BioSearch: a semantic search engine for Bio2RDF

Abstract

Biomedical data are growing at an incredible pace and require substantial expertise to organize data in a manner that makes them easily findable, accessible, interoperable and reusable. Massive effort has been devoted to using Semantic Web standards and technologies to create a network of Linked Data for the life sciences, among others. However, while these data are accessible through programmatic means, effective user interfaces for non-experts to SPARQL endpoints are few and far between. Contributing to user frustrations is that data are not necessarily described using common vocabularies, thereby making it difficult to aggregate results, especially when distributed across multiple SPARQL endpoints. We propose BioSearch — a semantic search engine that uses ontologies to enhance federated query construction and organize search results. BioSearch also features a simplified query interface that allows users to optionally filter their keywords according to classes, properties and datasets. User evaluation demonstrated that BioSearch is more effective and usable than two state of the art search and browsing solutions. Database URL: http://ws.nju.edu.cn/biosearch/

Dictionary of disease ontologies (DODO): a graph database to facilitate access and interaction with disease and phenotype ontologies

“…Disease identifiers within the different ontologies are annotated with cross-reference relationships to connect to independent biomedical databases. However, no ontology provides a complete mapping across all existing ontologies 8,9 . Efforts to create an integrated ontology such as MonDO and EFO are continuously expanding to enrich their mappings but are currently not exhaustive and will lack mappings to proprietary ontologies.…”

“…While this stimulated the construction of integrated biological knowledgebases, the use of independent, ontology-specific identifiers, heterogeneous decisions on disease definitions, and the inherent presence of errors complicates integrating disease ontologies 6,8 . In addition, the navigation of these large integrated knowledgebases, with often an inherently complicated data model, is difficult for most, non-expert users 4,6,9 .…”

“…The Dictionary of Disease Ontologies (DODO) was developed to address these two challenges. (1) While efforts such as MonDO and EFO try to integrate different disease ontologies through semantic learning and manual curation, these resources, like the different disease ontologies themselves, are currently not providing a complete mapping across all diseases ontologies 8,9 . In addition, the existing efforts for integration are not flexible to extend easily to proprietary disease ontologies.…”

“…Such access would facilitate a more flexible connection to the different life science resources and create a more complete biomedical knowledge landscape. Currently, the programmatic access provided by many ontologies often requires expertise in creating SPARQL queries and a high level of understanding of the underlying databases or data model to be able to generate more complex queries 4,8,9 . The DODO R package allows easier access, exploration, and definition of disease concepts of interest.…”

Enabling Semantic Queries Across Federated Bioinformatics Databases

“…Moreover, for the purpose of building the federated data access system, we make the following additional contributions: (i) a semantic model for gene expression; (ii) an extension and adaptation of the Vocabulary of Interlinked Datasets (VoID) (33); (iii) public SPARQL 1.1 (32) query endpoints for OMA and Bgee; and (iv) a user-friendly search interface based on an extensible catalogue of query templates in plain English. The main purpose of (iv) is to demonstrate that the different database models can be jointly queried based on our approach, but our system supports any 1.1 SPARQLcompliant general-purpose query builders, such as in (34)(35)(36)(37)(38)(39). Our article is structured as follows.…”