Leveraging Linked Data to Discover Semantic Relations Within Data Sources

Taheriyan, Mohsen; Knoblock, Craig A.; Szekely, Pedro; Ambite, José Luis

doi:10.1007/978-3-319-46523-4_33

Cited by 25 publications

(31 citation statements)

References 14 publications

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“…Given the size of the available digital data repositories, manual curation is fast becoming unfeasible. Automated query answering platforms (leveraging data from museum records [45], computerised tools for symptom based medical diagnosis inference [32], archaeological database processing [37], etc.) represent an attractive solution, however, several important issues remain unaddressed:…”

Section: Introductionmentioning

confidence: 99%

An architecture for the autonomic curation of crowdsourced knowledge

et al. 2017

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Human knowledge curators are intrinsically better than their digital counterparts at providing relevant answers to queries. That is mainly due to the fact that an experienced biological brain will account for relevant community expertise as well as exploit the underlying connections between knowledge pieces when offering suggestions pertinent to a specific question, whereas most automated database managers will not. We address this problem by proposing an architecture for the autonomic curation of crowdsourced knowledge, that is underpinned by semantic technologies. The architecture is instantiated in the career data domain, thus yielding Aviator, a collaborative platform capable of producing complete, intuitive and relevant answers to career related queries, in a time effective manner. In addition to pro- The Good Careers Guide, London, UK viding numeric and use case based evidence to support these research claims, this extended work also contains a detailed architectural analysis of Aviator to outline its suitability for automatically curating knowledge to a high standard of quality.

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Section: Introductionmentioning

confidence: 99%

An architecture for the autonomic curation of crowdsourced knowledge

et al. 2017

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“…In our approach, we aim to improve the mappings of single-scenario use cases, i.e., the precision and recall of the query-answering of the resulting Linked Data improves, by using DSQMs, because the DSQMs might contain knowledge that already tackles these challenges and DSQMs have proven benefits [8,9]. However, we aim to use an extended set of knowledge compared to previous efforts to improve the mappings to tackle these complex challenges.…”

Section: Problem Statementmentioning

confidence: 99%

“…If the different scenarios are in different domains previous mappings will have a limited usability. They use graph patterns found in existing Linked Data to determine how the classes and properties are related to each other [8]. This allows support for single-scenario use cases, because for these cases the tool is not able to use previous mappings to get that knowledge.…”

Section: Mapping Generation Toolsmentioning

confidence: 99%

“…In most cases, when tools are accompanied with an evaluation, they only assess a limited set of the mapping process' aspects: time required to transform the mapping suggestions to the correct ones [11]; W3C Direct Mapping Test Cases 2 [14]; precision, recall, and CPU time when using the semantic model [9] and Linked Data [8]; or number of user actions [21]. However, they only give a limited insight about task complexity and/or mapping quality, and not every tool is compared with the results of other tools.…”

Section: Mapping Generation Evaluationmentioning

confidence: 99%

“…Subsequently, the mapping process requires a low user effort. In most cases, these tools only use the original data schema and ignore other knowledge available in existing (Linked) Data [7,8], Schemas (data schemas, ontologies and vocabularies), the Queries that will be executed on the new rdf data (query workload) [2], and Mappings [9] (DSQM). With existing knowledge, we refer to knowledge that is available before the mapping process.…”

Section: Introductionmentioning

confidence: 99%

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Ontology-Based Data Access Mapping Generation Using Data, Schema, Query, and Mapping Knowledge

et al. 2017

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Abstract. Ontology-Based Data Access systems provide access to nonrdf data using ontologies. These systems require mappings between the non-rdf data and ontologies to facilitate this access. Manually defining such mappings can become a costly process when dealing with large and complex data sources, and/or multiple data sources at the same time. This resulted in different mapping generation tools. While a number of these tools use knowledge from the original data, existing Linked Data, schemas, and/or mappings, they still fall short when dealing with complex challenges and the user effort can be high. In this paper, we propose an approach, together with an evaluation, that discovers and uses extended knowledge from existing (Linked) Data, schemas, query workload, and mappings, and combines it with knowledge provided by the mapping process to generate a new mapping. Our approach aims to improve the mapping quality, while decreasing the task complexity, and subsequently the user effort.

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