D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF

Vu, Binh Duong; Pujara, Jay; Knoblock, Craig A.

doi:10.1145/3360901.3364449

Cited by 15 publications

(21 citation statements)

References 3 publications

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“…To make the mapping process easier and less laborious, we developed a language called D-REPR [90] for describing datasets. Users build a D-REPR model for a dataset through four steps.…”

Section: D-repr Representation Languagementioning

confidence: 99%

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Gil

Garijo

Khider

et al. 2021

ACM Trans. Interact. Intell. Syst.

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Major societal and environmental challenges involve complex systems that have diverse multi-scale interacting processes. Consider, for example, how droughts and water reserves affect crop production and how agriculture and industrial needs affect water quality and availability. Preventive measures, such as delaying planting dates and adopting new agricultural practices in response to changing weather patterns, can reduce the damage caused by natural processes. Understanding how these natural and human processes affect one another allows forecasting the effects of undesirable situations and study interventions to take preventive measures. For many of these processes, there are expert models that incorporate state-of-the-art theories and knowledge to quantify a system's response to a diversity of conditions. A major challenge for efficient modeling is the diversity of modeling approaches across disciplines and the wide variety of data sources available only in formats that require complex conversions. Using expert models for particular problems requires integration of models with third-party data as well as integration of models across disciplines. Modelers face significant heterogeneity that requires resolving semantic, spatiotemporal, and execution mismatches, which are largely done by hand today and may take more than 2 years of effort. We are developing a modeling framework that uses artificial intelligence (AI) techniques to reduce modeling effort while ensuring utility for decision making. Our work to date makes several innovative contributions: (1) an intelligent user interface that guides analysts to frame their modeling problem and assists them by suggesting relevant choices and automating steps along the way; (2) semantic metadata for models, including their modeling variables and constraints, that ensures model relevance and proper use for a given decision-making problem; and (3) semantic representations of datasets in terms of modeling variables that enable automated data selection and data transformations. This framework is implemented in the MINT (Model INTegration) framework, and currently includes data and models to analyze the interactions between natural and human systems involving climate, water availability, agricultural production, and markets. Our work to date demonstrates the utility of AI techniques to accelerate modeling to support decision-making and uncovers several challenging directions for future work.

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“…To make the mapping process easier and less laborious, we developed a language called D-REPR [90] for describing datasets. Users build a D-REPR model for a dataset through four steps.…”

Section: D-repr Representation Languagementioning

confidence: 99%

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Gil

Garijo

Khider

et al. 2021

ACM Trans. Interact. Intell. Syst.

Self Cite

View full text Add to dashboard Cite

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“…In this case, EA needs to be defined as a transformation function in the mapping rules. There are different mapping languages enabling the involvement of functions as part of the mapping rules such as RML+FnO [6], R2RML-F [7], FunUL [14], and D-REPR [21]. There also exist different engines capable of processing functions in different languages.…”

Section: Related Workmentioning

confidence: 99%

“…The resolution of these conflicts as part of the process of KG creation can be defined as Data Operators in a Data Ecosystem (DE) proposed by Capiello et al [3]. Alternatively, mapping languages have been extended to embrace Data Operators as functions that can be included as programming scripts directly in the mapping rules [7,14,21] or can follow a declarative approach (e.g., using the Function Ontology, FnO) [6]. They offer clear benefits in comparison to ad-hoc pre-and post-processing techniques in terms of reusability and reproducibility.…”

Section: Introductionmentioning

confidence: 99%

EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines

Jozashoori,

Sakor,

Iglesias

et al. 2021

Preprint

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Despite encoding enormous amount of rich and valuable data, existing data sources are mostly created independently, being a significant challenge to their integration. Mapping languages, e.g., RML and R2RML, facilitate declarative specification of the process of applying meta-data and integrating data into a knowledge graph. Mapping rules can also include knowledge extraction functions in addition to expressing correspondences among data sources and a unified schema. Combining mapping rules and functions represents a powerful formalism to specify pipelines for integrating data into a knowledge graph transparently. Surprisingly, these formalisms are not fully adapted, and many knowledge graphs are created by executing ad-hoc programs to pre-process and integrate data. In this paper, we present EABlock, an approach integrating Entity Alignment (EA) as part of RML mapping rules. EABlock includes a block of functions performing entity recognition from textual attributes and link the recognized entities to the corresponding resources in Wikidata, DBpedia, and domain specific thesaurus, e.g., UMLS. EABlock provides agnostic and efficient techniques to evaluate the functions and transfer the mappings to facilitate its application in any RML-compliant engine. We have empirically evaluated EABlock performance, and results indicate that EABlock speeds up knowledge graph creation pipelines that require entity recognition and linking in state-of-the-art RML-compliant engines. EABlock is also publicly available as a tool through a GitHub repository and a DOI.

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“…Grounding on this statement, different approaches have been proposed for facilitating the definition of functions to enhance data curation (e.g., [11,13,25]). Similarly, declarative languages have been proposed to allow for the definition of functions in the mappings; exemplar approaches include R2RML-F [8], FunUL [17], RML+FnO [7], and D-REPR [26]. Moreover, mapping engines enable to interpret functions in declarative mappings (e.g., Squerall [20], RMLStreamer 14 and CARML 15 for RML+FnO), as well as in non-declarative formalisms [18].…”

Section: Related Workmentioning

confidence: 99%

“…A rich spectrum of mapping languages has been proposed to specify schema-ontology alignments across data sources implemented in a variety of semi-structured and structured formats; exemplar approaches include R2RML [6], RML [10], and xR2RML [21]. Furthermore, function-based mapping languages [7,8,17,26] are equipped with abstractions that enable interoperable and reusable specifications of data transformations by means of user-defined functions. Moreover, formalisms like RML+FnO [7] combine the Function ontology and RML, enabling declarative specification of the schema-ontology alignments and data transformations that define the process of KG creation.…”

Section: Introductionmentioning

confidence: 99%

FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

Jozashoori,

Chaves-Fraga,

Iglesias

et al. 2020

Preprint

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Data has exponentially grown in the last years, and knowledge graphs constitute powerful formalisms to integrate a myriad of existing data sources. Transformation functions -specified with functionbased mapping languages like FunUL and RML+FnO -can be applied to overcome interoperability issues across heterogeneous data sources. However, the absence of engines to efficiently execute these mapping languages hinders their global adoption. We propose FunMap, an interpreter of function-based mapping languages; it relies on a set of lossless rewriting rules to push down and materialize the execution of functions in initial steps of knowledge graph creation. Although applicable to any function-based mapping language that supports joins between mapping rules, FunMap feasibility is shown on RML+FnO. FunMap reduces data redundancy, e.g., duplicates and unused attributes, and converts RML+FnO mappings into a set of equivalent rules executable on RMLcompliant engines. We evaluate FunMap performance over real-world testbeds from the biomedical domain. The results indicate that FunMap reduces the execution time of RML-compliant engines by up to a factor of 18, furnishing, thus, a scalable solution for knowledge graph creation.

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D-REPR: A Language for Describing and Mapping Diversely-Structured Data Sources to RDF

Cited by 15 publications

References 3 publications

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

Artificial Intelligence for Modeling Complex Systems: Taming the Complexity of Expert Models to Improve Decision Making

EABlock: A Declarative Entity Alignment Block for Knowledge Graph Creation Pipelines

FunMap: Efficient Execution of Functional Mappings for Knowledge Graph Creation

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