scholarly journals GraphPrompt: Biomedical Entity Normalization Using Graph-based Prompt Templates

2021 ◽  
Author(s):  
Jiayou Zhang ◽  
Zhirui Wang ◽  
Shizhuo Zhang ◽  
Megh Manoj Bhalerao ◽  
Yucong Liu ◽  
...  
Keyword(s):  
Real World ◽  
Life Sciences ◽  
Cell Types ◽  
Learning Approach ◽  
Biomedical Data ◽  
Graph Structure ◽  
Holistic View ◽  
Real World Applications ◽  
Different Types ◽  
Unique Graph

Biomedical entity normalization unifies the language across biomedical experiments and studies, and further enables us to obtain a holistic view of life sciences. Current approaches mainly study the normalization of more standardized entities such as diseases and drugs, while disregarding the more ambiguous but crucial entities such as pathways, functions and cell types, hindering their real-world applications. To achieve biomedical entity normalization on these under-explored entities, we first introduce an expert-curated dataset OBO-syn encompassing 70 different types of entities and 2 million curated entity-synonym pairs. To utilize the unique graph structure in this dataset, we propose GraphPrompt, a prompt-based learning approach that creates prompt templates according to the graphs. GraphPrompt obtained 41.0% and 29.9% improvement on zero-shot and few-shot settings respectively, indicating the effectiveness of these graph-based prompt templates. We envision that our method GraphPrompt and OBO-syn dataset can be broadly applied to graph-based NLP tasks, and serve as the basis for analyzing diverse and accumulating biomedical data.

scholarly journals Semi-supervised User Profiling with Heterogeneous Graph Attention Networks

2019 ◽  
Author(s):  
Weijian Chen ◽  
Yulong Gu ◽  
Zhaochun Ren ◽  
Xiangnan He ◽  
Hongtao Xie ◽  
...  
Keyword(s):  
Social Networks ◽  
Real World ◽  
User Profiling ◽  
Graph Structure ◽  
User Characteristics ◽  
Attention Networks ◽  
User Behaviors ◽  
Real World Applications ◽  
Purchase Behaviors ◽  
Personal Interests

Aiming to represent user characteristics and personal interests, the task of user profiling is playing an increasingly important role for many real-world applications, e.g., e-commerce and social networks platforms. By exploiting the data like texts and user behaviors, most existing solutions address user profiling as a classification task, where each user is formulated as an individual data instance. Nevertheless, a user's profile is not only reflected from her/his affiliated data, but also can be inferred from other users, e.g., the users that have similar co-purchase behaviors in e-commerce, the friends in social networks, etc. In this paper, we approach user profiling in a semi-supervised manner, developing a generic solution based on heterogeneous graph learning. On the graph, nodes represent the entities of interest (e.g., users, items, attributes of items, etc.), and edges represent the interactions between entities. Our heterogeneous graph attention networks (HGAT) method learns the representation for each entity by accounting for the graph structure, and exploits the attention mechanism to discriminate the importance of each neighbor entity. Through such a learning scheme, HGAT can leverage both unsupervised information and limited labels of users to build the predictor. Extensive experiments on a real-world e-commerce dataset verify the effectiveness and rationality of our HGAT for user profiling.

A Framework for Developing Diagram Applications

2014 ◽  
pp. 339-348
Author(s):  
Wei Lai ◽  
Weidong Huang
Keyword(s):  
Case Studies ◽  
Real World ◽  
Graph Structure ◽  
Real World Applications ◽  
Automatic Layout ◽  
Uml Diagrams ◽  
Application Specific ◽  
Shape And Size

This chapter presents a framework for developing diagram applications. The diagrams refer to those graphs where nodes vary in shape and size used in real world applications, such as flowcharts, UML diagrams, and E-R diagrams. The framework is based on a model the authors developed for diagrams. The model is robust for diagrams and it can represent a wide variety of applications and support the development of powerful application-specific functions. The framework based on this model supports the development of automatic layout techniques for diagrams and the development of the linkage between the graph structure and applications. Automatic layout for diagrams is demonstrated and two case studies for diagram applications are presented.

scholarly journals A Noval Weighted Meta Graph Method for Classification in Heterogeneous Information Networks

2020 ◽  
Vol 10 (5) ◽  
pp. 1603
Author(s):  
Jinli Zhang ◽  
Tong Li ◽  
Zongli Jiang ◽  
Xiaohua Hu ◽  
Ali Jazayeri
Keyword(s):  
Real World ◽  
Structural Features ◽  
Information Networks ◽  
Heterogeneous Information ◽  
Heterogeneous Information Networks ◽  
Real World Applications ◽  
Different Types ◽  
Multiple Challenges ◽  
Real World Datasets

There has been increasing interest in the analysis and mining of Heterogeneous Information Networks (HINs) and the classification of their components in recent years. However, there are multiple challenges associated with distinguishing different types of objects in HINs in real-world applications. In this paper, a novel framework is proposed for the weighted Meta graph-based Classification of Heterogeneous Information Networks (MCHIN) to address these challenges. The proposed framework has several appealing properties. In contrast to other proposed approaches, MCHIN can fully compute the weights of different meta graphs and mine the latent structural features of different nodes by using these weighted meta graphs. Moreover, MCHIN significantly enlarges the training sets by introducing the concept of Extension Meta Graphs in HINs. The extension meta graphs are used to augment the semantic relationship among the source objects. Finally, based on the ranking distribution of objects, MCHIN groups the objects into pre-specified classes. We verify the performance of MCHIN on three real-world datasets. As is shown and discussed in the results section, the proposed framework can effectively outperform the baselines algorithms.

Modeling and using context

1998 ◽  
Vol 13 (2) ◽  
pp. 185-194 ◽  
Author(s):  
PATRICK BRÉZILLON ◽  
MARCOS CAVALCANTI
Keyword(s):  
Artificial Intelligence ◽  
Problem Solving ◽  
Real World ◽  
Point Of View ◽  
Theoretical Level ◽  
The Past ◽  
System Building ◽  
Real World Applications ◽  
Different Types ◽  
Model Context

The first International and Interdisciplinary Conference on Modeling and Using Context (CONTEXT-97) was held at Rio de Janeiro, Brazil on February 4–6 1997. This article provides a summary of the presentations and discussions during the three days with a focus on context in applications. The notion of context is far from defined, and is dependent in its interpretation on a cognitive science versus an engineering (or system building) point of view. However, the conference makes it possible to identify new trends in the formalization of context at a theoretical level, as well as in the use of context in real-world applications. Results presented at the conference are ascribed in the realm of the works on context over the past few years at specific workshops and symposia. The diversity of the attendees' origins (artificial intelligence, linguistics, philosophy, psychology, etc.) demonstrates that there are different types of context, not a unique one. For instance, logicians model context at the level of the knowledge representation and the reasoning mechanisms, while cognitive scientists consider context at the level of the interaction between two agents (i.e. two humans or a human and a machine). In the latter case, there are now strong arguments proving that one can speak of context only in reference to its use (e.g. context of an item or of a problem solving exercise). Moreover, there are different types of context that are interdependent. This makes it possible to understand why, despite the consensus on some context aspects, agreement on the notion of context is not yet achieved.

scholarly journals Learning from Data Heterogeneity: Algorithms and Applications

2017 ◽  
Author(s):  
Jingrui He
Keyword(s):  
Predictive Modeling ◽  
Real World ◽  
State Of The Art ◽  
Intrinsic Property ◽  
Learning From Data ◽  
Real World Applications ◽  
Different Types ◽  
Data Heterogeneity ◽  
Review State ◽  
Real World Problems

Nowadays, as an intrinsic property of big data, data heterogeneity can be seen in a variety of real-world applications, ranging from security to manufacturing, from healthcare to crowdsourcing. It refers to any inhomogeneity in the data, and can be present in a variety of forms, corresponding to different types of data heterogeneity, such as task/view/instance/oracle heterogeneity. As shown in previous work as well as our own work, learning from data heterogeneity not only helps people gain a better understanding of the large volume of data, but also provides a means to leverage such data for effective predictive modeling. In this paper, along with multiple real applications, we will briefly review state-of-the-art techniques for learning from data heterogeneity, and demonstrate their performance at addressing these real world problems.

Study of the Molecular Architecture of Keratin Filaments by Electron Microscopy

1982 ◽  
Vol 40 ◽  
pp. 118-119
Author(s):  
U. Aebi ◽  
P. Rew ◽  
T.-T. Sun
Keyword(s):  
Electron Microscopy ◽  
Structural Organization ◽  
Cell Types ◽  
Structural Features ◽  
Molecular Architecture ◽  
The Past ◽  
Keratin Filaments ◽  
Different Types ◽  
10 Nm Filaments ◽  
Different Cell Types

Various types of intermediate-sized (10-nm) filaments have been found and described in many different cell types during the past few years. Despite the differences in the chemical composition among the different types of filaments, they all yield common structural features: they are usually up to several microns long and have a diameter of 7 to 10 nm; there is evidence that they are made of several 2 to 3.5 nm wide protofilaments which are helically wound around each other; the secondary structure of the polypeptides constituting the filaments is rich in ∞-helix. However a detailed description of their structural organization is lacking to date.

Foundations of Blockchain Technology for Industrial and Societal Applications

2018 ◽  
Author(s):  
Shivika Narang ◽  
Praphul Chandra ◽  
Shweta Jain ◽  
Narahari Y
Keyword(s):  
Supply Chain ◽  
Data Structure ◽  
Real World ◽  
New Wave ◽  
Distributed Consensus ◽  
Wave Technology ◽  
Blockchain Technology ◽  
Different Types ◽  
Original Application

The blockchain concept forms the backbone of a new wave technology that promises to be deployed extensively in a wide variety of industrial and societal applications. In this article, we present the scientific foundations and technical strengths of this technology. Our emphasis is on blockchains that go beyond the original application to digital currencies such as bitcoin. We focus on the blockchain data structure and its characteristics; distributed consensus and mining; and different types of blockchain architectures. We conclude with a section on applications in industrial and societal settings, elaborating upon a few applications such as land registry ledger, tamper-proof academic transcripts, crowdfunding, and a supply chain B2B platform. We discuss what we believe are the important challenges in deploying the blockchain technology successfully in real-world settings.

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