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Exploring and Evaluating Attributes, Values, and Structures for Entity Alignment

Entity alignment (EA) aims at building a unified Knowledge Graph (KG) of rich content by linking the equivalent entities from various KGs. GNN-based EA methods present promising performances by modeling the KG structure defined by relation triples. …

Multi-modal Cooking Workflow Construction for Food Recipes

Understanding food recipe requires anticipating the implicit causal effects of cooking actions, such that the recipe can be converted into a graph describing the temporal workflow of the recipe. This is a non-trivial task that involves common-sense …

Semantic Graphs for Generating Deep Questions

This paper proposes the problem of Deep Question Generation (DQG), which aims to generate complex questions that require reasoning over multiple pieces of information of the input passage. In order to capture the global structure of the document and …

Expertise Style Transfer: A New Task Towards Better Communcation between Experts and Laymen

The curse of knowledge can impede communication between experts and laymen. We propose a new task of expertise style transfer and contribute a manually annotated dataset with the goal of alleviating such cognitive biases. Solving this task not only …

Hyperbolic Visual Embedding Learning for Zero-Shot Recognition

This paper proposes a Hyperbolic Visual Embedding Learning Network for zero-shot recognition. The network learns image embeddings in hyperbolic space, which is capable of preserving the hierarchical structure of semantic classes in low dimensions. …

Zero-shot Ingredient Recognition by Multi-Relational Graph Convolutional Network

Recognizing ingredients for a given dish image is at the core of automatic dietary assessment, attracting increasing attention from both industry and academia. Nevertheless, the task is challenging due to the difficulty of collecting and labeling …

Predicting Concept-based Research Trends with Rhetorical Framing

Applying data mining techniques to help researchers discover, understand, and predict research trends is a highly beneficial but challenging task. The existing researches mainly use topics extracted from literatures as objects to build predicting …

Course Concept Extraction in MOOCs via Embedding-Based Graph Propagation

Massive Open Online Courses (MOOCs), offering a new way to study online, are revolutionizing education. One challenging issue in MOOCs is how to design effective and fine-grained course concepts such that students with different backgrounds can grasp …

Prerequisite Relation Learning for Concepts in MOOCs

What prerequisite knowledge should students achieve a level of mastery before moving forward to learn subsequent coursewares? We study the extent to which the prerequisite relation between knowledge concepts in Massive Open Online Courses (MOOCs) can …

Domain Specific Cross-Lingual Knowledge Linking Based on Similarity Flooding

The global knowledge sharing makes large-scale multi-lingual knowledge bases an extremely valuable resource in the Big Data era. However, current mainstream multi-lingual ontologies based on online wikis still face the limited coverage of …