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Regular Paper Issue
Enriching Context Information for Entity Linking with Web Data
Journal of Computer Science and Technology 2020, 35 (4): 724-738
Published: 27 July 2020

Entity linking (EL) is the task of determining the identity of textual entity mentions given a predefined knowledge base (KB). Plenty of existing efforts have been made on this task using either “local” information (contextual information of the mention in the text), or “global” information (relations among candidate entities). However, either local or global information might be insufficient especially when the given text is short. To get richer local and global information for entity linking, we propose to enrich the context information for mentions by getting extra contexts from the web through web search engines (WSE). Based on the intuition above, two novel attempts are made. The first one adds web-searched results into an embedding-based method to expand the mention’s local information, where we try two different methods to help generate high-quality web contexts: one is to apply the attention mechanism and the other is to use the abstract extraction method. The second one uses the web contexts to extend the global information, i.e., finding and utilizing more extra relevant mentions from the web contexts with a graph-based model. Finally, we combine the two models we propose to use both extended local and global information from the extra web contexts. Our empirical study based on six real-world datasets shows that using extra web contexts to extend the local and the global information could effectively improve the F1 score of entity linking.

Regular Paper Issue
An Efficient Framework for Multiple Subgraph Pattern Matching Models
Journal of Computer Science and Technology 2019, 34 (6): 1185-1202
Published: 22 November 2019

With the popularity of storing large data graph in cloud, the emergence of subgraph pattern matching on a remote cloud has been inspired. Typically, subgraph pattern matching is defined in terms of subgraph isomorphism, which is an NP-complete problem and sometimes too strict to find useful matches in certain applications. And how to protect the privacy of data graphs in subgraph pattern matching without undermining matching results is an important concern. Thus, we propose a novel framework to achieve the privacy-preserving subgraph pattern matching in cloud. In order to protect the structural privacy in data graphs, we firstly develop a k-automorphism model based method. Additionally, we use a cost-model based label generalization method to protect label privacy in both data graphs and pattern graphs. During the generation of the k-automorphic graph, a large number of noise edges or vertices might be introduced to the original data graph. Thus, we use the outsourced graph, which is only a subset of a k-automorphic graph, to answer the subgraph pattern matching. The efficiency of the pattern matching process can be greatly improved in this way. Extensive experiments on real-world datasets demonstrate the high efficiency of our framework.

Regular Paper Issue
Scalable and Adaptive Joins for Trajectory Data in Distributed Stream System
Journal of Computer Science and Technology 2019, 34 (4): 747-761
Published: 19 July 2019

As a fundamental operation in LBS (location-based services), the trajectory similarity of moving objects has been extensively studied in recent years. However, due to the increasing volume of moving object trajectories and the demand of interactive query performance, the trajectory similarity queries are now required to be processed on massive datasets in a real-time manner. Existing work has proposed distributed or parallel solutions to enable large-scale trajectory similarity processing. However, those techniques cannot be directly adapted to the real-time scenario as it is likely to generate poor balancing performance when workload variance occurs on the incoming trajectory stream. In this paper, we propose a new workload partitioning framework, ART (Adaptive Framework for Real-Time Trajectory Similarity), which introduces practical algorithms to support dynamic workload assignment for RTTS (real-time trajectory similarity). Our proposal includes a processing model tailored for the RTTS scenario, a load balancing framework to maximize throughput, and an adaptive data partition manner designed to cut off unnecessary network cost. Based on this, our model can handle the large-scale trajectory similarity in an on-line scenario, which achieves scalability, effectiveness, and efficiency by a single shot. Empirical studies on synthetic data and real-world stream applications validate the usefulness of our proposal and prove the huge advantage of our approach over state-of-the-art solutions in the literature.

Regular Paper Issue
A Generative Model Approach for Geo-Social Group Recommendation
Journal of Computer Science and Technology 2018, 33 (4): 727-738
Published: 13 July 2018

With the development and prevalence of online social networks, there is an obvious tendency that people are willing to attend and share group activities with friends or acquaintances. This motivates the study on group recommendation, which aims to meet the needs of a group of users, instead of only individual users. However, how to aggregate different preferences of different group members is still a challenging problem: 1) the choice of a member in a group is influenced by various factors, e.g., personal preference, group topic, and social relationship; 2) users have different influences when in different groups. In this paper, we propose a generative geo-social group recommendation model (GSGR) to recommend points of interest (POIs) for groups. Specifically, GSGR well models the personal preference impacted by geographical information, group topics, and social influence for recommendation. Moreover, when making recommendations, GSGR aggregates the preferences of group members with different weights to estimate the preference score of a group to a POI. Experimental results on two datasets show that GSGR is effective in group recommendation and outperforms the state-of-the-art methods.

Regular Paper Issue
Discovering Functional Organized Point of Interest Groups for Spatial Keyword Recommendation
Journal of Computer Science and Technology 2018, 33 (4): 697-710
Published: 13 July 2018

A point of interest (POI) is a specific point location that someone may find useful. With the development of urban modernization, a large number of functional organized POI groups (FOPGs), such as shopping malls, electronic malls, and snacks streets, are springing up in the city. They have a great influence on people’s lives. We aim to discover functional organized POI groups for spatial keyword recommendation because FOPGs-based recommendation is superior to POIs-based recommendation in efficiency and flexibility. To discover FOPGs, we design clustering algorithms to obtain organized POI groups (OPGs) and utilize OPGs-LDA (Latent Dirichlet Allocation) model to reveal functions of OPGs for further recommendation. To the best of our knowledge, we are the first to study functional organized POI groups which have important applications in urban planning and social marketing.

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