T-Crowd: Effective Crowdsourcing for Tabular Data

Crowdsourcing employs human workers to solve computer-hard problems, such as data cleaning, entity resolution, and sentiment analysis. When crowdsourcing tabular data, e.g., the attribute values of an entity set, a worker's answers on the different attributes (e.g., the nationality and age of a celebrity star) are often treated independently. This assumption is not always true and can lead to suboptimal crowdsourcing performance. In this paper, we present the T-Crowd system, which takes into consideration the intricate relationships among tasks, in order to converge faster to their true values. Particularly, T-Crowd integrates each worker's answers on different attributes to effectively learn his/her trustworthiness and the true data values. The attribute relationship information is also used to guide task allocation to workers. Finally, T-Crowd seamlessly supports categorical and continuous attributes, which are the two main datatypes found in typical databases. Our extensive experiments on real and synthetic datasets show that T-Crowd outperforms state-of-the-art methods in terms of truth inference and reducing the cost of crowdsourcing

Active tag recommendation for interactive entity search : Interaction effectiveness and retrieval performance

We introduce active tag recommendation for interactive entity search, an approach that actively learns to suggest tags from preceding user interactions with the recommended tags. The approach utilizes an online reinforcement learning model and observes user interactions on the recommended tags to reward or penalize the model. Active tag recommendation is implemented as part of a realistic search engine indexing a large collection of movie data. The approach is evaluated in task-based user experiments comparing a complete search system enhanced with active tag recommendation to a control system in which active tag recommendation is not available. In the experiment, participants (N = 45) performed search tasks on the movie domain and the corresponding search interactions, information selections, and entity rankings were logged and analyzed. The results show that active tag recommendation (1) improves the ranking of entities compared to written-query interaction, (2) increases the amount of interaction and effectiveness of interactions to rank entities that end up being selected in a task, and (3) reduces, but does not substitute, the need for written-query interaction (4) without compromising task execution time. The results imply that active learning for search support can help users to interact with entity search systems by reducing the need for writing queries and improve search outcomes without compromising the time used for searching.Peer reviewe

A Survey on Temporal Knowledge Graph Completion: Taxonomy, Progress, and Prospects

Temporal characteristics are prominently evident in a substantial volume of knowledge, which underscores the pivotal role of Temporal Knowledge Graphs (TKGs) in both academia and industry. However, TKGs often suffer from incompleteness for three main reasons: the continuous emergence of new knowledge, the weakness of the algorithm for extracting structured information from unstructured data, and the lack of information in the source dataset. Thus, the task of Temporal Knowledge Graph Completion (TKGC) has attracted increasing attention, aiming to predict missing items based on the available information. In this paper, we provide a comprehensive review of TKGC methods and their details. Specifically, this paper mainly consists of three components, namely, 1)Background, which covers the preliminaries of TKGC methods, loss functions required for training, as well as the dataset and evaluation protocol; 2)Interpolation, that estimates and predicts the missing elements or set of elements through the relevant available information. It further categorizes related TKGC methods based on how to process temporal information; 3)Extrapolation, which typically focuses on continuous TKGs and predicts future events, and then classifies all extrapolation methods based on the algorithms they utilize. We further pinpoint the challenges and discuss future research directions of TKGC