Di Nunzio, Giorgio Maria

Moldovan, Alexandru

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A study on query expansion with MeSH Terms and elasticsearch. IMS Unipd at CLEF eHealth Task 3

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A Study on Query Expansion with MeSH Termsand Elasticsearch.IMS Unipd at CLEF eHealth Task 3Giorgio Maria Di Nunzio and Alexandru MoldovanDept. of Information Engineering – University of Paduagiorgiomaria.dinunzio@unipd.it,alexandru.moldovan@studenti.unipd.itAbstract. In this paper, we describe the first participation of the Infor-mation Management Systems (IMS) group at CLEF eHealth 2018 Task3, Consumer Health Search Task. In particular, we participated in thesubtask IRTask 1: Ad-hoc Search which is a standard ad-hoc search task,aiming at retrieving information relevant to people seeking health adviceon the web.The goal of our work is to evaluate 1) different query expansion strategiesbased on the recognition of Medical Subject Headings (MeSH) termspresent in the original query; 2) different approaches to combine multipleranking lists given the query expansions. We used Elasticsearch as searchengine and the indexes provided by the organizers of this task.1 IntroductionIn this paper, we report the experimental results of our first participation tothe CLEF eHealth Lab Task 3 [4, 3]: “Consumer Health Search”. This task in-vestigates the problem of retrieving documents to support information needs ofhealth consumers that are confronted with a health problem.This work is part of a Master Degree thesis in Computer Engineering wherethe main goal is to test the effectiveness of some variants of query expansionapproaches based on the recognition of MeSH terms present in the original query.The contribution of our experiments to this task can be summarized as fol-lows:– A study of several query expansion approaches that takes into account therelationships between MeSH terms [6];– An evaluation of different document scoring strategies given the multipleranking list produced by the query expansions [1, 6].The remainder of the paper will introduce the methodology and a brief sum-mary of the experimental settings that we used in order to create the runs thatwe submitted for the task.2 MethodologyIn this section, we describe the query expansion approaches [6] as well as thedocument scoring strategies [1] that we used to create the expanded queries andthe ranked lists.2.1 Query expansion approachesFor the experiments of the query expansion approach, we used the English ver-sion of the information need. Before running the automatic expansion algorithms,we performed a manual check in order to correct spelling errors. For example,for topic 189001, the term gonorrhea is misspelt as gonhrrea.Identification of MeSH terms After the manual cleaning process, we use theMeshOnDemand1 API to identify the MeSH terms present in the query.For example, for topic 188001 “caffeine high blood pressure” we obtain thefollowing MeSH terms– Caffeine2– Hypertension3plus one additional term– Blood pressure4Finding related MeSH terms For each MeSH term found in the previousstep, we use the MeSHRDF5 database to look for semantically related (MeSH)terms. See for example Figure 1 that shows a part of the structure of the tree ofrelations among concepts related to the MeSH term Caffeine.We choose a subset of all the possible relations (predicates) between termsin the MeSHRDF database6, and we use this subset of predicates for queryexpansion in the following way:– Baseline: the original query is used without any additional term.– Simple Expansion (SE): given a MeSH term identified in the first step, allthe MesH entries related to that term are kept, except for the predicates‘meshv:Qualifier’, ‘meshv:seeAlso’, ‘meshv:broader’ e ‘meshv:broaderDescriptor’.Then we re-apply just once (not recursively) a SE for each ‘child’ node.– SE broader: like SE, in addition, only for the original MeSH terms (those thatappear in the query) we expand the MesH terms by adding the predicates‘meshv:broader’ e ‘meshv:broaderDescriptor’.1 https://meshb.nlm.nih.gov/MeSHonDemand2 https://meshb.nlm.nih.gov/record/ui?name=Caffeine3 https://meshb.nlm.nih.gov/record/ui?name=Hypertension4 https://meshb.nlm.nih.gov/record/ui?name=Blood&#37;Pressure5 https://id.nlm.nih.gov/mesh/6 https://hhs.github.io/meshrdf/predicatesFig. 1. Partial view of the tree structure of the MeSH term Caffeine.– SE also: like SE broader, instead of adding the predicates ‘broader’, we addthe MesH terms related with the predicate ‘meshv:seeAlso’.– SE broader also: a combination of SE broader and SE also.– SE child broader: first, apply SE, then for each ‘child’ node search for ‘par-ents’ different from the original MeSH terms.– SE recursive down: like SE, for each ‘child’ we recursively apply SE until leafnodes are found (no more recursions).– All in one: all the approaches at once.At the end of a query expansion process, we have three main data objects:– the original query q;– a vector m = (m1,m2, . . . ,mn) of MeSH terms associated with the originalquery;– a list t of expanded terms of n elements where each element ti is anothervector of terms resulting from the iteration of the expansion approach, ti =(ti1, ti2, . . . , tij).Given these three objects, we create a set of expanded queries that will beused to create a number of ranked lists, as explained in the following subsections.Building expanded query Give the vector of MesH terms m and the list ofexpanded terms t, we create a set of expanded queries by means of the followingprocedure:– For each term term mi of the vector of MeSH terms associated with theoriginal query,– we substitute mi with one of the terms in ti, for example ti1,– then, we build the expanded query by joining the original query with thenew vector m∗i = (m1, . . . , ti1, . . . ,mn), q∗ = q⋃m∗i .Therefore, at the end of the process we generate a set V of vectors of expandedqueries where the cardinality |V | is the sum of all the elements in the vectors ofthe list t. For each vector of vk ∈ V we obtain a list lk of ranked documents.Merging ranked lists We use different approaches to merge the |V | rankedlist into a single list based on the combination of scores of the documents.– Average: given a document present in one or more lists, the scores associatedto the document are averaged. Then the documents are ordered in decreasingorder on the basis of this new score.– Sum: given a document present in one or more lists, we sum the scoresassociated to the documents.– Normalized sum: like Sum but the sum of the scores are normalized by thehighest score in the ranked lists.– Round robin: for each rank r, we take the document of each list lk at r andadd it to the new ranked list if it has not already been seen.3 ExperimentsFor the experiments, we used the Elasticsearch search engine7 and the indexesprovided by the organizers of the task. We used the BM25 ranking function withdefault parameters.Given the constraints on the number of runs, four in total, that could besubmitted to the task, we submitted one baseline run and three query expansionvariant with the same scoring approach. We will evaluate all the other combina-tions as soon as the qrels will be made available.In particular, we submitted the following runs that use the Sum (of thescores) as the document scoring approach:– baseline.exp, a baseline run (plain BM25 with no expansion),– sum score simple.exp, simple expansion,– sum score broader also, simple expansion plus the two predicates broaderand also,– sum score recursive.exp, a recursive down approach.In Table ??, we show the number of query variants that are generated pertopic by three approaches.7 https://www.elastic.co/products/elasticsearch4 Final remarks and Future WorkThe aim of our first participation to the CLEF eHealth Task 3 was to test theeffectiveness of different query expansion approaches that use the MeSH termRDF graph as well as different merging approaches of the ranking lists producedby the query expansion approach. As future work, we will study the combinationof the MesH term expansion with the help of medical terminological records [5]for technologically assisted systematic reviews [2].References1. Nick J. Belkin, Paul Kantor, Edward A. Fox, and Joseph A. Shaw. Combiningthe evidence of multiple query representations for information retrieval. Informa-tion Processing & Management, 31(3):431 – 448, 1995. The Second Text RetrievalConference (TREC-2).2. Giorgio Maria Di Nunzio. A study of an automatic stopping strategy for tech-nologically assisted medical reviews. In Advances in Information Retrieval - 40thEuropean Conference on IR Research, ECIR 2018, Grenoble, France, March 26-29,2018, Proceedings, pages 672–677, 2018.3. Jimmy, Guido Zuccon, Joao Palotti, Lorraine Goeuriot, and Liadh. Kelly, editors.Overview of the CLEF 2018 Consumer Health Search Task. CLEF 2018 EvaluationLabs and Workshop: Online Working Notes. CEUR-WS, September 2018.4. Hanna Suominen, Liadh Kelly, Lorraine Goeuriot, Evangelos Kanoulas, Leif Az-zopardi, Rene Spijker, Dan Li, Aurélie Névéol, Lionel Ramadier, Aude Robert, JoaoPalotti, Jimmy, and Guido Zuccon, editors. Overview of the CLEF eHealth Eval-uation Lab 2018. CLEF 2018 - 8th Conference and Labs of the Evaluation Forum,volume Lecture Notes in Computer Science (LNCS). Springer, September 2018.5. Federica Vezzani, Giorgio Maria Di Nunzio, and Geneviève Henrot. Trimed: Amultilingual terminological database. In Proceedings of the Eleventh InternationalConference on Language Resources and Evaluation, LREC 2018, Miyazaki, Japan,May 7-12, 2018., 2018.6. Theodore B Wright, David Ball, and William Hersh. Query expansion using meshterms for dataset retrieval: Ohsu at the biocaddie 2016 dataset retrieval challenge.Database, 2017:bax065, 2017.Table 1. Number of query variants per approachtopic simple broader also recursive151001 290 412 524152001 310 330 1402153001 71 117 127154001 90 128 139155001 74 123 184156001 281 406 547157001 222 226 671158001 85 152 85159001 298 404 1260160001 84 191 119161001 104 134 129162001 42 118 42163001 168 286 216164001 38 60 231165001 181 296 439166001 253 323 436167001 120 126 312168001 241 339 241169001 16 80 16170001 144 169 169171001 22 61 22172001 96 135 96173001 15 20 15174001 112 186 166175001 82 124 102176001 42 101 42177001 76 169 92178001 236 336 388179001 100 179 146180001 83 105 89181001 28 75 28182001 257 279 423183001 85 157 116184001 117 166 117185001 96 175 96186001 245 308 562187001 85 204 169188001 125 164 239189001 18 38 18190001 377 428 678191001 6 19 6192001 169 245 270193001 53 88 104194001 95 147 95195001 47 62 47196001 13 104 13197001 109 201 185198001 51 78 51199001 131 209 131200001 129 185 202average 124.24 183.36 239.94

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A study on query expansion with MeSH Terms and elasticsearch. IMS Unipd at CLEF eHealth Task 3

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