link analysis of life sciences linked data

Link Analysis of Life Science Linked Data

1

Wei Hu1, Honglei Qiu1, and Michel Dumontier2

1State Key Laboratory for Novel Software Technology, Nanjing University, China2Center for Biomedical Informatics Research, Stanford University

@micheldumontier::ISWC 2015

Linked Data offers links between datasets, but they are often incomplete and may contain

errors.


Network Analysis• Network analysis has long been

used to study link structures – The structure of the Web– Network medicine: cellular

networks and implications


Power law is scale free

A graph demonstrates the small worldphenomenon, if its clustering coefficient issignificantly higher than that of a randomgraph on the same node set, and if the graphhas a shorter average distance.

BTC2010

The clustering coefficient quantifies how closeits neighbors are to be a clique. The averagedistance is the average shortest path lengthbetween all nodes in the graph.

Dataset link analysis (using RDF data model)

Entity link analysis (using cross-references)

Term link analysis (using ontology matching)



Linked Data for the Life Sciences

5

Bio2RDF is an open source project to unify the representation and interlinking of biological data using RDF.

chemicals/drugs/formulations, genomes/genes/proteins, domainsInteractions, complexes & pathwaysanimal models and phenotypesDisease, genetic markers, treatmentsTerminologies & publications

• Release 3 (June 2014)• 35 datasets• 11B RDF triples• 1B entities• 2K classes• 4K properties

Dataset Links


Network Properties1. Well linked

2. Hubs and authorities

3. small-world phenomenonAverage distance = 2.77 vs 6Clustering coefficient = 0.22 vs

0.134. robust on systematic removal of nodes

Entity Link Analysis

How well do entities link to each other?• 76% entity links involve a special kind of RDF triples

– e.g. <kegg:D03455, kegg:x-drugbank, drugbank:DB00002>– x-relations have under-specified semantics

• May be truly identical, may refer to another related entity …

• Degree distribution– Some do not follow power law

• Exponent is too large (close to 5)

7

BTC2010


symmetry of entity links varies between different pairs of datasets

• Over 99% of links are reciprocated in DrugBank-PharmGKB and OMIM-HGNC– Suggests link sharing and synchronization

• Only 58% of links in DrugBank-KEGG and 51% of OMIM-Orphanetlinks are reciprocal– Suggests incomplete mapping

• 28% of OMIM-Orphanet links are malposed– Suggests variation in model (omim:Phenotype to orphanet:Disorder)

8 @micheldumontier::ISWC 2015

Transitivity Analysis: Find mismatches and discover new links


Evaluation of Entity Matching

How accurate are current entity matching approaches?• Built a benchmark from the reciprocal links between similarly-typed

entities • Evaluated several entity matching approaches

– Label similarity: Levenstein, Jaro-Winkler, N-gram, Jaccard– Machine learning: Linear regression, logistic regression with 5 properties

• Many-to-one links are difficult to be discovered


Term Link Analysis

How similar are the topics in the data network?• Use ontology matching to generate term link graph

– Falcon-AO (linguistic matchers + structural matcher + synonyms)• Created 83K class mappings, 1.5K object property mappings, and 858 data

property mappings– Similarity threshold = 0.9– Top-5 popular labels for classes and properties

• Significant overlap in topics, does not follow power law as in broader SW


Correlation of Link Graphs

To what degree are each of the three link graphs are correlated?• Spearman’s rank correlation coefficient:

– Entity link graph dataset pairs: entity links / entities– Term link graph dataset pairs: term mappings / terms– Dataset link graph dataset pairs: shortest path length

• All positively correlated– Closer datasets in distance have more linked entities and terms– Number of linked entities contributes little to overlap of topics


Summary of Findings

• Dataset, entity and term link graphs do not necessarily share the samecharacteristics with the Hypertext / Semantic Web– Degree distribution of entity links does not follow power law– Data hubs

• A significant number of entities have been linked using x-relations, but their intended semantics differs– Classes are identical or equivalent entity links represent logical equivalence

• Symmetric and transitive entity links do exist, but their utility is weakeneddue to their small number– Meanings of entity links may shift during transitive closure

• Only matching the labels of entities may fail, while combining different properties and using simple learning algorithms achieve good accuracy


[email protected]

Website: http://dumontierlab.comPresentations: http://slideshare.com/micheldumontier


http://dumontierlab.com/

http://slideshare.com/micheldumontier