Quoc Viet Hung Nguyen

Chat with Graph Search

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DISCLAIMER: The Graph Chatbot is not programmed to provide explicit or categorical answers to your questions. Rather, it transforms your questions into API requests that are distributed across the various IT services officially administered by EPFL. Its purpose is solely to collect and recommend relevant references to content that you can explore to help you answer your questions.

Data redundancy has been one of the most important problems in data-intensive applications such as data mining and machine learning. Removing data redundancy brings many benefits in efficient data updating, effective data storage, and error-free query proc ...

The heterogeneity of today's Web sources requires information retrieval (IR) systems to handle multi-modal queries. Such queries define a user's information needs by different data modalities, such as keywords, hashtags, user profiles, and other media. Rec ...

Maximal subgraph mining is increasingly important in various domains, including bioinformatics, genomics, and chemistry, as it helps identify common characteristics among a set of graphs and enables their classification into different categories. Existing ...

Data models capture the structure and characteristic properties of data entities, e.g., in terms of a database schema or an ontology. They are the backbone of diverse applications, reaching from information integration, through peer-to-peer systems and ele ...

What-if analysis is a data-intensive exploration to inspect how changes in a set of input parameters of a model influence some outcomes. It is motivated by a user trying to understand the sensitivity of a model to a certain parameter in order to reach a se ...

The Web became the central medium for valuable sources of information fusion applications. However, such user-generated resources are often plagued by inaccuracies and misinformation as a result of the inherent openness and uncertainty of the Web. While fi ...

Graph embedding aims at learning a vector-based representation of vertices that incorporates the structure of the graph. This representation then enables inference of graph properties. Existing graph embedding techniques, however, do not scale well to larg ...

Schema and ontology matching is a process of establishing correspondences between schema attributes and ontology concepts, for the purpose of data integration. Various commercial and academic tools have been developed to support this task. These tools prov ...

Queries to detect isomorphic subgraphs are important in graph-based data management. While the problem of subgraph isomorphism search has received considerable attention for the static setting of a single query, or a batch thereof, existing approaches do n ...

Participatory sensing has emerged as a new data collection paradigm, in which humans use their own devices (cell phone accelerometers, cameras, etc.) as sensors. This paradigm enables to collect a huge amount of data from the crowd for world-wide applicati ...

Network alignment is a method to align nodes that belong to the same entity from different networks. A well-known application of network alignment is to map user accounts from different social networks that belong to the same person. As network alignment h ...

Conceptual models such as database schemas, ontologies or process models have been established as a means for effective engineering of information systems. Yet, for complex systems, conceptual models are created by a variety of stakeholders, which calls fo ...

Participatory sensing has become a new data collection paradigm that leverages the wisdom of the crowd for big data applications without spending cost to buy dedicated sensors. It collects data from human sensors by using their own devices such as cell pho ...

Schema matching is the process of establishing correspondences between the attributes of database schemas for data integration purposes. Although several automatic schema matching tools have been developed, their results are often incomplete or erroneous. ...

In recent years, crowdsourcing has become essential in a wide range of Web applications. One of the biggest challenges of crowdsourcing is the quality of crowd answers as workers have wide-ranging levels of expertise and the worker community may contain fa ...

As the number of scientific papers getting published is likely to soar, most of modern paper management systems (e.g. ScienceWise, Mendeley, CiteULike) support tag-based retrieval. In that, each paper is associated with a set of \emph{tags}, allowing user ...

The Web constitutes a valuable source of information. In recent years, it fostered the construction of large-scale knowledge bases, such as Freebase, YAGO, and DBpedia. The open nature of the Web, with content potentially being generated by everyone, howev ...

The amount of information available on the Web has been growing dramatically, raising the importance of techniques for searching the Web. Recently, Web Tables emerged as a model, which enables users to search for information in a structured way. However, e ...

Crowdsourcing has been widely established as a means to enable human computation at large-scale, in particular for tasks that require manual labelling of large sets of data items. Answers obtained from heterogeneous crowd workers are aggregated to obtain a ...

Graph neural networks take node features and graph structure as input to build representations for nodes and graphs. While there are a lot of focus on GNN models, understanding the impact of node features and graph structure to GNN performance has received ...

Quoc Viet Hung Nguyen

Chat with Graph Search

Validating functional redundancy with mixed generative adversarial networks

Efficient and Effective Multi-Modal Queries Through Heterogeneous Network Embedding

Scalable maximal subgraph mining with backbone-preserving graph convolutions

Handling probabilistic integrity constraints in pay-as-you-go reconciliation of data models

What-if Analysis with Conflicting Goals: Recommending Data Ranges for Exploration

Maximal fusion of facts on the web with credibility guarantee

Scalable Robust Graph Embedding with Spark

Minimizing Human Effort in Reconciling Match Networks

Efficient Streaming Subgraph Isomorphism with Graph Neural Networks

Towards Enabling Probabilistic Databases for Participatory Sensing

A comparative study on network alignment techniques

Reconciling Matching Networks of Conceptual Models

Efficient User Guidance for Validating Participatory Sensing Data

Reconciling Schema Matching Networks Through Crowdsourcing

Minimizing Efforts in Validating Crowd Answers

Tag-based Paper Retrieval: Minimizing User Effort with Diversity Awareness

User Guidance for Efficient Fact Checking

Result Selection and Summarization for Web Table Search

Computing Crowd Consensus with Partial Agreement (Extended Abstract)

Nature vs. Nurture: Feature vs. Structure for Graph Neural Networks