Divided We Stand Out! Forging Cohorts fOr Numeric Outlier Detection in Large Scale Knowledge Graphs (CONOD)

Jabeen, Hajira; Dadwal, Rajjat; Sejdiu, Gëzim; Lehmann, Jens

doi:10.1007/978-3-030-03667-6_34

Cited by 6 publications

(7 citation statements)

References 12 publications

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“…CONOD [16] is a scalable and generic algorithm for numeric outlier detection for DBpedia. It utilized rdf:type and Linked Hypernyms Dataset (LHD) [17] for creating cohorts.…”

Section: Related Workmentioning

confidence: 99%

“…Cohorts, unlike clusters, could overlap with each other. For cohorts, [16] used a scalable clustering approach based on Locality Sensitive Hashing (LSH) [18]. As the authors used rdf:type and LHD, this approach is only applicable to DBpedia.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

“…In summary, all the above-mentioned methods (except [16]) are not scalable to large-scale knowledge graphs, are complex, and require manual intervention. In addition, although [16] is scalable, but can be only applied on DBpedia. On the contrary, DistAD is distributed and scalable, is automatic and does not require any user interaction, and operates over arbitrary data.…”

Section: Related Workmentioning

confidence: 99%

“…Engie-accident dataset and 1M and 10M triple sample of DBpedia 15 . Engie SA 16 is a French multinational electric utility company that operates in the fields of energy transition, electricity generation and distribution, natural gas, nuclear, renewable energy, and petroleum. The accident dataset contains data about accidents that occurred in France in 2018 17 .…”

Section: A Experiments A: Assessment Of the Detected Anomaliesmentioning

confidence: 99%

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DistAD: A Distributed Generic Anomaly Detection Framework over Large KGs

Moghaddam

Lehmann

Jabeen

2022

2022 IEEE 16th International Conference on Semantic Computing (ICSC)

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The last decades have witnessed significant advancements in terms of data generation, management, and maintenance especially in the area of data lakes, and heterogeneous data. This has resulted in vast amounts of data becoming available in a variety of forms and formats including RDF. As RDF data have been created by liberal curation methods (e.g. crowdsourcing and automatic extraction tools with limited restriction and cross-validation on input data), they are prone to various kinds of errors that can be hidden in different dimensions (i.e. subject, predicate, and object level). Detecting those errors not only improves the KGs quality but also makes it possible to detect anomalous events in the data which can be used for subsequent analysis. In this paper, we present DistAD, a generic, scalable, and distributed framework for anomaly detection on large RDF knowledge graphs. DistAD provides a great granularity for the end-users to select from a vast number of different algorithms, methods, and (hyper-)parameters to detect outliers. The proposed framework is fully open-source, well-documented, and fully integrated into the active community project Semantic Analytics Stack (SANSA). The experiments on real-world use cases disclose that the framework is not only able to handle huge RDF data but also able to successfully detect hidden anomalies/outliers in KGs.

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“…CONOD [16] is a scalable and generic algorithm for numeric outlier detection for DBpedia. It utilized rdf:type and Linked Hypernyms Dataset (LHD) [17] for creating cohorts.…”

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: Related Workmentioning

confidence: 99%

Section: A Experiments A: Assessment Of the Detected Anomaliesmentioning

confidence: 99%

See 3 more Smart Citations

DistAD: A Distributed Generic Anomaly Detection Framework over Large KGs

Moghaddam

Lehmann

Jabeen

2022

2022 IEEE 16th International Conference on Semantic Computing (ICSC)

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Scalable resource description framework clustering: A distributed approach for analyzing knowledge graphs using minHash locality sensitive hashing

Agarwal¹,

Sinha

2022

Concurrency and Computation

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Web is becoming rich in data. Some of the sources from where these data are originating includes Blogs, YouTube, Twitter, Emails, E-commerce, Banking, sensors, and the Internet of Things. But these data are structured in a very poor fashion. The content of the web is becoming heterogeneous in nature both in terms of compendium and structure. It can be said that these data are human-readable data but the main motive is to draw inferences from these data which is only possible if it can be made machine-accessible. Clustering is considered an important task to organize these data and draw meaningful inferences from these data. In this paper, a clustering approach is proposed that can be applied to knowledge graphs and the possibility of applying Locality Sensitive Hashing is explored. Given the size of linked data, it is observed that this approach can be effective and scalable in comparison to other clustering approaches such as Hierarchical clustering, K-Means clustering, and K-Medoid clustering in discovering different communities that are defined by the link structure of the graph. The experimental results on different types of Linked Data sources justify the efficacy of the proposed model in terms of scalability and efficiency.

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