Unsupervised wrapper induction using linked data

Gentile, Anna Lisa; Zhang, Ziqi; Augenstein, Isabelle; Ciravegna, Fabio

doi:10.1145/2479832.2479845

“…We model such a curve using an exponential function shown in Eq. (16), where k is a scalar that controls the speed of convergence and |T | returns the number of observed examples in terms of argument pairs. which we obtain adequate knowledge and therefore close to maximum confidence.…”

Section: Knowledge Confidence Models Kcmentioning

confidence: 99%

“…16 Therefore, we study the problem of heterogeneous relations on DBpedia, the largest LOD dataset serving as a hub for connecting multiple sources in the LOD domain. DBpedia is also a representative example of relation het- 16 Based on the downloadable datasets as by 01-11-2013.…”

Section: Dbpediamentioning

confidence: 99%

Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web

Vandenbussche¹,

Atemezing

²

,

Poveda‐Villalón

³

et al. 2016

View full text Add to dashboard Cite

Abstract. This article addresses a number of limitations of state-of-the-art methods of Ontology Alignment: 1) they primarily address concepts and entities while relations are less well-studied; 2) many build on the assumption of the 'well-formedness' of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. We propose a domain-and language-independent and completely unsupervised method to align equivalent relations across schemata based on their shared instances. We introduce a novel similarity measure able to cope with unbalanced population of schema elements, an unsupervised technique to automatically decide similarity threshold to assert equivalence for a pair of relations, and an unsupervised clustering process to discover groups of equivalent relations across different schemata. Although the method is designed for aligning relations within a single dataset, it can also be adapted for cross-dataset alignment where sameAs links between datasets have been established. Using three gold standards created based on DBpedia, we obtain encouraging results from a thorough evaluation involving four baseline similarity measures and over 15 comparative models based on variants of the proposed method. The proposed method makes significant improvement over baseline models in terms of F1 measure (mostly between 7% and 40%), and it always scores the highest precision and is also among the top performers in terms of recall. We also make public the datasets used in this work, which we believe make the largest collection of gold standards for evaluating relation alignment in the LOD context.

show abstract

“…We model such a curve using an exponential function shown in Eq. (16), where k is a scalar that controls the speed of convergence and |T | returns the number of observed examples in terms of argument pairs.…”

Section: Knowledge Confidence Models Kcmentioning

confidence: 99%

“…kc |T | = exp |T | = 1 − e −|T |·k (16) For each model we need to define a parameter. For lgt, we need to define n, the number of examples above which we obtain adequate knowledge and therefore close to maximum confidence.…”

Section: Knowledge Confidence Models Kcmentioning

confidence: 99%

See 1 more Smart Citation

An unsupervised data-driven method to discover equivalent relations in large Linked Datasets

Zhang

¹

,

Gentile

²

,

Blomqvist

³

et al. 2016

Self Cite

View full text Add to dashboard Cite

Abstract. This article addresses a number of limitations of state-of-the-art methods of Ontology Alignment: 1) they primarily address concepts and entities while relations are less well-studied; 2) many build on the assumption of the 'well-formedness' of ontologies which is unnecessarily true in the domain of Linked Open Data; 3) few have looked at schema heterogeneity from a single source, which is also a common issue particularly in very large Linked Dataset created automatically from heterogeneous resources, or integrated from multiple datasets. We propose a domain-and language-independent and completely unsupervised method to align equivalent relations across schemata based on their shared instances. We introduce a novel similarity measure able to cope with unbalanced population of schema elements, an unsupervised technique to automatically decide similarity threshold to assert equivalence for a pair of relations, and an unsupervised clustering process to discover groups of equivalent relations across different schemata. Although the method is designed for aligning relations within a single dataset, it can also be adapted for cross-dataset alignment where sameAs links between datasets have been established. Using three gold standards created based on DBpedia, we obtain encouraging results from a thorough evaluation involving four baseline similarity measures and over 15 comparative models based on variants of the proposed method. The proposed method makes significant improvement over baseline models in terms of F1 measure (mostly between 7% and 40%), and it always scores the highest precision and is also among the top performers in terms of recall. We also make public the datasets used in this work, which we believe make the largest collection of gold standards for evaluating relation alignment in the LOD context.

show abstract

Understanding Data Centers from Logs: Leveraging External Knowledge for Distant Supervision

DeLuca

¹

,

Gentile

²

,

Ristoski

³

et al. 2020

Lecture Notes in Computer Science

0

View full text Add to dashboard Cite

Unsupervised wrapper induction using linked data

Cited by 24 publications

References 19 publications

Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web

Linked Open Vocabularies (LOV): A gateway to reusable semantic vocabularies on the Web

An unsupervised data-driven method to discover equivalent relations in large Linked Datasets

Understanding Data Centers from Logs: Leveraging External Knowledge for Distant Supervision

Contact Info

Product

Resources

About