Leveraging Terminological Structure for Object Reconciliation

Noessner, Jan; Niepert, Mathias; Meilicke, Christian; Stuckenschmidt, Heiner

doi:10.1007/978-3-642-13489-0_23

Cited by 67 publications

(49 citation statements)

References 14 publications

(18 reference statements)

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“…EPWNG [18] 100 100 100 RiMOM [23] 100 100 100 ObjectCoref [7] 100 99.8 99.9 LN2R [15] 100 100 100 CODI [12] 87 …”

Section: Evaluation and Preliminary Resultsunclassified

“…Hu et al [7] build a kernel by adopting the formal semantics of the Semantic Web that is then extended iteratively in terms of discriminative property-value pairs in the descriptions of URIs. Algorithms that combine formal semantics of the Semantic Web and string matching techniques also include Zhishi.me [11], LN2R [15], CODI [12] and ASMOV [8]. These systems can be applied to datasets in different domains without human provided matching rules, such as People, Location, Organization and Restaurant.…”

Section: Related Workmentioning

confidence: 99%

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Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Song

2012

The Semantic Web – ISWC 2012

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Abstract. Due to the decentralized nature of the Semantic Web, the same real world entity may be described in various data sources and assigned syntactically distinct identifiers. In order to facilitate data utilization in the Semantic Web, without compromising the freedom of people to publish their data, one critical problem is to appropriately interlink such heterogeneous data. This interlinking process can also be referred to as Entity Coreference, i.e., finding which identifiers refer to the same real world entity. This proposal will investigate algorithms to solve this entity coreference problem in the Semantic Web in several aspects. The essence of entity coreference is to compute the similarity of instance pairs. Given the diversity of domains of existing datasets, it is important that an entity coreference algorithm be able to achieve good precision and recall across domains represented in various ways. Furthermore, in order to scale to large datasets, an algorithm should be able to intelligently select what information to utilize for comparison and determine whether to compare a pair of instances to reduce the overall complexity. Finally, appropriate evaluation strategies need to be chosen to verify the effectiveness of the algorithms.

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“…EPWNG [18] 100 100 100 RiMOM [23] 100 100 100 ObjectCoref [7] 100 99.8 99.9 LN2R [15] 100 100 100 CODI [12] 87 …”

Section: Evaluation and Preliminary Resultsunclassified

Section: Related Workmentioning

confidence: 99%

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Song

2012

The Semantic Web – ISWC 2012

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“…Finally, a feature evaluation verifies the effectiveness of each proposed pruning technique compared to alternatives. For future work, we will apply P-EPWNG to datasets from the Ontology Alignment Evaluation Initiative 4 and also evaluate if our pruning techniques can help to scale other entity coreference algorithms [8], [15]. …”

Section: Discussionmentioning

confidence: 99%

“…Algorithms that combine formal semantics of the Semantic Web and string matching techniques also include Zhishi.me [13], LN2R [14], CODI [15] and ASMOV [16].…”

Section: B Evaluation Function Based Context Pruningmentioning

confidence: 99%

Accuracy vs. Speed: Scalable Entity Coreference on the Semantic Web with On-the-Fly Pruning

Song¹,

Heflin²

2012

2012 IEEE/WIC/ACM International Conferences on Web Intelligence and Intelligent Agent Technology

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Abstract-One challenge for the Semantic Web is to scalably establish high quality owl:sameAs links between coreferent ontology instances in different data sources; traditional approaches that exhaustively compare every pair of instances do not scale well to large datasets. In this paper, we propose a pruning-based algorithm for reducing the complexity of entity coreference. First, we discard candidate pairs of instances that are not sufficiently similar to the same pool of other instances. A sigmoid function based thresholding method is proposed to automatically adjust the threshold for such commonality on-the-fly. In our prior work, each instance is associated with a context graph consisting of neighboring RDF nodes. In this paper, we speed up the comparison for a single pair of instances by pruning insignificant context in the graph; this is accomplished by evaluating its potential contribution to the final similarity measure. We evaluate our system on three Semantic Web instance categories. We verify the effectiveness of our thresholding and context pruning methods by comparing to nine stateof-the-art systems. We show that our algorithm frequently outperforms those systems with a runtime speedup factor of 18 to 24 while maintaining competitive F1-scores. For datasets of up to 1 million instances, this translates to as much as 370 hours improvement in runtime.

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“…Researching links involve many steps: link-based object classification (categorizes objects primarily based on links and attributes) [7], object kind prediction (predicts object types supported attributes, links, and objects joined to it) [8], link kind prediction (predicts the aim of the link supported the objects involved) [9], link existence prediction (predicts the existence of a link) [10], link cardinality estimation (predicting the amount of links (and objects reached) to an object) [11], object reconciliation (determining whether or not 2 objects are the same supported their links) [12], cluster detection (predicting if an object set belongs together) [13], subgraph detection (discovering sub-graphs inside networks) [14], and data mining (mining for information concerning data) [15], [16]. Other samples of mining social networks are link prediction, namely exploitation the options intrinsic of the present model of a social network to model future connections inside the network.…”

Section: Introductionmentioning

confidence: 99%

Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering

Sharma¹

2016

IJCA

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Emergence of recent techniques for scientific knowledge collection has resulted in large scale accumulation of information relating various fields. Typical info querying ways are inadequate to extract helpful data from huge knowledge banks. Cluster analysis is one of the key knowledge analysis way and the k-means clustering algorithm is widely used for several data mining applications. The analysis of the cancer data set with the k mean and then applying with the Som. Many ways are planned within the literature for improving the performance with the kmeans clustering formula. This paper proposes a technique for creating knowledge retrieval more practical and efficient using som with K mean clustering technique, So as to get better clustering with reduced quality.

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Leveraging Terminological Structure for Object Reconciliation

Cited by 67 publications

References 14 publications

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Scalable and Domain-Independent Entity Coreference: Establishing High Quality Data Linkages across Heterogeneous Data Sources

Accuracy vs. Speed: Scalable Entity Coreference on the Semantic Web with On-the-Fly Pruning

Efficient Data Retrieval using Combine Approach of SOM and K-Mean Clustering

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