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

Abstract

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.

Augmented EHR: Enrichment of EHR with Contents from Semantic Web Sources

“…Existing Semantic Web tools to discover equivalent relationships between EHR coded values and terms from the LOD cloud were explored for this work. However, most schema definitions are not necessarily well formed in the domain of LOD [39] and cannot be seamlessly crawled. In addition, the quality of these LOD datasets in terms of completeness, consistency, conciseness, and interlinking remains a challenge [40].…”

Geographic Knowledge Graph Attribute Normalization: Improving the Accuracy by Fusing Optimal Granularity Clustering and Co-Occurrence Analysis

“…Unsupervised learning usually achieves normalization from a text morphology perspective by calculating the text similarity, or by string matching vocabulary with existing knowledge base concepts. Gunaratna et al [22] and Zhang et al [23] identified synonymous attributes in Linked Open Data (LOD) [24] using overlap between triples; the latter gives an unsupervised framework for the attribute normalization. Ristad et al [25] calculated the text similarity using string edit distance to achieve attribute normalization.…”

On the Current State of Linked Open Data

“…Severalissueshavebeeninvestigatedin (Zhang,Gentile,Blomqvist,Augenstein,&Ciravegna, 2017),e.g.,manyoftheLODontologiesareincompleteandnoisyandarenotwell-structuredand welldefined.Thealignmentofheterogeneousrelationshipsisnotproperlyaddressedbecauseof synonymyandpolysemy,lackofstructuralinformationaboutrelations,inconsistencyinthemeaning andusageofschemata.TheinteroperabilityofLOD-basedapplicationsisalsoaffectedduetorare linksatschemalevelLODdatasets(asmostoftheresearchfocusedthedatalevellinksinLOD datasets) (Zhangetal.,2017).…”