From Appearance to Essence

Truth discovery has been widely studied in recent years as a fundamental means for resolving the conflicts in multi-source data. Although many truth discovery methods have been proposed based on different considerations and intuitions, investigations show that no single method consistently outperforms the others. To select the right truth discovery method for a specific application scenario, it becomes essential to evaluate and compare the performance of different methods. A drawback of current research efforts is that they commonly assume the availability of certain ground truth for the evaluation of methods. However, the ground truth may be very limited or even impossible to obtain, rendering the evaluation biased. In this article, we present CompTruthHyp, a generic approach for comparing the performance of truth discovery methods without using ground truth. In particular, our approach calculates the probability of observations in a dataset based on the output of different methods. The probability is then ranked to reflect the performance of these methods. We review and compare 12 representative truth discovery methods and consider both single-valued and multi-valued objects. The empirical studies on both real-world and synthetic datasets demonstrate the effectiveness of our approach for comparing truth discovery methods.Xiu Susie Fang, Quan Z. Sheng, Xianzhi Wang, Wei Emma Zhang, Anne H. H. Ngu, Jian Yan

Integrating Heterogeneous Features for Efficient Content-based Music Retrieval

Poster Trac

InMAF: Indexing Music Databases via Multiple Acoustic Features

System Demo Trac

SmartVote: a full-fledged graph-based model for multi-valued truth discovery

© 2018, Springer Science+Business Media, LLC, part of Springer Nature. In the era of Big Data, truth discovery has emerged as a fundamental research topic, which estimates data veracity by determining the reliability of multiple, often conflicting data sources. Although considerable research efforts have been conducted on this topic, most current approaches assume only one true value for each object. In reality, objects with multiple true values widely exist and the existing approaches that cope with multi-valued objects still lack accuracy. In this paper, we propose a full-fledged graph-based model, SmartVote, which models two types of source relations with additional quantification to precisely estimate source reliability for effective multi-valued truth discovery. Two graphs are constructed and further used to derive different aspects of source reliability (i.e., positive precision and negative precision) via random walk computations. Our model incorporates four important implications, including two types of source relations, object popularity, loose mutual exclusion, and long-tail phenomenon on source coverage, to pursue better accuracy in truth discovery. Empirical studies on two large real-world datasets demonstrate the effectiveness of our approach

GrandBase: generating actionable knowledge from Big Data

Purpose This paper aims to propose a system for generating actionable knowledge from Big Data and use this system to construct a comprehensive knowledge base (KB), called GrandBase. Design/methodology/approach In particular, this study extracts new predicates from four types of data sources, namely, Web texts, Document Object Model (DOM) trees, existing KBs and query stream to augment the ontology of the existing KB (i.e. Freebase). In addition, a graph-based approach to conduct better truth discovery for multi-valued predicates is also proposed. Findings Empirical studies demonstrate the effectiveness of the approaches presented in this study and the potential of GrandBase. The future research directions regarding GrandBase construction and extension has also been discussed. Originality/value To revolutionize our modern society by using the wisdom of Big Data, considerable KBs have been constructed to feed the massive knowledge-driven applications with Resource Description Framework triples. The important challenges for KB construction include extracting information from large-scale, possibly conflicting and different-structured data sources (i.e. the knowledge extraction problem) and reconciling the conflicts that reside in the sources (i.e. the truth discovery problem). Tremendous research efforts have been contributed on both problems. However, the existing KBs are far from being comprehensive and accurate: first, existing knowledge extraction systems retrieve data from limited types of Web sources; second, existing truth discovery approaches commonly assume each predicate has only one true value. In this paper, the focus is on the problem of generating actionable knowledge from Big Data. A system is proposed, which consists of two phases, namely, knowledge extraction and truth discovery, to construct a broader KB, called GrandBase