物联网社交媒体大数据的异构语义众包融合方法

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本文主要探讨了在物联网（Internet of Things, IoT）背景下社交媒体的日益普及如何推动了语义信息在连接人类智慧与异构媒体大数据中的关键角色。"Crowdsourcing Semantic Fusion" (CSF) 是一种创新的方法，它利用社交网络的力量，通过众包的方式对来自各种不同来源、格式和类型的媒体大数据进行语义融合，以提升数据检索的效率和准确性。 CSF的关键在于将传统的单一源信息处理扩展到包括用户的集体智慧。在物联网环境中，大量的传感器、设备和应用产生的海量数据包含了丰富的多媒体内容，如图像、视频、音频和文本。这些数据在没有统一语义框架的情况下难以有效地整合和分析。通过CSF，用户不仅可以提供直接的数据，还可以通过他们的理解和解释，为这些数据赋予更深层次的意义，从而实现跨媒体的信息关联和知识挖掘。文章指出，为了实现这一目标，研究者们开发了一系列算法和技术，例如自然语言处理（NLP）、计算机视觉（CV）和机器学习（ML），来解析和理解社交媒体中的非结构化文本，以及识别和整合图像和视频中的隐含语义。此外，协作过滤和推荐系统也被用来发现用户的共同兴趣和行为模式，进一步优化语义融合过程。在实验部分，作者可能展示了在实际应用场景中，如智能家居、智能城市或工业物联网中的CSF技术如何提高搜索效率、减少噪声、增强决策支持和个性化服务。通过比较不同的融合策略和模型，研究结果展示了CSF在解决异构媒体大数据挑战方面的潜力。这篇研究论文于2017年7月16日接收，经过修订后于1月29日接受，并于1月31日在线发表在《信息融合》（Information Fusion）期刊上。关键词包括“众包计算”、“语义融合”、“社交媒体”、“大数据”和“物联网”。其研究成果对于物联网时代的数据管理、智能决策和用户参与具有重要意义，预示着未来在异构媒体大数据处理领域可能的发展趋势。

Information Fusion 37 (2017) 77–85

Contents lists available at ScienceDirect

Information Fusion

journal homepage: www.elsevier.com/locate/inffus

CSF: Crowdsourcing semantic fusion for heterogeneous media big data

in the internet of things

Kehua Guo

a , b , ∗

, Yayuan Tang

, Peiyun Zhang

School of Information Science and Engineering, Central South University, Changsha, China

Key Laboratory of Information Processing and Intelligent Control of Fujian, Minjiang University, Fuzhou, China

School of Mathematics and Computer Science, Anhui Normal University, Wuhu, China

a r t i c l e i n f o

Article history:

Received 16 July 2016

Revised 24 January 2017

Accepted 29 January 2017

Available online 31 January 2017

Keywords:

Crowdsourcing computing

Semantic fusion

Social media

Big data

Internet of things

a b s t r a c t

With the rising popularity of social media in the context of environments based on the Internet of things

(IoT), semantic information has emerged as an important bridge to connect human intelligence with het-

erogeneous media big data. As a critical tool to improve media big data retrieval, semantic fusion encoun-

ters a number of challenges: the manual method is ineﬃcient, and the automatic approach is inaccurate.

To address these challenges, this paper proposes a solution called CSF (Crowdsourcing Semantic Fusion)

that makes full use of the collective wisdom of social users and introduces crowdsourcing computing to

semantic fusion. First, the correlation of cross-modal semantics is mined and the semantic objects are

normalized for fusion. Second, we employ the dimension reduction and relevance feedback approaches

to reduce non-principal components and noise. Finally, we research the storage and distribution mecha-

nism. Experiment results highlight the eﬃciency and accuracy of the proposed approach. The proposed

method is an effective and practical cross-modal semantic fusion and distribution mechanism for hetero-

geneous social media, provides a novel idea for social media semantic processing, and uses an interactive

visualization framework for social media knowledge mining and retrieval to improve semantic knowledge

and the effect of representation.

Introduction

With the growth of Information Technology in daily life, me-

dia data from the Internet of things (IoT) environments, such as

sensors, mobile devices, and Web pages, have exhibited a startling

pace of evolution. Due to the improvements in Internet socializa-

tion, IoT environments can produce, at a very low cost, a large

amount of social media information containing text, images, au-

dio, video, and so on [1] . Multi-source heterogeneous social media

data, which provide comprehensive knowledge and reﬂect the rel-

evant rules for the generation and dissemination of social events,

are gradually becoming a new form of knowledge representation

[2] . There is frequent demand to seek the knowledge needed and

explore its regulation through the massive amounts of existing so-

cial media data.

Semantic information has been an important bridge to connect

human intelligence to media data big data in the IoT environments.

However, due to the heterogeneity of social media, semantic infor-

mation is also heterogeneous. Semantic fusion has been a critical

∗

Corresponding author.

E-mail address: guokehua@csu.edu.cn (K. Guo).

tool to improve media big data retrieval. As a bridge that connects

low-level data expression to the human understanding of human

knowledge, it has emerged as an important component in improv-

ing the eﬃciency and accuracy of retrieval [3] . Current semantic

fusion methods can be roughly divided into two categories, and

both these categories have their respective advantages and disad-

vantages [4] . One is the manual method, which is executed by a

producer based on human understanding; its accuracy is higher,

but it consumes a large amount of manpower. The other is the au-

tomatic method, which mainly involves low-level data to fuse se-

mantic information without human intervention, and can be used

for large-scale data. However, there persists a substantial gap be-

tween computer and human intelligence, which renders accuracy

diﬃcult to ensure. Obtaining eﬃcient and accurate semantic infor-

mation has thus emerged as a research hotspot and a challenging

problem in recent years [5] .

With the increasing socialization of the IoT, crowdsourcing-

based computing has become an important research topic in many

areas, such as human-computer interaction, machine learning, and

artiﬁcial intelligence. Crowdsourcing employs an unknown public,

using a computer and the Internet, to complete a task that ma-

chines and individuals ﬁnd diﬃcult to process separately [6] . In

light of the advantages of collective wisdom, this paper introduces

http://dx.doi.org/10.1016/j.inffus.2017.01.008

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