This report describes the Barton Libraries RDF dataset and Longwell querybenchmark that we use for our recent VLDB paper on Scalable Semantic WebData Management Using Vertical Partitioning

Abadi, Daniel J.

Hollenbach, Kate

Madden, Samuel R.

Marcus, Adam

DSpace@MIT

Computer Science and Artificial Intelligence LaboratoryTechnical Reportm a s s a c h u s e t t s  i n s t i t u t e  o f  t e c h n o l o g y,  c a m b r i d g e ,  m a  0 213 9  u s a  —  w w w. c s a i l . m i t . e d uMIT-CSAIL-TR-2007-036 July 6, 2007Using The Barton Libraries Dataset As An RDF benchmarkDaniel J. Abadi, Adam Marcus, Samuel R. Madden, and Kate HollenbachUsing The Barton Libraries Dataset As An RDF benchmarkDaniel J. AbadiMITdna@csail.mit.eduAdam MarcusMITmarcua@csail.mit.eduSamuel R. MaddenMITmadden@csail.mit.eduKate HollenbachMITkjhollen@mit.eduABSTRACTThis report describes the Barton Libraries RDF dataset and Long-well query benchmark that we use for our recent VLDB paper onScalable Semantic Web Data Management Using Vertical Partition-ing [4].1. BARTON DATAThe dataset used for this benchmark is taken from the publiclyavailable Barton Libraries dataset [1]. This data is provided by theSimile Project [3], which develops tools for library data manage-ment and interoperability. The data contains records that composean RDF-formatted dump of the MIT Libraries Barton catalog, con-verted from raw data stored in an old library format standard calledMARC (Machine Readable Catalog). Because of the multiple sourcesthe data was derived from and the diverse nature of the data that iscataloged, the structure of the data is quite irregular.At the time of publication of this report, there are slightly morethan 50 million triples in the dataset, with a total of 221 uniqueproperties, of which the vast majority appear infrequently. Of theseproperties, 82 (37%) are multi-valued, meaning that they appearmore than once for a given subject; however, these properties ap-pear more often (77% of the triples have a multi-valued property).The dataset provides a good demonstration of the relatively unstruc-tured nature of Semantic Web data.2. LONGWELL OVERVIEWLongwell [2] is a tool developed by the Simile Project, whichprovides a graphical user interface for generic RDF data explorationin a web browser. It begins by presenting the user with a list of thevalues the type property can take (such as Text or Notated Musicin the library dataset). The user can click on the types of data hedesires to further explore. Longwell shows the list of currently fil-tered resources (RDF subjects) in the main portion of the screen,and a list of filters in panels along the side. Each panel representsa property that is defined on the current filter, with popular objectvalues for that property and their frequency also presented in thisbox. If the user selects an object value, this filters the working setof resources to those that have that property-object value defined,Permission to copy without fee all or part of this material is granted providedthat the copies are not made or distributed for direct commercial advantage,the VLDB copyright notice and the title of the publication and its dateappear, and notice is given that copying is by permission of the Very LargeData Base Endowment. To copy otherwise, or to republish, to post on serversor to redistribute to lists, requires a fee and/or special permission from thepublisher, ACM.VLDB ‘07, September 23-28, 2007, Vienna, Austria.Copyright 2007 VLDB Endowment, ACM 978-1-59593-649-3/07/09.Figure 1: Longwell Opening Screenupdating the appropriate panels with the new frequency counts forthis narrower set of resources.We will now describe a sample browsing session through theLongwell interface, along with screenshots from this sample ses-sion. The opening screen with the list of different types is shownin Figure 1. Note the list of different types is shown at the top ofthe screen and their frequencies in the upper-right-hand side of thescreen.The path starts when the user selects Text from the type panel (atthe upper-right-hand side of the screen), which filters the data intoa list of text entities. This is shown in Figure 2.On the right side of the screen, we find that popular proper-ties on these entities. As the user scrolls down (see Figure 3), wecan see that they include “subject,” “creator,” “genre,” and “pub-lisher.” Within each property there is a list of the counts of the pop-ular objects within this property. For example, as the user scrollsFigure 2: Longwell Screen Shot After Clicking on “Text” in theType Property Paneldown even more (see Figure 4), we find out that the German objectvalue appears 122 times and the French object value appears 131times under the language property. By clicking on “fre” (Frenchlanguage), information about the 131 French texts in the databaseis presented, along with the revised set of popular properties andproperty values defined on these French texts. This is shown in Fig-ure 5.Currently, Longwell only runs on a small fraction of the Bartondata – 9375 records, as its RDF triple store cannot scale to supportthe full 50 million triple dataset while still allowing interactive re-sponse time to queries (we show this scalability limitation in ourpaper [4]).3. LONGWELL QUERIESThe benchmark features seven queries that need to be executedon a typical Longwell path through the data. These queries arebased on a typical browsing session, where the user selects a fewspecific entities to focus on and where the aggregate results sum-marizing the contents of the RDF store are updated.The full queries are described at a high level here and are pro-vided in full in the next section as SQL queries against a triple store.Query 1 (Q1). Calculate the opening panel displaying the counts ofthe different types of data in the RDF store. This requires a searchfor the objects and counts of those objects with property Type.There are 30 such objects. For example: Type: Text has a count of1, 542, 280, and Type: NotatedMusic has a count of 36, 441.Query 2 (Q2). The user selects Type: Text from the previous panel.Figure 3: Longwell Screen Shot After Clicking on “Text” in theType Property Panel and Scrolling DownLongwell must present him with a list of other defined propertiesfor resources of Type: Text. It must also calculate the frequencyof these properties. For example, the Language property is defined1, 028, 826 times for resources that are of Type: Text.Query 3 (Q3). For each property defined on items of Type: Text,populate the property panel with the counts of popular object valuesfor that property (where popular means that an object value appearsmore than once). For example, the property Edition has 8 items withvalue “[1st ed. reprinted].”Query 4 (Q4). This query recalculates all of the property-objectcounts from Q3 if the user clicks on the “French” value in the “Lan-guage” property panel. Essentially this is narrowing the working setof subjects to those whose Type is Text and Language is French.This query has a much higher-selectivity than Q3.Query 5 (Q5). Here we perform a type of inference. If there aretriples of the form (X Records Y) and (Y Type Z) then we can in-fer that X is of type Z. Here X Records Y means that X recordsinformation about Y (for example, X might be a web page with in-formation on Y). For this query, we want to find the inferred type ofall subjects that have this Records property defined that also origi-nated in the US Library of Congress (i.e. contain triples of the form(X origin “DLC”)). The subject and inferred type is returned for allnon-Text entities.Query 6 (Q6). For this query, we combine the inference first stepof Q5 with the property frequency calculation of Q2 to extract in-formation in aggregate about items that are either directly known tobe of Type: Text (as in Q2) or inferred to be of Type: Text throughthe Q5 Records inference.Figure 4: Longwell Screen Shot After Clicking on “Text” in theType Property Panel and Scrolling Down to the Language Prop-erty PanelQuery 7 (Q7). Finally, we include a simple triple selection querywith no aggregation or inference. The user tries to learn what aparticular property (in this case Point) actually means by selectingother properties that are defined along with a particular value of thisproperty. The user wishes to retrieve subject, Encoding, and Typeof all resources with a Point value of “end.” The result set indicatesthat all such resources are of the type Date. This explains why theseresources can have “start” and “end” values: each of these resourcesrepresents a start or end date, depending on the value of Point.We make the assumption that an administrator has selected a setof 28 interesting properties over which queries will be run. Theseproperties are listed in Section 5. There are 26761389 triples forthese properties. For queries Q2, Q3, Q4, and Q6, only these 28properties are considered for aggregation.4. LONGWELL QUERIES IN SQLThe queries below are the seven benchmark queries written inSQL against a triple store schema.The properties table listed in these queries contains the list of 28properties that are processed for queries 2, 3, 4, and 6.Query1:SELECT A.obj, count(*)FROM triples AS AWHERE A.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"GROUP BY A.objQuery2:SELECT B.prop, count(*)Figure 5: Longwell Screen Shot After Clicking on “fre” in theLanguage Property PanelFROM triples AS A, triples AS B,properties AS PWHERE A.subj = B.subjAND A.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"AND A.obj = "<http://simile.mit.edu/2006/01/ontologies/mods3#Text>"AND P.prop = B.propGROUP BY B.propQuery3:SELECT B.prop, B.obj, count(*)FROM triples AS A, triples AS B,properties AS PWHERE A.subj = B.subjAND A.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"AND A.obj = "<http://simile.mit.edu/2006/01/ontologies/mods3#Text>"AND P.prop = B.propGROUP BY B.prop, B.objHAVING count(*) > 1Query4:SELECT B.prop, B.obj, count(*)FROM triples AS A, triples AS B,triples AS C, properties AS PWHERE A.subj = B.subjAND A.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"AND A.obj = "<http://simile.mit.edu/2006/01/ontologies/mods3#Text>"AND P.prop = B.propAND C.subj = B.subjAND C.prop = "<http://simile.mit.edu/2006/01/ontologies/mods3#language>"AND C.obj ="<http://simile.mit.edu/2006/01/language/iso639-2b/fre>"GROUP BY B.prop, B.objHAVING count(*) > 1Query5:SELECT B.subj, C.objFROM triples AS A, triples AS B,triples AS CWHERE A.subj = B.subjAND A.prop = "<http://simile.mit.edu/2006/01/ontologies/mods3#origin>"AND A.obj = "<info:marcorg/DLC>"AND B.prop = "<http://simile.mit.edu/2006/01/ontologies/mods3#records>"AND B.obj = C.subjAND C.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"AND C.obj != "<http://simile.mit.edu/2006/01/ontologies/mods3#Text>"Query6:SELECT A.prop, count(*)FROM triples AS A, properties AS P ((SELECT B.subjFROM triples AS BWHERE B.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"AND B.obj = "<http://simile.mit.edu/2006/01/ontologies/mods3#Text>")UNION(SELECT C.subjFROM triples AS C, triples AS DWHERE C.prop = "<http://simile.mit.edu/2006/01/ontologies/mods3#records>"AND C.obj = D.subjectAND D.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#type>"AND D.obj = "<http://simile.mit.edu/2006/01/ontologies/mods3#Text>")) AS uniontableWHERE A.subj = uniontable.subjAND P.prop = A.propGROUP BY A.prop;Query7:SELECT A.subj, B.obj,C.objFROM triples AS A, triples AS B,triples AS CWHERE A.prop = "<http://simile.mit.edu/2006/01/ontologies/mods3#Point>"AND A.obj = ’"end"’AND A.subj = B.subjectAND B.prop = "<http://simile.mit.edu/2006/01/ontologies/mods3#Encoding>"AND A.subj = C.subjectAND C.prop = "<http://www.w3.org/1999/02/22-rdf-syntax-ns#Type>";5. PROPERTIESThe 28 properties contained in the properties table are:<http://simile.mit.edu/2006/01/ontologies/mods3#access><http://simile.mit.edu/2006/01/ontologies/mods3#address><http://simile.mit.edu/2006/01/ontologies/mods3#affiliation><http://simile.mit.edu/2006/01/ontologies/mods3#authority><http://simile.mit.edu/2006/01/ontologies/mods3#catalogingLanguage><http://simile.mit.edu/2006/01/ontologies/mods3#code><http://simile.mit.edu/2006/01/ontologies/mods3#contents><http://simile.mit.edu/2006/01/ontologies/mods3#copyrightDate><http://simile.mit.edu/2006/01/ontologies/mods3#dateCreated><http://simile.mit.edu/2006/01/ontologies/mods3#dates><http://simile.mit.edu/2006/01/ontologies/mods3#edition><http://simile.mit.edu/2006/01/ontologies/mods3#encoding><http://simile.mit.edu/2006/01/ontologies/mods3#extent><http://simile.mit.edu/2006/01/ontologies/mods3#fullName><http://simile.mit.edu/2006/01/ontologies/mods3#issuance><http://simile.mit.edu/2006/01/ontologies/mods3#language><http://simile.mit.edu/2006/01/ontologies/mods3#nonSort><http://simile.mit.edu/2006/01/ontologies/mods3#origin><http://simile.mit.edu/2006/01/ontologies/mods3#partName><http://simile.mit.edu/2006/01/ontologies/mods3#partNumber><http://simile.mit.edu/2006/01/ontologies/mods3#point><http://simile.mit.edu/2006/01/ontologies/mods3#qualifier><http://simile.mit.edu/2006/01/ontologies/mods3#records><http://www.w3.org/1999/02/22-rdf-syntax-ns#type><http://simile.mit.edu/2006/01/ontologies/mods3#sub><http://simile.mit.edu/2006/01/ontologies/mods3#changed><http://simile.mit.edu/2006/01/ontologies/mods3#created><http://simile.mit.edu/2006/01/ontologies/mods3#physicalDescription>6. REFERENCES[1] Library catalog data.http://simile.mit.edu/rdf-test-data/barton/.[2] Longwell website. http://simile.mit.edu/longwell/.[3] Simile website. http://simile.mit.edu/.[4] D. J. Abadi, A. Marcus, S. Madden, and K. Hollenbach.Scalable semantic web data management using verticalpartitioning. In Proc. of VLDB, 2007.

Using The Barton Libraries Dataset As An RDF benchmark

http://dspace.mit.edu/bitstream/1721.1/37816/1/MIT-CSAIL-TR-2007-036.pdf

Using The Barton Libraries Dataset As An RDF benchmark

Abstract

Similar works

Full text

Available Versions

DSpace@MIT