QueryMed:生物医学RDF数据的直观SPARQL查询工具

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RDF (Resource Description Framework) 和 SPARQL (SPARQL Protocol and RDF Query Language) 是用于描述和查询网络上结构化数据的重要工具，特别是在生命科学和医学领域。本文介绍了一款名为 QueryMed 的开源工具，它是一个直观的SPARQL查询构建器和结果集可视化器，专为生物医学领域的RDF数据设计。 QueryMed的主要目标是简化用户在多源生物医学数据上的查询过程，即使他们对所使用的底层语义网（如OWL或RDFS）的结构以及如何编写SPARQL查询可能不熟悉。这款工具的价值在于其灵活性，能够支持针对各种生物医学主题的相关查询，并且可以跨越多个SPARQL endpoint。它旨在提供一个友好的界面，使非技术背景的用户也能方便地进行查询，无需深入理解数据描述的复杂概念。用户可以通过QueryMed选择他们想要使用的数据源，利用自身的专业知识来决定最合适的查询资源。系统允许动态添加其他数据源，以满足那些不在默认列表中的查询需求。这个特性使得QueryMed成为了一个高度可扩展和适应性强的工具，适用于不同层次的用户，从科研人员到临床实践者，都能轻松找到适合他们的查询路径。 QueryMed的工作流程包括用户输入查询条件，系统解析并执行SPARQL查询，然后将结果以易于理解的方式呈现。结果集可视化功能使得用户可以直观地分析和探索数据，从而支持他们在研究中发现新的关联、趋势或洞察。通过QueryMed，非专家用户也能参与到知识发现和数据驱动的研究过程中，促进了跨领域合作和信息共享。 QueryMed作为一款强大的RDF和SPARQL工具，不仅简化了生物医学数据查询，还降低了数据访问的门槛，提升了数据利用的效率和广泛性，对于推动医学研究和实践中的知识整合具有重要意义。

QueryMed: An Intuitive SPARQL Query Builder for

Biomedical RDF Data

Oshani Seneviratne

Massachusetts Institute of Technology

Cambridge, MA

USA

oshani@csail.mit.edu

Rachel Sealfon

Massachusetts Institute of Technology

Cambridge, MA

USA

rsealfon@csail.mit.edu

ABSTRACT

We have developed an open-source SPARQL query builder

and result set visualizer for biomedical data, QueryMed,

that allows end users to easily construct and run transla-

tional medicine queries across multiple data sources.

QueryMed is ﬂexible enough to allow queries relevant to

a wide range of biomedical topics, runs queries across mul-

tiple SPARQL endpoints, and is designed to be accessible

to users who d o not know the structure of the und erl yi n g

ontologies used in describing the datasets, or the SPARQL

query language t o query the data. The system allows users

to select the d a t a sources that they wish to use, drawing

on their specialized domain knowledge to decide the most

appropriate data sources to query. Users c a n add additional

data sources if they are interested in querying endpoints that

are not in the default list. After retrieval of the initial result

set, query results can be ﬁltered to imp rove their relevance.

The system also allows the user to exploit the underlying

structure of the RDF data to improve query result s .

Categories and Subject Descriptors

J.3 [Life and Medical Sciences]: Computer Applications;

H.3.3 [Information Search and Retrieval]: Information

Systems

Keywords

Biomedical Ontologies, SPARQL, Query Federation, Query

Building, Semantic Web, User Interfaces

1. INTRODUCTION

The quantity of publicly available data in the biomedical

domain has dramatically increased over recent years. Pub-

licly avai la b l e biomedica l resources include data on drug dis-

covery [?, ?], clin i ca l trials, diseases, disease genes, and phe-

notypes. With the linked open data movement, the semantic

web community has been very proactive in converting these

rich information resources to RDF [?]. In fact, the biomedi-

cal domain is among the early successes o f the sema ntic web

due to the rapidity with which the community has made its

data available in RDF triple stores [18].

To allow end users to exploit the abun d a n ce of useful

biomedical data that is currently available in RDF, there is a

need for easy-to use systems that do not require the end user

WWW2010, April 26-30, 2010, Raleigh, North Carolina.

to have knowledge of the underlying structure of the data,

and that also allow users to run federated queries on multiple

SPARQL endpoints. There is also a need for eﬃcient hybrid

interfaces that a ll ow both browsing a n d querying [?], since

many currently avail abl e systems are linked data browsers

such as the Tabulator [ ? ], which allow a user to navigate the

data in an exp lo ra t o ry manner but lack support for ﬁltering

and querying the data.

Answering many medically and biologically relevant ques-

tions requires searching, ﬁltering, and combining informa-

tion from multiple endpoints. For example, a physician may

know her patient’s personal information, symptoms, current

medications, and genotype. She may wish to determine the

patient’s treatment plan and identify clinical trials for which

the patient is eligible. Although the physician has a single

question–“based on the information I have about this pa-

tient, what is the best treatment plan and set of clinical tri-

als available?”–the re is no single d a t a source that the physi-

cian can use to answer this question. The information that

the physician needs must be gathered from numerous da ta

sources such as Pubmed, DailyMed, Drug b a n k , LinkedCT,

Diseasome, and GO [7, 1, 3, 6, 2, 4]. Her question must be

broken up into discrete pieces that can be execut ed individ-

ually at one data source a t a time.

Since the physician must search many databases in or-

der to ﬁnd an answer to her single q u est i on, she requires

a system tha t can automatically ru n queries over multiple

data sources. Also, the physician may not know SPARQL

query syntax, the location of the SPARQL endpoints, or the

structure of the relevant ontologies. She is likely to want an

intuitive way to query and to display the query result. De-

veloping intuitive ways to query multiple data sources and

display results is both an important and a challenging prob-

lem. Our system, QueryMed, allows users with no knowl-

edge of the SPARQL query language or the structure of the

underlying ontologies to easily run queries across multiple

SPARQL endpoints.

This paper is organized as follows: Section 2 provides

background information on the semantic web and its rel-

evance for the biomedical d o m a in . Section 3 describes our

system. Section 4 discusses related work and illustrates how

QueryMed diﬀers from previous systems. Finally, section

5 outlines future work and su mm a riz es the contributions of

our system.

2. BACKGROUND

The semantic web can be viewed as a global database sys-

tem for the informa ti o n available on the world wide web.

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