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Guided exploration and integration of urban data

Published: 01 May 2013 Publication History

Abstract

Governments and enterprises are interested in the return-on-investment for exposing their data. This brings forth the problem of making data consumable, with minimal effort. Beyond search techniques, there is a need for effective methods to identify heterogeneous datasets that are closely related, as part of data integration or exploration tasks. The large number of datasets demands a new generation of Smarter Systems for data content aggregation that allows users to incrementally liberate, access and integrate information, in a manner that scales in terms of gain for the effort spent. In the context of such a pay-as-you go system, we are presenting a novel method for exploring and discovering relevant datasets based on semantic relatedness. We are demonstrating a system for contextual knowledge mining on hundreds of real-world datasets from Dublin City. We evaluate our semantic approach, using query logs and domain expert judgments, to show that our approach effectively identifies related datasets and outperforms text-based recommendations.

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Cited By

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  • (2024)ForestQB: Enhancing Linked Data Exploration through Graphical and Conversational UIs IntegrationACM Journal on Computing and Sustainable Societies10.1145/36757592:3(1-33)Online publication date: 16-Sep-2024
  • (2017)StolperwegeProceedings of the 28th ACM Conference on Hypertext and Social Media10.1145/3078714.3078748(319-320)Online publication date: 4-Jul-2017
  • (2015)Data Access Linking and Integration with DALI: Building a Safety Net for an Ocean of City DataThe Semantic Web - ISWC 201510.1007/978-3-319-25010-6_11(186-202)Online publication date: 24-Oct-2015
  • Show More Cited By

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cover image ACM Conferences
HT '13: Proceedings of the 24th ACM Conference on Hypertext and Social Media
May 2013
275 pages
ISBN:9781450319676
DOI:10.1145/2481492
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Published: 01 May 2013

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HT '13 Paper Acceptance Rate 16 of 96 submissions, 17%;
Overall Acceptance Rate 378 of 1,158 submissions, 33%

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Cited By

View all
  • (2024)ForestQB: Enhancing Linked Data Exploration through Graphical and Conversational UIs IntegrationACM Journal on Computing and Sustainable Societies10.1145/36757592:3(1-33)Online publication date: 16-Sep-2024
  • (2017)StolperwegeProceedings of the 28th ACM Conference on Hypertext and Social Media10.1145/3078714.3078748(319-320)Online publication date: 4-Jul-2017
  • (2015)Data Access Linking and Integration with DALI: Building a Safety Net for an Ocean of City DataThe Semantic Web - ISWC 201510.1007/978-3-319-25010-6_11(186-202)Online publication date: 24-Oct-2015
  • (2014)Finding mr and mrs entity in the city of knowledgeProceedings of the 25th ACM conference on Hypertext and social media10.1145/2631775.2631817(261-266)Online publication date: 1-Sep-2014
  • (2014)D2SC: Data-driven smarter cities2014 IEEE International Conference on Pervasive Computing and Communication Workshops (PERCOM WORKSHOPS)10.1109/PerComW.2014.6815274(599-603)Online publication date: Mar-2014
  • (2013)Searching in the city of knowledgeProceedings of the 36th international ACM SIGIR conference on Research and development in information retrieval10.1145/2484028.2484195(1123-1123)Online publication date: 28-Jul-2013
  • (undefined)SPUD - Semantic Processing of Urban DataSSRN Electronic Journal10.2139/ssrn.3199081

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