A semantics-based approach to multi-source heterogeneous information fusion in the internet of things

Wang, Feng; Hu, Liang; Zhou, Jin; Hu, Jiejun; Zhao, Kuo

doi:10.1007/s00500-015-1899-7

Cited by 28 publications

(16 citation statements)

References 22 publications

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“…These problems in IoT data integration have motivated many researchers to build multi-store systems [11]. Prior work on multi-store systems can be divided into four categories: (1) the warehousing approach [12], (2) the federated or virtual integration approach [13]- [16], (3) the specialized approach [17], and (4) the schema-less approach [18], [19]. The warehousing approach integrates data by materializing combined data into one storage repository called a warehouse.…”

Section: Introductionmentioning

confidence: 99%

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

et al. 2020

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The growing number of connected Internet of Things (IoT) devices has increased the necessity for processing IoT data from multiple heterogeneous data stores. IoT data integration is a challenging problem owing to the heterogeneity of data stores in terms of their query language, data models, and schemas. In this paper, we propose a multi-store query system for IoT data called MusQ, where users can formulate join operation queries for heterogeneous data sources. To reconcile the heterogeneity between source schemas of IoT data stores, we extract a global schema from local source schemas semi-automatically by applying schema-matching and schema-mapping steps. In order to minimize the burden on the user to understand the finer details of various query languages, we define a unified query language called the multi-store query language (MQL), which follows a subset of the Datalog grammar. Thus, users can easily retrieve IoT data from multiple heterogeneous sources with MQL queries. As the three MQL query-processing join algorithms are based on a mediator-wrapper approach, MusQ performs efficient data integration over significant volumes of IoT data from multiple stores. We conduct extensive experiments to evaluate the performance of the MusQ system using a synthetic and large real IoT data set for three different types of data stores (RDBMS, NoSQL, and HDFS). The experimental results demonstrate that MusQ is suitable, scalable, and efficient query processing for multiple heterogeneous IoT data stores. Those advantages of MusQ are important in several areas that involve complex IoT systems, such as smart city, healthcare, and energy management. INDEX TERMS Data management and analytics, Internet of Things, multi-store system, query processing, schema integration. FIGURE 2. Grammar of MQL queries.

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Section: Introductionmentioning

confidence: 99%

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

et al. 2020

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“…The main challenge is how to effectively integrate large-scale textual information with different structure from various sources to recognize the embellished misinformation with hidden characteristics. Recently, although people have proposed numerous heterogeneous information fusion methods [6][7][8][9][10][11][12], the necessary condition of these methods is that the structures of the heterogeneous information are specific and known before training and testing, which is impracticable to apply in the unpredictable web source. Fortunately, the emerging success on knowledge-graphbased inference may explain this problem.…”

Section: Introductionmentioning

confidence: 99%

Robust Reasoning Over Heterogeneous Textual Information for Fact Verification

Wang

Xia

et al. 2020

IEEE Access

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Automatic fact verification (FV) based on artificial intelligence is considered as a promising approach which can be used to identify misinformation distributed on the web. Even though previous FV using deep learning have made great achievements in single dataset (e.g., FEVER), the trained systems are unlikely to be capable of extracting evidence from heterogeneous web-sources and validating claims in accordance with evidence found on the Internet. Nevertheless, the heterogeneity covers abundant semantic information, which will help FV system identify misinformation in a more accurate way. The current work is the first attempt to make the combination of knowledge graph (KG) and graph neural network (GNN) to enhance the robustness of FV systems for heterogeneous information. As a result, it can be generalized to multi-domain datasets after training on a sufficient single one. To make information update and aggregate well on the collaborative graph, the present study proposes a double graph attention network (DGAT) framework which recursively propagates the embeddings from a node's neighbors to refine the node's embedding as well as applies an attention mechanism to classify the importance of the neighbors. We train and evaluate our system on FEVER, a single and benchmark dataset for FV, and then re-evaluate our system on UKP Snopes Corpus, a new richly annotated corpus for FV tasks on the basis of heterogeneous web sources. According to experimental results, although DGAT has no excellent advantages in a single dataset, it shows outstanding performance in more realistic and multi-domain datasets. Moreover, the current study also provides a feasible method for deep learning to have the ability to infer heterogeneous information robustly.

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“…Semantic similarity is applied in plentiful applications about artificial intelligence and computational linguistics, such as word sense disambiguation, information retrieval, knowledge acquisition and natural language processing [1][2][3]. But most research on Semantic Computing (SC) are devoted to the following three fields: (1) formal models for theoretical foundations of SC, such as description logics, ontology reasoning and answer set programming [4,5]; (2) fundamental languages and technologies for interoperability and reuse of information including RDF, RDFS, the OWL family of languages, the WSML family of languages, and SPARQL [6,7]; (3) some applications of SC [8][9][10]. As a result, "semantic computing" refers to computational implementations of semantic reasoning (e.g., ontology reasoning, rule reasoning, semantic query and semantic search) but it is not from the perspective of the formal theory of computation.…”

Section: Introductionmentioning

confidence: 99%

A Formal Model for Semantic Computing Based on Generalized Probabilistic Automata

Huang

Wasti

Wei

et al. 2019

Entropy

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In most previous research, “semantic computing” refers to computational implementations of semantic reasoning. It lacks support from the formal theory of computation. To provide solid foundations for semantic computing, researchers propose a different understanding of semantic computing based on finite automata. This approach provides a computer theoretical approach to semantic computing. But finite automata are not capable enough to deal with imprecise knowledge. Therefore, in this paper, we provide foundations for semantic computing based on probabilistic automata. Even though traditional probabilistic automata can handle imprecise knowledge, their limitation resides in their being defined on a fixed finite input alphabet. This deeply restricts the abilities of automata. In this paper, we rebuild traditional probabilistic automata for semantic computing. Furthermore, our new probabilistic automata are robust enough to handle any alphabet as input. They have better performances in many applications. We provide an application for weather forecasting, a domain for which traditional probabilistic automata are not effective due to their finite input alphabet. Our new probabilistic automata can overcome these limitations.

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A semantics-based approach to multi-source heterogeneous information fusion in the internet of things

Cited by 28 publications

References 22 publications

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

MusQ: A Multi-Store Query System for IoT Data Using a Datalog-Like Language

Robust Reasoning Over Heterogeneous Textual Information for Fact Verification

A Formal Model for Semantic Computing Based on Generalized Probabilistic Automata

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