Integrating Semi-structured Data into Business Applications: A Web Intelligence Example

Baumgartner, Robert; Frölich, Oliver; Gottlob, Georg; Herzog, Marcus; Lehmann, Peter

doi:10.1007/11590019_54

Cited by 5 publications

(4 citation statements)

References 10 publications

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“…In this sense, web data is aggregated, or restructured, from multiple primary data sources (the web documents, or pages, from the World Wide Web). A recent survey by Ferrar et al in 2014 (Ferrara et al., 2014; Baumgartner et al., 2005) provides an overview and taxonomy of methods for web data extraction and discusses the range of uses that such data is applied to. The authors underscore this data's unique potential (based on availability and quantity) to offer enhanced scope for analysis and deep examination of complex social and economic phenomena.…”

Section: Related Workmentioning

confidence: 99%

“…Data's messiness and its structure have multiple challenges when it comes to extracting relevant facts and information. The main question in this area is how this data can be extracted, shaped into a semantically useful structures, and integrated into a system that allows further exploration and analysis (Baumgartner et al., 2005). This requires several trade-offs between automation and accuracy as well as challenges in the content structure, variability and privacy (Ferrara et al., 2014; Zaveri et al., 2016; Paulheim, 2017).…”

Section: Related Workmentioning

confidence: 99%

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The UK Online Gender Audit 2018: A comprehensive audit of gender within the UK's online environment

Huluba¹,

Kingdon²,

McLaren³

2018

Heliyon

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Gender inequality has exploded as a recent issue within mainstream media across US and UK cultural commentary. High-profile scandals of sexual harassment and gender pay differences have focused attention on the on-going disparity between sexes and political status. This paper presents a novel experiment in the application of so-called “big data” to analyse gender inequality. Using Artificial Intelligence (AI) techniques in the form of Natural Language Processing, a web crawler is used to audit the whole.uk online domain, and to measure the United Kingdom's (UK's) online economic presence for gender representation in terms of: prominence, job roles, and leadership within and across economic sectors. The procedure scans over 200 million web pages, and harvests 157,032 organisations and over 2.3 million people. The results reveal material bias (60%+) towards the representation of men over the majority of economic sectors, and across representation of power and status within job roles and professional titles. The experiment highlights not only new levels of gender bias but also the use of the Internet as a valuable source of plentiful data for social and economic analysis.

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Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

The UK Online Gender Audit 2018: A comprehensive audit of gender within the UK's online environment

Huluba¹,

Kingdon²,

McLaren³

2018

Heliyon

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“…This property allows data with disparate structure to be integrated effectively. Typical drivers for semistructured data have been worldwide companies and the web, where the usage of semistructured data has increased enormously [4,46]. For example, semistructured data can be used in a worldwide company to pull together disparate data stored in different repositories for company-wide reports, analysis and decision making.…”

Section: Introductionmentioning

confidence: 99%

Theorem prover approach to semistructured data design

Lee

Dobbie

Sun

et al. 2010

Form Methods Syst Des

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The wide adoption of semistructured data has created a growing need for effective ways to ensure the correctness of its organization. One effective way to achieve this goal is through formal specification and automated verification. This paper presents a theorem proving approach towards verifying that a particular design or organization of semistructured data is correct. We formally specify the semantics of the Object Relationship Attribute data model for Semistructured Data (ORA-SS) modeling notation and its correctness criteria for semistructured data normalization using the Prototype Verification System (PVS). The result is that effective verification on semistructured data models and their normalization can be carried out using the PVS theorem prover.

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“…The problem has also been addressed in semi-structured query languages(Baumgartner et al, 2005;Björklund et al, 2011) and in description logics(Baader et al, 2009). Still the focus here is on query containment for relational databases.…”

mentioning

confidence: 99%

Evaluating an Automata Approach to Query Containment

Minock

2017

Proceedings of the 13th International Conference on Finite State Methods and Natural Language Processing (FSMNLP 2017

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Given two queries Q super and Q sub , query containment is the problem of determining if Q sub (D) ⊆ Q super (D) for all databases D. This problem has long been explored, but to our knowledge no one has empirically evaluated a straightforward application of finite state automata to the problem. We do so here, covering the case of conjunctive queries with limited set conditions. We evaluate an implementation of our approach against straightforward implementations of both the canonical database and theorem proving approaches. Our implementation outperforms theorem proving on a natural language interface corpus over a photo/video domain. It also outperforms the canonical database implementation on single relation queries with large set conditions.

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Integrating Semi-structured Data into Business Applications: A Web Intelligence Example

Cited by 5 publications

References 10 publications

The UK Online Gender Audit 2018: A comprehensive audit of gender within the UK's online environment

The UK Online Gender Audit 2018: A comprehensive audit of gender within the UK's online environment

Theorem prover approach to semistructured data design

Evaluating an Automata Approach to Query Containment

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