Geosemantically-enhanced PubMed Queries Using the Geonames Ontology and Web Services

Transcription

1 Geosemantically-enhanced PubMed Queries Using the Geonames Ontology and Web Services Maged N. Kamel Boulos, PhD, MSc, MBBCh Plymouth University, UK

2 Agenda About PubMed and MeSH The Problem and Previous Work A Proposed Solution Implementation and Proof-of-Concept Results Conclusions

3 About PubMed and MeSH PubMed ( is a biomedical bibliographic database curated by the US National Library of Medicine (NLM). It includes more than 21 million citations (abstracts) from MEDLINE, life science journals, and online books. In addition to free text search, PubMed can be queried using MeSH (Medical Subject Headings - NLM's controlled vocabulary thesaurus (ontology). MeSH terms can be "exploded" automatically to retrieve citations that carry the specified MeSH heading (or subheading) and also retrieve records that carry any of the more specific MeSH headings (or subheadings) indented beneath it in the MeSH Tree structure.

4 The Problem While MeSH and free text searching work fairly well for biomedical/clinical terms, problems arise when trying to limit search to studies from or about a particular geographic location (which is sometimes necessary; for example, when trying to find location-specific trials and studies whose results would be mostly relevant to the local population of a given country or region). For example, if a citation record does not include the word 'Canada' but instead includes 'Ontario' as the named place in its text, a PubMed query to find all studies conducted in Canada using only the keyword 'Canada' will not be able to retrieve that particular record, even though it is very relevant to the user's query.

5 The Problem (Cont d) An alternative way would be to include in the query as keywords the names of all possible provinces and other places in Canada (and any alternative names each place might have) that might be named in a citation. However, this approach would be very impractical and prone to forgetting one or more names when done manually by the average user.

6 Previous Work Several attempts have been made to develop geographic search filters for PubMed (using custom pre-formulated queries comprising country and regional terms), e.g., Valderas et al. (2006) and Pienaar et al. (2011). But all these filters are still prone to omissions and often fail to achieve the desired 100% sensitivity (i.e., not missing any of the relevant records in PubMed). They also need to be manually created for each required country/region, e.g., Valderas et al. (2006) covered Spain only, while Pienaar et al. (2011) was limited to Africa.

7 A Proposed Solution We propose that a GeoNames-powered PubMed search has the potential of solving these aforementioned issues. GeoNames ( is a geographic ontology that covers all countries of the worlds and contains over 8 million place names. It is available for download free of charge (licensed under a Creative Commons Attribution 3.0 License). An optional 'premium data subscription' costing 60 per month enables access to a higher quality release that has passed a large number of consistency checks.

8 A Proposed Solution (Cont d) GeoNames also offers a number of free Web Services ( such as the Place Hierarchy Web Services ( place-hierarchy.html) and the RSS to GeoRSS Converter ( By bringing in the "intelligence" of a geographic ontology and the ability to reason with place names (e.g., identify and treat 'Ontario' as 'a child of' (a province of) 'Canada' in the ontology tree hierarchy see a search engine can infer implicit meanings in the queried records that are not directly mentioned as free text or as keyword terms in either the citation records or their metadata.

9 A Proposed Solution (Cont d) A GeoNames-assisted PubMed search can thus retrieve that record that only mentions 'Ontario' in its text and/or associated metadata (with no occurrence of the word 'Canada' anywhere in the record) when the user types (searches for) 'Canada' as the location keyword, to use our same previous example. Typing 'Europe' can be similarly "exploded" to automatically include all relevant place names (countries, regions, cities/towns) in Europe, (perhaps as multiple machineformulated sub-queries whose results are then pooled and displayed together, to avoid exceeding PubMed's maximum permitted query string length). See also: M.N. Kamel Boulos, A.V. Roudsari, and E.R. Carson, "Towards a semantic medical Web: HealthCyberMap's tool for building an RDF metadata base of health information resources based on the Qualified Dublin Core Metadata Set," Med Sci Monit, International Scientific Literature, Inc., USA, 8(7):MT124-36, 2002 Jul. Available at M.N. Kamel Boulos, "A first look at HealthCyberMap medical semantic subject search engine," Technol Health Care, IOS Press, The Netherlands, 12(1):33-41, Available at

10 Implementation and Proof-of-Concept Results To implement this feature, a 'query interceptor/parser' will need to be developed to parse user queries for any place names (or the user can be offered to choose main locations from a drop-down list), then the identified place names are passed to a Web Service similar to the GeoNames Place Hierarchy Service to compile a list of place name children, parents and/or siblings/neighbours, as necessary (user may also be offered the option to select the geographic granularity or resolution of the query). Finally, the output from the Place Hierarchy Service is used to automatically generate suitable PubMed queries that would, for example, "explode" the user's original place name to also retrieve records from or about related/children places.

11 Implementation and Proof-of-Concept Results (Cont d) An alternative/complementary method ('Visual Search Option') would be to ask the user to search for the clinical terms/study types they are interested in without including any place name(s), then using a Service such as GeoNames' RSS to GeoRSS Converter to automatically parse PubMed search results (in RSS format) for place names in citations and geocode those names, so that the corresponding studies can be displayed on a map, (e.g., using Google Maps, which now supports GeoRSS - which the user can instantly query visually, e.g., identify the aforementioned 'Ontario' record example as a map pin within the boundaries of Canada.

12 Implementation and Proof-of-Concept Results (Cont d) We were able to produce a quick proof-of-concept demonstration of the 'Visual Search Option' using PubMed (RSS output of search results), GeoNames' RSS to GeoRSS Converter, and Google Maps. We searched PubMed for 'COPD' (chronic obstructive pulmonary disease) with date limits (citations published between and ) and no place names. The RSS search output from PubMed was then fed into GeoNames' RSS to GeoRSS Converter ( rss-to-georss-converter.html) and the resultant GeoRSS feed was finally passed to Google Maps for visualisation. with_pubmed_query_results_rss feed_xml_id

13 GeoNames-powered 'Visual Search Option' showing PubMed query results (records with a place name) as map pins on Google Maps. A citation record with the text 'Hamilton and Toronto, Ontario' was correctly mapped within the boundaries of Canada. A user looking for citations from or about Canada will be able to instantly (visually) spot them all on the map. feed_xml_id

14 Conclusions Compared to free text search, ontology-powered search has the potential of improving the sensitivity of a search engine and the relevance of its results by enabling the discovery and use of implicit information not directly (explicitly) mentioned in the queried resources, thanks to the rich domain concept relationships that an ontology brings in to the table, such as the hierarchical relationships ('parent of', 'grandparent of', 'sibling of', 'neighbour of', 'child of', etc.) of a place name.

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16 Conclusions (Cont d) Authors of biomedical papers and librarians indexing them (adding cataloguing metadata/keywords to individual citation records) should be encouraged to include any applicable place names in their work and to do so in a consistent manner (e.g., at least mention the place and the region/country it belongs to, since mentioning the place alone might not always be enough or unambiguous). However, even with such practices in place, a geographic ontology such as GeoNames would still be needed to reason with the added metadata beyond what any free text matching search algorithm can do, e.g., to infer that 'Paris, France' is 'part (or child) of' 'Europe' in response to a user query about studies conducted in 'Europe' (and thus successfully retrieve papers with 'Paris, France' in their abstract text/metadata, even when 'Europe' is not mentioned verbatim in them, meaning that such papers would otherwise be irretrievable in response to the same user query using a conventional free text search engine).