Text Data Integration

• URL: http://arxiv.org/abs/2603.27055v1
• Date: Sat, 28 Mar 2026 00:03:41 GMT
• Title: Text Data Integration
• Abstract summary: In this chapter, we make the case for the integration of textual data, to later present its challenges, state of the art and open problems.<n>Until now, most data integration systems have leaned on only combining structured data sources.
• Score: 2.70201105073482
• License: http://creativecommons.org/licenses/by/4.0/
• Abstract: Data comes in many forms. From a shallow perspective, they can be viewed as being either in structured (e.g., as a relation, as key-value pairs) or unstructured (e.g., text, image) formats. So far, machines have been fairly good at processing and reasoning over structured data that follows a precise schema. However, the heterogeneity of data poses a significant challenge on how well diverse categories of data can be meaningfully stored and processed. Data Integration, a crucial part of the data engineering pipeline, addresses this by combining disparate data sources and providing unified data access to end-users. Until now, most data integration systems have leaned on only combining structured data sources. Nevertheless, unstructured data (a.k.a. free text) also contains a plethora of knowledge waiting to be utilized. Thus, in this chapter, we firstly make the case for the integration of textual data, to later present its challenges, state of the art and open problems.

Related papers

• Generative Data Transformation: From Mixed to Unified Data [57.84692191369066]
  textscTaesar is a emphdata-centric framework for textbftarget-textbfal textbfregeneration.<n>It encodes cross-domain context into target sequences, enabling standard models to learn intricate dependencies without complex fusion architectures.
  arXiv  Detail & Related papers  (2026-02-26T08:30:09Z)
• An Integrated Data Processing Framework for Pretraining Foundation Models [57.47845148721817]
  Researchers and practitioners often have to manually curate datasets from difference sources. We propose a data processing framework that integrates a Processing Module and an Analyzing Module. The proposed framework is easy to use and highly flexible.
  arXiv  Detail & Related papers  (2024-02-26T07:22:51Z)
• Privacy-Preserving Graph Machine Learning from Data to Computation: A Survey [67.7834898542701]
  We focus on reviewing privacy-preserving techniques of graph machine learning. We first review methods for generating privacy-preserving graph data. Then we describe methods for transmitting privacy-preserved information.
  arXiv  Detail & Related papers  (2023-07-10T04:30:23Z)
• Cross Modal Data Discovery over Structured and Unstructured Data Lakes [5.270224494298927]
  Organizations are collecting increasingly large amounts of data for data driven decision making. These data are often dumped into a centralized repository, consisting of thousands of structured and unstructured datasets. Perversely, such mixture of datasets makes the problem of discovering elements relevant to a user's query or an analytical task very challenging.
  arXiv  Detail & Related papers  (2023-06-01T17:34:42Z)
• A Comprehensive Survey of Dataset Distillation [73.15482472726555]
  It has become challenging to handle the unlimited growth of data with limited computing power. Deep learning technology has developed unprecedentedly in the last decade. This paper provides a holistic understanding of dataset distillation from multiple aspects.
  arXiv  Detail & Related papers  (2023-01-13T15:11:38Z)
• ASDOT: Any-Shot Data-to-Text Generation with Pretrained Language Models [82.63962107729994]
  Any-Shot Data-to-Text (ASDOT) is a new approach flexibly applicable to diverse settings. It consists of two steps, data disambiguation and sentence fusion. Experimental results show that ASDOT consistently achieves significant improvement over baselines.
  arXiv  Detail & Related papers  (2022-10-09T19:17:43Z)
• Graph integration of structured, semistructured and unstructured data for data journalism [4.508924138721326]
  We describe a complete approach for integrating dynamic sets of heterogeneous datasets. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.
  arXiv  Detail & Related papers  (2020-12-16T09:59:27Z)
• Partially-Aligned Data-to-Text Generation with Distant Supervision [69.15410325679635]
  We propose a new generation task called Partially-Aligned Data-to-Text Generation (PADTG) It is more practical since it utilizes automatically annotated data for training and thus considerably expands the application domains. Our framework outperforms all baseline models as well as verify the feasibility of utilizing partially-aligned data.
  arXiv  Detail & Related papers  (2020-10-03T03:18:52Z)
• Graph integration of structured, semistructured and unstructured data for data journalism [0.0]
  We describe a complete approach for integrating dynamic sets of heterogeneous data sources. Our approach is implemented within the ConnectionLens system; we validate it through a set of experiments.
  arXiv  Detail & Related papers  (2020-07-23T08:55:09Z)
• ENT-DESC: Entity Description Generation by Exploring Knowledge Graph [53.03778194567752]
  In practice, the input knowledge could be more than enough, since the output description may only cover the most significant knowledge. We introduce a large-scale and challenging dataset to facilitate the study of such a practical scenario in KG-to-text. We propose a multi-graph structure that is able to represent the original graph information more comprehensively.
  arXiv  Detail & Related papers  (2020-04-30T14:16:19Z)
• Towards an Integrated Platform for Big Data Analysis [4.5257812998381315]
  This paper presents the vision of an integrated plat-form for big data analysis that combines all these aspects. Main benefits of this approach are an enhanced scalability of the whole platform, a better parameterization of algorithms, and an improved usability during the end-to-end data analysis process.
  arXiv  Detail & Related papers  (2020-04-27T03:15:23Z)

This list is automatically generated from the titles and abstracts of the papers in this site.

This site does not guarantee the quality of this site (including all information) and is not responsible for any consequences.