossible to handle. We developed ROXANNE Research platform, an all-in-one platform which processes intercepted phone calls, runs state-of-the-art components such as speaker identification, automatic speech recognition or named entity detection, and builds a knowledge graph of the extracted information. Our aim for this work is to do a first step in the direction of an open research platform combining speech, text, and video processing algorithms with criminal network analysis for combating organized crime.

ISCA-INT SPEECH COMMUNICATION ASSOC2021

Node Attribute Completion in Knowledge Graphs with Multi-Relational Propagation

Eda Bayram, Alberto Garcia Duran, Robert West

The existing literature on knowledge graph completion mostly focuses on the link prediction task. However, knowledge graphs have an additional incompleteness problem: their nodes possess numerical attributes, whose values are often missing. Our approach, denoted as MrAP, imputes the values of missing attributes by propagating information across the multi-relational structure of a knowledge graph. It employs regression functions for predicting one node attribute from another depending on the relationship between the nodes and the type of the attributes. The propagation mechanism operates iteratively in a message passing scheme that collects the predictions at every iteration and updates the value of the node attributes. Experiments over two benchmark datasets show the effectiveness of our approach.

2020

Representation Learning for Multi-relational Data

Eda Bayram

Recent years have witnessed a rise in real-world data captured with rich structural information that can be better depicted by multi-relational or heterogeneous graphs.However, research on relational representation learning has so far mostly focused on the problems arising in simple, homogeneous graphs. Integrating the structural priors provided by multi-relational data may further empower the generalization capacity of representation learning models, yet it still remains an open challenge.Although there is a strong line of works on relational machine learning on knowledge graphs, it is quite concentrated on the task ofcompleting missing edges, which is known as link prediction. In this thesis, we shift the focus away from the well-addressed node and graph classification problems on simple graphs or the link prediction problem on knowledge graphs, and prompt new research questions targeting the representation learning problems that are overlooked in multi-relational data.First, we focus on the problem of node regression on multi-relational graphs, noting that inference of continuous node features across a graph is rather under-studied in the current relational learning research.We propose a novel propagation method which aims to complete missing features at the nodes of a multi-relational and directed graph.Our multi-relational propagation algorithm is composed of iterative neighborhood aggregations which originate from a relational local generative model. Our findings show the benefit of exploiting the inductive bias led by the multi-relational structure of the data.Next, we consider the node attribute completion problem in knowledge graphs, which is relatively unexplored by the knowledge graph reasoning literature.We propose a novel multi-relational attribute propagation method where we harness not only the relational structure of the knowledge graph, but also the dependencies between various types of numerical node attributes relying on a heterogeneous feature space.Our algorithm is framed within a message-passing scheme where the propagation parameters are estimated in advance. We also propose an alternative semi-supervised learning framework where the parameters and the missing node attributes are inferred in an end-to-end fashion.Experimental results on well-known knowledge graph datasets relay the effectiveness of our message-passing approach, which specifies the computational graph by the heterogeneity of the data.Finally, we study graph learning in multi-relational data domain.Unlike the existing structure inference methods, we aim at exploiting and combining each source of relational information provided by the data domain to learn the underlying graph of a set of observations.For this purpose, we employ a multi-layer graph representation which encodes multiple types of relationships between data entities. Then, we propose a mask learning method to infer a specific combination of the layers which reveals the structure of observations.Experiments conducted both on simulated and real-world data suggest that incorporating multi-relational domain knowledge enhances structure inference by boosting its adaptability to a variety of input data conditions.

EPFL2021

Representation Learning for Multi-relational Data

Eda Bayram

EPFL2021