Yantao Jia

ECAI Conference 2020 Conference Paper

CIDetector: Semi-Supervised Method for Multi-Topic Confidential Information Detection

• Jianguo Jiang
• Yue Lu
• Min Yu 0001
• Yantao Jia
• Jiafeng Guo
• Chao Liu 0020
• Weiqing Huang

Confidential information firewalling with text classifier is to identify the text containing confidential information whose publication might be harmful to national security, business trade, or personal life. Traditional methods, e. g. , listing a set of suspicious keywords together with regular-expression based filter, fail to solve the multi-topic phenomenon, i. e. , one text containing the confidential information with different topics. In this paper, we propose a semi-supervised method, CIDetector, for multi-topic confidential information detection. We introduce coarse confidential polarity as prior knowledge into word embeddings, which can regularize the distribution of words to have a clear task classification boundary. Then we introduce a multi-attention network classifier to extract task-related features and model dependencies between features for multi-topic classification. Experiments are conducted by real-world data from WikiLeaks and demonstrated the superiority of our proposed method.

AAAI Conference 2020 Short Paper

Link Prediction between Group Entities in Knowledge Graphs (Student Abstract)

• Jialin Su
• Yuanzhuo Wang
• Xiaolong Jin
• Yantao Jia
• Xueqi Cheng

Link prediction in knowledge graphs (KGs) aims at predicting potential links between entities in KGs. Existing knowledge graph embedding (KGE) based methods represent individual entities and links in KGs as vectors in low-dimension space. However, these methods focus mainly on the link prediction of individual entities, yet neglect that between group entities, which exist widely in real-world KGs. In this paper, we propose a KGE based method, called GTransA, for link prediction between group entities in a heterogeneous network by integrating individual entity links into group entity links during prediction. Experiments show that GTransA decreases mean rank by 5. 4%, compared to TransA.

AAAI Conference 2018 Short Paper

Path-Based Attention Neural Model for Fine-Grained Entity Typing

• Denghui Zhang
• Manling Li
• Pengshan Cai
• Yantao Jia
• Yuanzhuo Wang

Fine-grained entity typing aims to assign entity mentions in the free text with types arranged in a hierarchical structure. It suffers from the label noise in training data generated by distant supervision. Although recent studies use many features to prune wrong label ahead of training, they suffer from error propagation and bring much complexity. In this paper, we propose an end-to-end typing model, called the path-based attention neural model (PAN), to learn a noise-robust performance by leveraging the hierarchical structure of types. Experiments on two data sets demonstrate its effectiveness.

AAAI Conference 2016 Conference Paper

Locally Adaptive Translation for Knowledge Graph Embedding

• Yantao Jia
• Yuanzhuo Wang
• Hailun Lin
• Xiaolong Jin
• Xueqi Cheng

Knowledge graph embedding aims to represent entities and relations in a large-scale knowledge graph as elements in a continuous vector space. Existing methods, e. g. , TransE and TransH, learn embedding representation by defining a global margin-based loss function over the data. However, the optimal loss function is determined during experiments whose parameters are examined among a closed set of candidates. Moreover, embeddings over two knowledge graphs with different entities and relations share the same set of candidate loss functions, ignoring the locality of both graphs. This leads to the limited performance of embedding related applications. In this paper, we propose a locally adaptive translation method for knowledge graph embedding, called TransA, to find the optimal loss function by adaptively determining its margin over different knowledge graphs. Experiments on two benchmark data sets demonstrate the superiority of the proposed method, as compared to the-state-of-the-art ones.

TIST Journal 2016 Journal Article

Location Prediction

• Yantao Jia
• Yuanzhuo Wang
• Xiaolong Jin
• Xueqi Cheng

In social networks, predicting a user’s location mainly depends on those of his/her friends, where the key lies in how to select his/her most influential friends. In this article, we analyze the theoretically maximal accuracy of location prediction based on friends’ locations and compare it with the practical accuracy obtained by the state-of-the-art location prediction methods. Upon observing a big gap between the theoretical and practical accuracy, we propose a new strategy for selecting influential friends in order to improve the practical location prediction accuracy. Specifically, several features are defined to measure the influence of the friends on a user’s location, based on which we put forth a sequential random-walk-with-restart procedure to rank the friends of the user in terms of their influence. By dynamically selecting the top N most influential friends of the user per time slice, we develop a temporal-spatial Bayesian model to characterize the dynamics of friends’ influence for location prediction. Finally, extensive experimental results on datasets of real social networks demonstrate that the proposed influential friend selection method and temporal-spatial Bayesian model can significantly improve the accuracy of location prediction.

AAAI Conference 2016 Conference Paper

Predicting Links and Their Building Time: A Path-Based Approach

• Manling Li
• Yantao Jia
• Yuanzhuo Wang
• Zeya Zhao
• Xueqi Cheng

AAAI Conference 2014 Conference Paper

Content-Structural Relation Inference in Knowledge Base

• Zeya Zhao
• Yantao Jia
• Yuanzhuo Wang

Relation inference between concepts in knowledge base has been extensively studied in recent years. Previous methods mostly apply the relations in the knowledge base, without fully utilizing the contents, i. e. , the attributes of concepts in knowledge base. In this paper, we propose a contentstructural relation inference method (CSRI) which integrates the content and structural information between concepts for relation inference. Experiments on data sets show that CSRI obtains 15% improvement compared with the state-of-theart methods.

AAAI Conference 2014 Conference Paper

LSDH: A Hashing Approach for Large-Scale Link Prediction in Microblogs

• Dawei Liu
• Yuanzhuo Wang
• Yantao Jia
• Jingyuan Li
• Zhihua Yu

One challenge of link prediction in online social networks is the large scale of many such networks. The measures used by existing work lack a computational consideration in the large scale setting. We propose the notion of social distance in a multi-dimensional form to measure the closeness among a group of people in Microblogs. We proposed a fast hashing approach called Locality-sensitive Social Distance Hashing (LSDH), which works in an unsupervised setup and performs approximate near neighbor search without high-dimensional distance computation. Experiments were applied over a Twitter dataset and the preliminary results testified the effectiveness of LSDH in predicting the likelihood of future associations between people.