An Overview on XML Semantic Disambiguation from Unstructured Text to Semi-Structured Data: Background, Applications, and Ongoing Challenges

Tekli, Joe

doi:10.1109/tkde.2016.2525768

Cited by 42 publications

(12 citation statements)

References 156 publications

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“…At first, Tekli [66] found that, in the entertainment industry, the feedback given by the audience in form of large sentences and getting semantic meaning from XML documents is very challenging. Additionally, Sanyal, Bhadra, and Das [67] pointed out that, by using business intelligence tool sentence-similarity retrieved, the technique proposed for the IT Ecosystem has been adopted by business firms.…”

Section: Study Reference Domain Contributionsmentioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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As data size increases drastically, its variety also increases. Investigating such heterogeneous data is one of the most challenging tasks in information management and data analytics. The heterogeneity and decentralization of data sources affect data visualization and prediction, thereby influencing analytical results accordingly. Data harmonization (DH) corresponds to a field that unifies the representation of such a disparate nature of data. Over the years, multiple solutions have been developed to minimize the heterogeneity aspects and disparity in formats of big-data types. In this study, a systematic review of the literature was conducted to assess the state-of-the-art DH techniques. This study aimed to understand the issues faced due to heterogeneity, the need for DH and the techniques that deal with substantial heterogeneous textual datasets. The process produced 1355 articles, but among them, only 70 articles were found to be relevant through inclusion and exclusion criteria methods. The result shows that the heterogeneity of structured, semi-structured, and unstructured (SSU) data can be managed by using DH and its core techniques, such as text preprocessing, Natural Language Preprocessing (NLP), machine learning (ML), and deep learning (DL). These techniques are applied to many real-world applications centered on the information-retrieval domain. Several assessment criteria were implemented to measure the efficiency of these techniques, such as precision, recall, F-1, accuracy, and time. A detailed explanation of each research question, common techniques, and performance measures is also discussed. Lastly, we present readers with a detailed discussion of the existing work, contributions, and managerial and academic implications, along with the conclusion, limitations, and future research directions.

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Section: Study Reference Domain Contributionsmentioning

confidence: 99%

Data Harmonization for Heterogeneous Datasets: A Systematic Literature Review

et al. 2021

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“…We therefore consider the language of such labels as a narrow, specific subset of the LoD. Works on the disambiguation of data schemas and underlying data values (Tekli, 2016;Tagarelli et al, 2009) typically approach the problem from a point of view of lexical semantics-i.e. the label is considered as a bag of words or word meanings-and concentrate on the exploitation of the context provided by the tabular or tree-based data structure for the purposes of disambiguation.…”

Section: State Of the Artmentioning

confidence: 99%

Exploring the Language of Data

Bella

Gremes²,

Giunchiglia

2020

Proceedings of the 28th International Conference on Computational Linguistics

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We set out to uncover the unique grammatical properties of an important yet so far underresearched type of natural language text: that of short labels typically found within structured datasets. We show that such labels obey a specific type of abbreviated grammar that we call the Language of Data, with properties significantly different from the kinds of text typically addressed in computational linguistics and NLP, such as 'standard' written language or social media messages. We analyse orthography, parts of speech, and syntax over a large, bilingual, hand-annotated corpus of data labels collected from a variety of domains. We perform experiments on tokenisation, part-of-speech tagging, and named entity recognition over real-world structured data, demonstrating that models adapted to the Language of Data outperform those trained on standard text. These observations point in a new direction to be explored as future research, in order to develop new NLP tools and models dedicated to the Language of Data.

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“…They are usually in the form of texts, audio files, videos, photos, e-mail messages, web pages, and presentations. Unstructured data (Figure 4) represents around 80 % of big data [5]. For decades, databases have been the central hub of data.…”

Section: Understanding Big Datamentioning

confidence: 99%