Pay-as-you-go Data Integration: Experiences and Recurring Themes

Paton, Norman W.; Belhajjame, Khalid; Embury, Suzanne M.; Fernandes, Alvaro A. A.; Maskat, Ruhaila

doi:10.1007/978-3-662-49192-8_7

Cited by 8 publications

(6 citation statements)

References 26 publications

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“…There has been a growing interest in assimilating user feedback in data integration (see [2] for a general proposal, and [12] for a general methodology and recent work in the area). User feedback is even more important in environments that are characterized by large-scale heterogeneity and highly autonomous data sources as is the case in the Web of Data(WoD) [9].…”

Section: The Feedback Model: Assimilating User-provided Evidencementioning

confidence: 99%

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Alshukaili

Fernandes

Paton

2016

Lecture Notes in Computer Science

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Abstract. On-the-fly generation of integrated representations of Linked Data (LD) search results is challenging because it requires successfully automating a number of complex subtasks, such as structure inference and matching of both instances and concepts, each of which gives rise to uncertain outcomes. Such uncertainty is unavoidable given the semantically heterogeneous nature of web sources, including LD ones. This paper approaches the problem of structuring LD search results as an evidencebased one. In particular, the paper shows how one formalism (viz., probabilistic soft logic (PSL)) can be exploited to assimilate different sources of evidence in a principled way and to beneficial effect for users. The paper considers syntactic evidence derived from matching algorithms, semantic evidence derived from LD vocabularies, and user evidence, in the form of feedback. The main contributions are: sets of PSL rules that model the uniform assimilation of diverse kinds of evidence, an empirical evaluation of how the resulting PSL programs perform in terms of their ability to infer structure for integrating LD search results, and, finally, a concrete example of how populating such inferred structures for presentation to the end user is beneficial, besides enabling the collection of feedback whose assimilation further improves search result presentation.

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Section: The Feedback Model: Assimilating User-provided Evidencementioning

confidence: 99%

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Alshukaili

Fernandes

Paton

2016

Lecture Notes in Computer Science

Self Cite

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“…The limitations highlighted above have seriously hampered the development of end-to-end approaches for schema integration, which is a major need in practice, especially when facing Big Data [22,46]. To that end, we propose an all-encompassing approach that largely automates (essential for Big Data scenarios) schema integration.…”

Section: Introductionmentioning

confidence: 99%

Incremental schema integration for data wrangling via knowledge graphs

Flores

Rabbani

Nadal

et al. 2024

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Virtual data integration is the current approach to go for data wrangling in data-driven decision-making. In this paper, we focus on automating schema integration, which extracts a homogenised representation of the data source schemata and integrates them into a global schema to enable virtual data integration. Schema integration requires a set of well-known constructs: the data source schemata and wrappers, a global integrated schema and the mappings between them. Based on them, virtual data integration systems enable fast and on-demand data exploration via query rewriting. Unfortunately, the generation of such constructs is currently performed in a largely manual manner, hindering its feasibility in real scenarios. This becomes aggravated when dealing with heterogeneous and evolving data sources. To overcome these issues, we propose a fully-fledged semi-automatic and incremental approach grounded on knowledge graphs to generate the required schema integration constructs in four main steps: bootstrapping, schema matching, schema integration, and generation of system-specific constructs. We also present Nextia DI , a tool implementing our approach. Finally, a comprehensive evaluation is presented to scrutinize our approach.

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“…This example helps us to understand the meaning of relevant information for a user query. In the literature, several researches have acknowledged that users' preferences play an important role while answering query [17,25,32]. This work addresses the problem of returning relevant information to users from a dataspace system.…”

Section: Introductionmentioning

confidence: 99%

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

Singh

Pandey²,

Saxena³

et al. 2022

Preprint

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Dataspace systems cope with the problem of integrating a variety of data based on its structures and semantics such as structured, semi-structured, and unstructured data, and returns the best-effort or approximate answers to their users. The existing works on query answering in a dataspace system are content-based and paid attention to return the best answers to the users without taking care of their preferences. This paper aims to consider not only the content-based information but also the users' preferences while answering the users' queries. Therefore, we present a Collaborative Query-Answering Framework for a Research Article Dataspace (CQFaRAD) that helps to efficiently answer the users' queries and returns more prominent answers to them. In this work, we present a collaborative approach that adopts the advantages of existing content-based and users' preferences-based approaches. To achieve this task, we use the BERT model to represent our dataspace and users' query. We have validated our proposed approach on the research papers dataset available on Kaggle. The experimental results show that our approach works fairly well to return relevant information to the users.

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Pay-as-you-go Data Integration: Experiences and Recurring Themes

Cited by 8 publications

References 26 publications

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Structuring Linked Data Search Results Using Probabilistic Soft Logic

Incremental schema integration for data wrangling via knowledge graphs

CQFaRAD: Collaborative Query-Answering Framework for a Research Article Dataspace

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