Edit Paper: LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data
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Access API | No data available now. + |
Has Benchmark | DBpedia +, DrugBank +, LinkedCT + and MESH + |
Has Challenges | No data available now. + |
Has DataCatalouge | {{{Catalogue}}} + |
Has Description | No data available now. + |
Has Dimensions | Performance + |
Has DocumentationURL | http://limes.sf.net + |
Has Downloadpage | http://limes.sf.net + |
Has Evaluation | Performance Analysis + |
Has EvaluationMethod | Compare LIMES with different numbers of exemplars on knowledge bases of different sizes. + |
Has ExperimentSetup | No data available now. + |
Has GUI | No + |
Has Hypothesis | No data available now. + |
Has Implementation | LIMES + |
Has InfoRepresentation | No data available now. + |
Has Limitations | No data available now. + |
Has NegativeAspects | No data available now. + |
Has PositiveAspects | No data available now. + |
Has Requirements | No data available now. + |
Has Results | LIMES outperforms SILK in all experimental settings. It is important to notice that the difference in performance grows with the (product of the) size of the source and target knowledge bases. + |
Has Subproblem | No data available now. + |
Has Version | No data available now. + |
Has abstract | The Linked Data paradigm has evolved into … The Linked Data paradigm has evolved into a powerful enabler for the transition from the document-oriented Web into the Semantic Web. While the amount of data published as Linked Data grows steadily and has surpassed 25 billion triples, less than 5% of these triples are links between knowledge bases. Link discovery frameworks provide the functionality necessary to discover missing links between knowledge bases in a semi-automatic fashion. Yet, the task of linking knowledge bases requires a significant amount of time, especially when it is carried out on large data sets. This paper presents and evaluates LIMES - a novel time-efficient approach for link discovery in metric spaces. Our
faster when mapping large knowledge bases. +approach utilizes the mathematical characteristics of metric spaces to compute estimates of the similarity between instances. These estimates are then used to filter out a large amount of those instance pairs that do not suffice the mapping conditions. Thus, LIMES can reduce the number of comparisons needed during the mapping process by several orders of magnitude. We present the mathematical foundation and the core algorithms employed in the implementation. We evaluate LIMES with synthetic data to elucidate its behavior on small and large data sets with different configurations and show that our approach can significantly reduce the time complexity of a mapping task. In addition, we compare the runtime of our framework with a state-oft heart link discovery tool. We show that LIMES is more than 60 times faster when mapping large knowledge bases. |
Has approach | Mathematical characteristics of metric spaces + |
Has authors | Axel-Cyrille Ngonga Ngomo + and Sören Auer + |
Has conclusion | We presented the LIMES framework, which im … We presented the LIMES framework, which implements a very time-efficient approach for the discovery of links between knowledge bases on the Linked Data Web. We evaluated our approach both with synthetic and real data and showed that it outperforms state-of-the-art approaches with respect to the number of comparisons and runtime. In particular, we showed that the speedup of our approach grows with the a-priori time complexity of the mapping task, making our framework especially suitable for handling large-scale matching tasks (cf. results of the SimCities experiment). (cf. results of the SimCities experiment). + |
Has future work | We aim to explore the combination of LIMES … We aim to explore the combination of LIMES with active learning strategies in a way, that a manual configuration of the tool becomes unnecessary. Instead, matching results will be computed quickly by using the exemplars in both the source and target knowledge bases. Subsequently, they will be presented to the user who will give feedback to the system by rating the quality of found matches. This feedback in turn will be employed for improving the matching configuration and to generate a revised list of matching suggestions to the user. This iterative process will be continued until a sufficiently high quality (in terms of precision and recall) of matches is reached. ecision and recall) of matches is reached. + |
Has keywords | Linked Data, Web of Data, Link Discovery, Record Linkage, Duplicate Detection, Instance-Based Matching + |
Has motivation | No data available now. + |
Has platform | No data available now. + |
Has problem | Link Discovery + |
Has relatedProblem | No data available now. + |
Has subject | Link Discovery + |
Has vendor | Open Source + |
Has year | 2011 + |
ImplementedIn ProgLang | Java + |
Proposes Algorithm | No data available now. + |
RunsOn OS | No data available now. + |
Title | LIMES - A Time-Efficient Approach for Large-Scale Link Discovery on the Web of Data + |
Uses Framework | No data available now. + |
Uses Methodology | No data available now. + |
Uses Toolbox | No data available now. + |