SPRING:Ranking the results of SPARQL queries on Linked Data

Kunal Mulay and P. Sreenivasa Kumar

Indian Institute of Technology, MadrasChennai, India

{kunalm,psk}@cse.iitm.ac.in

Abstract

Linked Open Data (LOD) is a huge effort in the direc-tion of making the Web of Data a reality. The LODcloud consists of about 200 datasets contributed byseveral independent data providers from various do-mains such as Publications, Geography, Government,Media, Biology and Drugs etc. The data is representedin RDF and SPARQL is the query language. Due tothe open nature of the data and its heterogeneous ori-gin, it is often the case that a real-world entity appearsin different datasets and with different names. Whensuch entities are to be reported in the query results,a mechanism of rank ordering them becomes essen-tial. In this paper, we propose a new framework forcalculating the importance scores of datasets and alsoresources inside the datasets. As consensus is a key el-ement that adds value to the data in the Web of Data,we base our ranking framework on constructs that areused to express sameness or equivalence among theentities in RDF data. We also make use of the un-derlying graph structure of LOD in the framework.The framework is experimentally verified on the Bil-lion Triple Challenge (BTC-2010) dataset. The resultsindicate that the framework is successful in giving en-tities from the most relevant dataset a higher scorecompared to other datasets.

1 Introduction

The web of data[2] has attracted a lot of attentionfrom researchers in the recent past. Web of data rep-resents individual entities and properties by their ownURIs. Unlike in the case of web of documents, thelinks in web of data are “typed”, as links are labeledby property names. The Linked Open Data(LOD)1

project represents a huge effort in the direction of re-alizing the web of data. The data in the LOD cloud has

17th International Conference on Management of DataCOMAD 2011, Bangalore, India, December 19–21, 2011c©Computer Society of India, 2011

1http://linkeddata.org

grown tremendously in last few years and it currentlyconsists of 256 number of datasets and 30,874,103,042number of triples(or links)2.

When compared to traditional web, the web of datacontains datasets created by various research organiza-tions, companies and individuals. It is often the casethat an entity appears in several datasets with dif-ferent names(URIs). The entity class names and theproperty names used in a dataset constitute a sharedvocabulary and enable the dataset publishers to con-vey semantic information about the data. The cur-rent major search engines, namely Google, Yahoo andBing, recognize the need of adopting standard vocab-ularies for annotating text data. They are promot-ing the adoption of shared vocabularies for commonlyused entities through www.schema.org. There are alsoother efforts such as Microformats3 and RDFa4 in thisdirection.

The web of data community uses Resource Descrip-tion Framework(RDF)5 as a standard for data repre-sentation. XML6 is used as one of the mechanisms forrepresenting RDF data. In addition, Notation3 (N3)7,N-Triples8 and Turtle9 serialization formats also ex-ist for representing RDF data. In RDF, each dataitem is represented by a triple (subject - predicate -object). Here subject and object represent individ-ual resources(entities) and predicate represents a re-lation(link) between them. When represented in theform of a graph, subject and object become the nodesand predicate becomes a directed edge from subjectnode to object node. Here, each of the resources (sub-ject, predicate and object) are named with URI ref-erences, with the exception that the value of an ob-ject can either be a URI reference or a string literal.For example, the RDF representation about the phone

2http://www4.wiwiss.fu-berlin.de/lodcloud/state/3http://microformats.org4http://www.w3.org/TR/xhtml-rdfa-primer5http://www.w3.org/TR/rdf-concepts6http://www.w3.org/TR/REC-rdf-syntax7http://www.w3.org/DesignIssues/Notation3.html8http://www.w3.org/TR/rdf-testcases/#ntriples9http://www.w3.org/TeamSubmission/turtle/

“HTC HD7S” could include the following triples:(product1, hasName , “HTC HD7S”)(product1, hasManufacturer, HTC )(product1, hasNetwork, 2G )(product1, hasNetwork, 3G)(Product1, hasOS , Windows7Mobile)(product1, isSuccessorOf, product2)(product2, hasName, “HTC HD7”)Here, first and last triples use string literal as objectvalues. All the other names used are in fact URIs(theyare omitted here for brevity).

SPARQL10 is a W3C standard for querying RDFrepositories. Considering RDF repository as a RDFgraph, a SPARQL query returns the nodes that satisfycertain conditions. These conditions are formed usingtriple patterns, and the collection of triple patterns iscalled Basic Graph Pattern (BGP). For example, be-low is the SPARQL query to find name of a mobilewhich is manufactured by HTC and supports 3G net-work.Select ?name Where {?product <hasManufacturer> <HTC> .?product <hasNetwork> <3G> .?product <hasName> ?name }Here each row inside the where clause shows a triplepattern and the collection of all triple patterns in-side the where clause represents a basic graph pat-tern. When this SPARQL query is run against theabove set of triples, the variable product is bound with<product1> node and the variable name is boundwith “HTC HD7S”.

Due to the open nature of web of data and alsoits size, a typical SPARQL query results in severalanswers. For example if we query for a researcheraffiliated with a particular institute and working ona particular topic, we might get results from DBLPdataset11, Freebase12, DBpedia13 and also some otherdatasets. In such a scenario, it becomes necessary torank order the results of SPARQL queries. In this pa-per, we focus on this problem. Consensus or agreementplays a vital role in adding value to data in the contextof web of data. We seek to utilize the features of web ofdata that promote consensus to propose a new frame-work of ranking for resources and triples. We present acomplete system called SPRING (SPARQL ResultrankING). To the best of our knowledge, no one hasinvestigated ranking of SPARQL results.

The main contribution of this paper are: A studyof ranking frameworks for web of data, a domain inde-pendent, three level architecture and a global rankingsystem for all datasets in Linked Data cloud.

10http://www.w3.org/TR/rdf-sparql-query11http://dblp.l3s.de/d2r/12http://www.freebase.com13http://dbpedia.org

2 Related work

Link Analysis. In traditional web setting, link anal-ysis is used by various search engines to rank relevantpages. Link analysis is of interest for the researchersworking in web IR, social networks and semantic web.In web, two pages are connected by a hyperlink. Whilein social network and semantic web, two entities areconnected by a link which represents a relationship.Ranking algorithms like PageRank[13] and HITS[11]make use of existing link structure for ranking.

HITS is an authority based algorithm, which assignstwo scores to each page. The authority score calculatesthe value of content on that page, while the hub scorecalculates the value of links to the other pages. Thealgorithm initializes these values to 1 and authorityand hub update is performed iteratively until the valueconverges.

The PageRank algorithm is built upon randomsurfer model. In this model a surfer begins withbrowsing a random URL. After visiting the page, thesurfer can either navigate to one of the link on thepage with probability d or jump to a random URLwith probability 1 − d. The PageRank algorithm as-signs a constant value to all the pages and iterativelypropagates the values until convergence.

Ranking in Semantic Web. Ranking in semanticweb is limited to ontology ranking, relationship rank-ing and entity ranking.Swoogle[4] proposed an ontology rank algorithm forindexing and searching ontologies or Semantic WebDocuments(SWD). Here N-gram is used for matchingSWDs. It uses a rational surfer model which has rootsin random surfer model. Later they extend the searchto Semantic Web Terms(SWT) or entities[5].

PopRank [12] is an object level ranking algorithm,which extends PageRank model by adding popularitypropagation factor to the links pointing towards theobjects. It uses a supervised learning to rank objects.The training data is prepared by domain experts andthus the algorithm is limited to a particular domain.

SemRank[1] proposes a relevance model, which usessemantic and information-theory techniques. But it isonly focused on the relationships and not other re-sources.

ReConRank [8] is divided into two parts, ResourceR-ank and ContextRank. Here each of the algorithm usesPageRank model for their calculation. ResourceRankis applied over the resource graph and ContextRankis applied over the context graph. Later these two arecombined to form ReConRank. The model uses in-telligent surfer model as opposed to other approacheswhich uses random surfer model.

TripleRank [6] is built on top of HITS algorithm.It converts the semantic web graph into a three-dimensional tensor representation. Then the author-ity ranking algorithm is applied to rank the resources.

DATASET

LAYER

RESOURCE

LAYER

TRIPLE

LAYER

D1

D1

D1

D2

D2

D2

D3

D3

D3

Inter-dataset sameAs links

Links except inter-dataset sameAs

Links between datasets of LOD

Figure 1: A three-layer model for ranking SPARQL query results

The limitations of this model is maintaining the tensorwhen applied to web of data.

DING [3] uses a two-layered random surfer modelfor ranking RDF resources. Here one layer is for rank-ing datasets and other for entities. It modified the ex-isting TF-IDF algorithm and proposed LF-IDF (LinkFrequency - Inverse Dataset Frequency) algorithm tomeasure relevance of a link label given its frequency inthe data.

A recent paper discusses ranking Linked Data fromrelational database[10]. The algorithm focused onlyon Linked Data generated by relational databases. Itconverts the data to RDF using D2R server, then cal-culates rank score of the relationships using concept-hops. The algorithm fails to perform when the datasetcontains explicit relationships. In which case, the num-ber of concept-hops counts to minimum and all rela-tionships have same score.

In addition there are some more ranking algorithmsfor ranking ontologies and resources but they revolvearound the above discussed models. To our knowledgeno model is designed to rank SPARQL query results.

At this point it is relevant to mention the differ-ences between web query result ranking and ranking ofSPARQL query results. Currently, the leading searchengines use a variety of parameters for ranking the webquery results. Some of these parameters such as user’ssearch history, popularity of a page (in terms of totalvisits) etc are not relevant to the context of rankingRDF triples. Web query results are typically large insize and also represent an approximate answer to thequery as the query is imprecise. In contrast to this,SPARQL queries are precise and the results are typi-cally smaller compared to web query results. In the se-mantic web context, consensus or agreement betweendata sources is the most relevant parameter and typi-

cally there are few links in the underlying graph thatreflect consensus and hence contribute to result rank-ing. Thus we believe that RDF triple ranking requiresa separate approach and web query ranking algorithmscan not directly be used in this context.

3 System overview and Architecture

In this section, we describe the three layer model forranking SPARQL query results. Fig. 1 shows the ar-chitecture of model, the top layer is called Datasetlayer, middle layer is called Resource layer and thebottom most layer is called Triple layer.

Let Di and Dj be two datasets that have simi-lar entities. Here, by similar entities, we mean enti-ties that represent same real world entity but havedifferent names in different datasets. For exampleTim-Berners Lee in DBpedia represented as http://dbpedia.org/page/Tim_Berners-Lee and in DBLPrepresented as http://www4.wiwiss.fu-berlin.de/dblp/page/person/100007. Say, Di is well connectedwith other datasets in Linked Data cloud, but onthe other hand, Dj is not well connected with otherdatasets. When similar entities from Di and Dj ap-pear in a query result, it is desirable to rank the entitiesof Di higher than those for Dj . The intuition behindthis is that by linkage to Di, several independent datapublishers have endorsed the data in Di.

The dataset layer makes use of the Linked Datacloud graph14 (Fig. 3) to assign ranking score to eachdataset. A directional link from dataset Di to Dj inLOD means that there are at-least 50 triples havingtheir subject resource in Di and object resource in Dj .A similar two-layer approach is used by DING[3], but

14http://richard.cyganiak.de/2007/10/lod/imagemap.html

the dataset score is calculated as a function of num-ber of inter-dataset links. Since, the data is huge andnumber of links keeps increasing each day, dataset rankneeds to be updated, each time some update is done inthe dataset. This method becomes a very costly andtime consuming, so here we propose a simpler way tocalculate dataset rank using Linked Data cloud. Thecalculation of ranking score for datasets will be dis-cussed in section 5.1.

The middle layer calculates ranking score for re-sources. The dataset layer takes into account all typesof inter-dataset links, while the calculation of rankingscore for resources is based only upon inter-datasetowl:sameAs links. Next section will discuss about thecharacteristic of owl:sameAs links and why only theselinks are used for calculating score for resources. Thelinks shown in Fig. 1 at the resource layer are theinter-dataset owl:sameAs links.

The bottom most layer assigns a score to each tripleof dataset, which is calculated using the scores of in-dividual resources.

4 Ranking framework

The proposed ranking framework relies on the factthat, Linked Data published on the web obeys theprinciples of publishing Linked Data15. Our focus isnot to calculate ranking score for new resources or re-sources specified only in one dataset. Rather, we pro-pose a solution for resources which are specified in twoor more datasets. However, the algorithm calculatesranking score for all the resources present in LinkedData cloud. The proposed method uses owl:sameAslinks existing between resources to calculate rankingscore. We studied practical use of owl:sameAs re-lationship and it appears best to use these links tomodel ranking score. The reason is that existence ofowl:sameAs predicate between a pair of entities fromtwo datasets indicates agreement between dataset cre-ators that the two entities are same even though theyhave different URIs.

Owl:sameAs

Owl:sameAs

Freebase:Tim-LeeDBpedia:Tim-Lee

Figure 2: sameAs relationship for resource Tim-Lee

4.1 Practical use of owl:sameAs

The property owl:sameAs is defined as “the two URI’sconnected by owl:sameAs actually refer to same ob-

15http://www4.wiwiss.fu-berlin.de/bizer/pub/LinkedDataTutorial/

ject”. When a real world object is referred by twoor more URI’s, these URI’s are called as URI aliasesand owl:sameAs is used to connect these aliases. Forexample Tim-Lee in DBpedia is defined as http://dbpedia.org/page/Tim_Berners-Lee and in DBLPhe is defined as http://www4.wiwiss.fu-berlin.de/dblp/page/person/100007 and DBpedia has aowl:sameAs link from the former to the latter, so thesetwo URIs are aliases of each other.

Though owl:sameAs is one of the most widely usedpredicates for connecting resources, the practical ap-plication of owl:sameAs is much different from howit is defined. The predicate owl:sameAs is transitiveand symmetric, but in practice these characteristicsare often violated. For example both Freebase16 andOpencyc17have the concepts of football (a sport) andfootball players (person who plays this sport). Inter-estingly football18 resource of Freebase is connected tofootball player19 in Opencyc by owl:sameAs predicate.This is obviously incorrect and can never be symmet-ric. Though there are several instances of owl:sameAsrelationships among Linked Data, not all of them arecorrect. The characteristic of symmetry is a minimalrequirement for all owl:sameAs link to be correct. Butwe cannot deny the fact that there exist some unidirec-tional owl:sameAs links which are equally importantas these bidirectional links. In the following sectionwe make use of symmetric owl:sameAs instances forsetting up the ranking framework.

Using owl:sameAs, a dataset states that a particularresource is same as another resource, that exists in adifferent dataset. If the other dataset also connectsthe same resource with owl:sameAs property to thefirst resource, it is most likely that both resources aresame. For example, as shown in Fig. 2, the resourceTim-Lee in DBpedia is declared same as resource Tim-Lee in Freebase, and in addition Freebase confirms itback to DBpedia.

There are three possible types of links between tworesources, which can create problems; they are ex-plained in the following sections.

4.1.1 sameAs link exists but resources are dif-ferent

This problem occurs when there is a unidirectionalowl:sameAs link existing between two resources, butthe resources are different. This may occur because ofsome malicious users trying to corrupt data. Since thedata is open, a malicious user can add owl:sameAs linkfrom own dataset’s resource to well known resources ofother datasets. Our framework is able to detect such

16http://www.freebase.com17http://www.opencyc.org/18http://www.freebase.com/view/en/football19http://sw.opencyc.org/concept/

Mx4rvVkDmZwpEbGdrcN5Y29ycA

As of September 2010

MusicBrainz

(zitgist)

P20

YAGO

World Fact-book (FUB)

WordNet (W3C)

WordNet(VUA)

VIVO UFVIVO

Indiana

VIVO Cornell

VIAF

URIBurner

Sussex Reading

Lists

Plymouth Reading

Lists

UMBEL

UK Post-codes

legislation.gov.uk

Uberblic

UB Mann-heim

TWC LOGD

Twarql

transportdata.gov

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totl.net

Tele-graphis

TCMGeneDIT

TaxonConcept

The Open Library (Talis)

t4gm

Surge Radio

STW

RAMEAU SH

statisticsdata.gov

.uk

St. Andrews Resource

Lists

ECS South-ampton EPrints

Semantic CrunchBase

semanticweb.org

SemanticXBRL

SWDog Food

rdfabout US SEC

Wiki

UN/LOCODE

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ECS (RKB

Explorer)

Roma

RISKS

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Pisa

OS

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NSF

New-castle

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IBM

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DBLP (RKB

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Budapest

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Revyu

researchdata.gov

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referencedata.gov

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Recht-spraak.

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Last.FM (rdfize)

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ProductDB

PBAC

Poké-pédia

Ord-nance Survey

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The Open Library

OpenCyc

OpenCalais

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Times

NTU Resource

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NDL subjects

MARC Codes List

Man-chesterReading

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The London Gazette

LOIUS

lobidResources

lobidOrgani-sations

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LinkedGeoData

LinkedCT

Linked Open

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LCSH

DBLP (L3S)

Linked Sensor Data (Kno.e.sis)

Good-win

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Jamendo

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NSZL Catalog

GovTrack

GESIS

GeoSpecies

GeoNames

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GTAA

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Project Guten-berg (FUB)

MediCare

Euro-stat

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DrugBank

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DailyMed

Freebase

flickr wrappr

Fishes of Texas

FanHubz

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EUTC Produc-

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Eurostat

EUNIS

ESD stan-dards

Popula-tion (En-AKTing)

NHS (EnAKTing)

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CO2(En-

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educationdata.gov

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ECS South-ampton

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datadcs

MySpace(DBTune)

MusicBrainz

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John Peel(DB

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Audio-scrobbler (DBTune)

Last.fmArtists

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DBTropes

dbpedia lite

DBpedia

Pokedex

Airports

NASA (Data Incu-bator)

MusicBrainz(Data

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Moseley Folk

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Climbing

Linked Data for Intervals

Cornetto

Chronic-ling

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UniParc

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PubChem

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Pfam PDB

OMIM

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KEGG Reaction

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HGNC

Gene Ontology

GeneID

GenBank

ChEBI

CAS

Affy-metrix

BibBaseBBC

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Figure 3: Linked Data cloud

possibilities and assign low ranking score for these re-sources.

4.1.2 sameAs link exists and resources are ap-proximately same

This case arises when resources are slightly different inthe way they are defined in two datasets. For examplein Opencyc Sodium is defined in its pure form, whilein DBpedia it is defined to include isotopes also. Obvi-ously they are not same but, there exist a owl:sameAslink from DBpedia to Opencyc for this particular re-source [7].

4.1.3 No sameAs link exists, but resources aresame

It is extremely hard for dataset creators to regularlyupdate their existing data. When their data is addedto cloud they may not find any equivalent resource ex-isting in other datasets and created a new resource.Since the same resource is defined in more than onedataset without owl:sameAs link, this is stated asproblem of missing link. There exist some well knownlink discovery frameworks which identify such missinglinks, for example SILK[9].

5 Calculation of ranking score

The calculation is divided into two phases,

1. Dataset rank computation.

2. Triple rank computation.

5.1 Ranking score of a dataset

According to principles of publishing web of linkdata “A new dataset must always link to at leastone of the existing dataset”. This statement opensa way to rank the datasets. Since a new datasethas to depend on existing dataset, automatically anamount of belief is introduced into the cloud. A newdataset creator always tries to connect to informationsource that is having similar and trust-able data as ofhis/her own dataset. As more number of new datasetsconnect to a dataset, more will be the importance ofthat dataset, also as the ranking value of a datasetincreases, more new datasets will try to connect tothat dataset. Consider the LOD cloud20 in Fig. 3,DBpedia seems to be the most important and populardataset amongst all other datasets of cloud. And asthe number of datasets linking to DBpedia increases,it is reasonable to assume that the value of DBpediaalso tends to increase, which leads to higher rank ofthe particular dataset relative to others.We make use of the statistics21 published about

20Linking Open Data cloud diagram, by Richard Cyganiakand Anja Jentzsch. http://lod-cloud.net

21http://www.w3.org/wiki/TaskForces/CommunityProjects/LinkingOpenData/DataSets/LinkStatistics

Linked Data cloud for all calculations describedbelow. In the proposed model, as the number ofincoming links to a dataset increases, rank of thatparticular dataset also increases, which leads to higherrank. Since only one link22 can be possible betweenany two datasets in LOD cloud, maximum numberof possible incoming links is one less than the totalnumber of datasets. So the ranking score for a datasetd, denoted as Rds(d), can be defined as:

Rds(d) =Total number of incoming links to dataset d

Total number of datasets in LOD cloud

In the next section, we explain how the rankingscore of datasets are used to calculate ranking scoreof individual resources and triples.

5.2 Ranking score of resources and triples

The ranking score of a triple is defined in terms of theranking score of individual resources. Ranking scoreof a resource is calculated using owl:sameAs relationexisting between two datasets. Here the practical usesof owl:sameAs will be exploited, as described in Sec-tion 4.1. Three types of owl:sameAs links are possible:

Type 1. Outgoing link to a resource in a differentdataset.Type 2. Incoming link from a resource from a differentdataset.Type 3. Incoming and outgoing links between a pairof resources, where both resources are from differentdatasets.

From these three types of links, we propose to useonly the last two types of links for calculation of rank-ing score of resources. Outgoing owl:sameAs linksfrom a resource to other resources in different datasetscan not be used for rank calculation. This is because,a new dataset may claim that a resource it introducesis owl:sameAs many others but unless other datasetowners confirm the relationship, it is not trustworthy.However, the incoming owl:sameAs links can be usedto calculate ranking score as an independent datasetowner has asserted this relationship. The third typeof links that are bidirectional are the ones that reflectmutual confidence between a pair of data sources andcan certainly be used for calculation. We call the val-ues calculated from the second type of links as partialscore values and values calculated from the third typeof links as mutual score values.

Fig. 4 shows the existing owl:sameAs links forthe DBpedia resource Tim-Lee, among the showndatasets. The number inside the nodes represents thetype of link discussed above.

22http://richard.cyganiak.de/2007/10/lod/

Figure 4: owl:sameAs graph for resource Tim-Lee

5.2.1 Mutual score

Mutual score is calculated using Type 3 links, dis-cussed in previous section. Let r1, r2,...., rn be the re-sources that exist in different datasets and have Type3 links to resource r in the dataset d. Now, mutualscore for a resource r, denoted as Rmutual(r), is de-fined as:

Rmutual(r) =

n∑i=1

Rds(dataSetOf(ri))

Here dataSetOf(ri) denotes the dataset, say di, thatcontains resource ri.

5.2.2 Partial score

Partial score uses Type 2 links for calculation. Let rbe a resource which has an incoming owl:sameAs linkfrom resource rk. Also, let pk be number of outgoingand non-bidirectional links from rk to other resources.Then rk will transfer Rds(dataSetOf(rk))/pk amountto resource r. Partial score of a resource r, denoted asRpa is defined as:

Rpa(r) =

m∑k=1

Rds(dataSetOf(rk))

pk

m = Number of resources, each from different datasetshaving type 2 links to r.

Partial score mainly takes care of link spamming.Intuitively a unidirectional owl:sameAs link is of lessuse compared as a bidirectional owl:sameAs link.However if the dataset that has several out-goingowl:sameAs links is indeed authentic, over a periodof time the links become bidirectional and the rank ofthe resource also increases.

5.2.3 Total resource ranking score

The ranking score of a resource, denoted as T (r), isdefined as23:

T (r) = Rmutual(r) + Rpa(r)

Theoretically, a owl:sameAs network for a resourcecan be designed which can make ranking of a resourcegreater than 1. But practically, the cloud is so dis-tributed that score value of a resource hardly goes be-yond 1. In case, the value goes beyond 1, we truncateit to 1. Thus, the score value of a resource is in therange [0,1].

5.2.4 Triple ranking score

A triple is defined as subject - predicate - object.Where each of the three refers to a resource or aconcept in real world. Score of a triple is calculatedby adding score values of individual resources oftriple. The triple score of a triple, denoted as Rtriple,is defined as:

Rtriple = (T (rsubject) + T (rpredicate) + T (robject))/3

Since score of a resource is in the range of [0,1],adding such three resource score can reach maximumlimit of 3. To normalize score value of triple we dividedthe score by 3. The Triple score of a triple is in therange of [0,1].

D1:a

D2:c

D3:g

D4:d

D5:e

D6:b

D7:f

Figure 5: sameAs network for node d of dataset D4

5.3 Example

Fig. 5 shows an example owl:sameAs network for re-source d in dataset D4. Here D4:d represents a re-source d of dataset D4. Now, calculating score forresource d :

23T (r) value of known resources such as owl:inverseOf,rdf:seeAlso and others defined by W3C and xmlns community istaken as 1. Because these resources are most valuable resources.

Since D5:e is the only resource having Type 3 linkto D4:d, also, D2:b and D6:c are the resources havingType 2 links to D4:d, with 2 and 3 unidirectionaloutgoing links respectively,

Rmutual(d) = Rds(dataSetOf(e))

Rpa(d) = Rds(dataSetOf(b))2 + Rds(dataSetOf(c))

3

T (d) = Rmutual(d) + Rpa(d)

After calculation, score can be attached to partic-ular triple and can be represented in form of quadsin place of triples. Here fourth attribute representsranking score.

6 Algorithm

Our algorithm is divided into three parts, Algorithm1 calculates dataset score for all the datasets and putsthem in a list, Algorithm 2 calculates resource scorefor all resources and puts them in a dictionary withresource as key and score as value, Algorithm 3 cal-culates triple score for all triples and writes them toa file. In addition, we define, BTC cloud(will be dis-cussed in section 7) as the subset of LOD cloud thatcontains datasets common in BTC dataset and LOD.The input for these algorithms are:

• A file containing all resources

• A file containing all triples

• A file containing triples with owl:sameAs linkonly

Algorithm 1: Dataset-score(r)

Input : A Resource rOutput: Dataset score of r

1 d = dataSetOf(r)2 In = Number of incoming links to Dataset d from

other datasets in BTC cloud.3 Total = Total number of datasets in BTC cloud

4 Rds = InTotal

5 return Rds

Algorithm 1 takes input as a resource r then it callsfor function dataSetOf(r) to find dataset d from whichthis resource belongs to and calculate number of in-coming links to dataset d. Line 4 calculates datasetscore for dataset d.

Algorithm 2 takes a resource r as an input. Line 1initialize listIn, listBoth and listPartial lists. HerelineIn stores resources having outgoing owl:sameAslink to resource r, listBoth stores resources havingbidirectional owl:sameAs link with resource r andlistPartial stores unidirectional outgoing owl:sameAslink of resources in listIN at time of calculating par-tial score. Lines 2-3 find resources with outgoing link

Algorithm 2: Resource-score(r)

Input : A resource r from list of resourcesResourceList

Output: Resource-score of r1 listIN ← null, listBoth← null, listPartial← null2 Find resources ri with incoming link to r3 Append ri to list listIN4 foreach rin ∈ listIN do5 Find resources rj with incoming link to rin6 if rin = r then7 Add to list listBoth8 end

9 end10 listIN = listIN − listBoth11 Rmutual ← 0, Rpa ← 0, T ← 012 if listBoth = null then13 Rmutual = 014 end15 else16 foreach ri ∈ listBoth do17 Rmutual = Rmutual + getDataSet-score(ri)18 end

19 end20 foreach ri ∈ listIN do21 count← 0, Res← null22 Find resources Res with outgoing link to ri23 foreach ro ∈ Res do24 if /∈ outgoing link from ro to ri then25 count = count+ 126 end

27 end28 Rpa = Rpa + getDataSet-score(ri)/count

29 end30 T = Rmutual +Rpa

31 return T

Algorithm 3: Triple-score(t)

Input : A Triple tOutput: Triple-score of t

1 Rtriple = (getResource-score(rsubject)+getResource-score(rpredicate) + getResource-score(robject))/3

2 return Rtriple

to r and append it to listIN . Lines 4-9 find resourceshaving bidirectional owl:sameAs links with r and ap-pends that to listBoth. Now, there are some resourceswhich will be in both the list listIN and listBoth,so, line 10 takes both lists and remove resources fromlistIN , which are already in listBoth. Now, listINcontains resources which have only outgoing link tor and not having bidirectional links. listBoth nowcontains type-3 resources and listIN contains type-2resources. Lines 12-19 check if listBoth is empty, if itis empty then mutual score (Rmutual) will be 0. Other-wise, use getDataset-score module to get dataset-scoreof ri and calculate Rmutual. Lines 20-27 take a resourceri from listIN and count for number of outgoing uni-

directional owl:sameAs links to other resources. Line28 calculates partial score for each ri and sum for allri to produce total partial score. Line 30 sums up mu-tual score and partial score of r to calculate resourcescore.

Algorithm 3 takes a triple from a file containingall triples as input. At line 1, algorithm calls forgetResource-score module, to get resource score of sub-ject, predicate and object resources from dictionarycontaining resource score. By adding value returned,triple score value of a triple is obtained.

7 Experimental setup and Results

We have chosen BTC 201024 dataset for experimentalverification of proposed framework. BTC dataset isone of the largest datasets available, it consists of 3.2Billion Triples. The dataset consists of data crawledfrom several other datasets where each resource isrepresented by a URI. We mapped these URIs withtheir domain names and found a total of 1132 do-main names. Since our experiment depends on LODcloud, we found there are 55 such datasets in commonbetween LOD cloud and BTC dataset. From these55 datasets we found only 31 dataset’s resources hav-ing links within the set of 55 datasets, all other 24data sets having all the links out of these 55 datasets.So, we performed our experiment on these 31 datasetresources present in BTC data. We call this newlyformed dataset as BTC-cloud dataset.

We performed our experiments on 4 machines, allrunning Ubuntu 10.10 with Quad-core 2.83GHZ pro-cessor having 4 GB RAM and 2TB of Hard disk. Mostof the work in this experiment is done using Allegro-graph 4.225. Since the data is large, we parallelizedexecution by using one machine running Allegrographserver and rest of the machines as clients.

We performed the following series of operations onBTC data to get it in required form:

• Convert N-Quads to N-Triples format

• Removed unwanted triples and triples containingliterals

• Mapped resources to their domain names

• Found common datasets between LOD cloud andBTC

• Divided BTC-cloud dataset into 31 sub-datasetsfor calculating score

Since our algorithm is based on owl:sameAs net-work, our next operation is to extract triples havingowl:sameAs relationship existing between resources ofdifferent datasets. The owl:sameAs network formed isused for calculation of ranking score.

24http://km.aifb.kit.edu/projects/btc-201025http://www.franz.com/agraph/allegrograph/

Dataset DS-score Dataset DS-score Dataset DS-score Dataset DS-scoreDBLP (rkb) 0 Musicbrainz 0.1290 DBpedia 0.5806 OS (rkb) 0Crunch Base 0 Dailymed 0.0967 Linkedct 0.0967 Diseasome 0.0967Drugbank 0.1290 DBLP Berlin 0.0645 DBLP Hannover 0.0645 Freebase 0.0645Lingvoj 0.0645 SW Conference Corpus 0.0645 Eurostat 0.0322 Factbook 0.0645Project Gutenberg 0.0322 Revyu 0.0645 Jamendo 0.0645 Linkedmdb 0.0967Yago 0.1290 Myspace 0.0322 Surgeradio 0.0322 Opencalais 0Opencyc 0.0645 Umbel 0.0322 Wikicompany 0.0322 Openguides 0.0322telegraphis (capitals) 0.0322 telegraphis (countries) 0.0322 Geonames 0.2580

Figure 6: Dataset-score of all 31 datasets used in the experiment

Resource(Dihydrofolate Reductase) Ranking scorehttp://www4.wiwiss.fu-berlin.de/drugbank/resource/targets/365 0.2903225805

http://dbpedia.org/resource/Dihydrofolate_reductase 0.129032258http://mpii.de/yago/resource/Dihydrofolate_reductase 0.0

Table A

Resource(Apple Island) Ranking scorehttp://sws.geonames.org/4984314 0.580645161

http://dbpedia.org/resource/Apple_Island 0.258064516

Table B

Figure 7: Resource-score

After these series of operations we found that thereare approximately 1.8 million unique resources, morethan 10 million unique triples and about 1 milliontriples with owl:sameAs predicate.

Our goal in this work is to rank SPARQL query re-sults, but the ranking scores of datasets and resourcesproduced during the process can also be used for rank-ing datasets and resources.

The ranking score of datasets is shown in Fig. 6.Here DBpedia is having highest score. The resourceranking score produced can be used for ranking entitiesin entity search engines. The ranking score producedare domain independent and indicates the global pop-ularity of individual. Fig. 7 shows two sets of resourcesand their ranking score. here, all entities in a set rep-resent the same real world entity. Table A shows theentity named “Dihydrofolate reductase” and its repre-sentation in three different datasets, namely DBpediaand YAGO26 that are cross-domain datasets and drug-bank27, a biology dataset. Here point to notice is thateven though the dataset rank of DBpedia is higher,the score of DBpedia resource is less than Drugbank.A similar example in Table B shows the entity named“Apple Island” and its representation in two datasets,Geonames and DBpedia, where Geonames28 is geo-graphical dataset. Here also the entity from the rele-vant domain dataset gets higher score than the other.

Fig. 8 shows how the results of a SPARQL querywould get ordered by the ranks of the triples. Thequery is as below:

26http://www.mpi-inf.mpg.de/yago-naga/yago/27http://www4.wiwiss.fu-berlin.de/drugbank/28http://www.geonames.org

select ?s where {?s <rdf:type> <dbpediac:LandscapeArtist>}29

8 Scalability

The latest statistics about web of data shows theamount of open Linked Data available on the web30.The algorithm in section 6 assumes that the data isavailable locally. Clearly, this approach does not workfor actual LOD as the size of the data is very large.However, in order to calculate the ranking scores pro-posed in this paper, we need only the triples that haveowl:sameAs predicate. The subgraph of LOD graphconsisting of these links is much smaller and can bestored locally. Thus the ranking scores of resourcescan be computed locally whereas scores for triples canbe computed on demand whenever they are required tobe reported as results. A better strategy for comput-ing and using the ranking scores needs to be workedout as part of future work.

9 Conclusion and Future Work

A key difference between Linked Data and other datasharing approach is, its open nature. Linked Dataworks on the principle of “anyone can say anythingabout anything”. Till now, the number of datasetsin Linked Data cloud gets doubled every year and isattracting a lot of interest from researchers and com-mercial organization.

29dbpediac : <http://dbpedia.org/class/yago/>rdf : <http://www.w3.org/1999/02/22-rdf-syntax-ns#>dbpediar : <http://dbpedia.org/resource/>

30http://www4.wiwiss.fu-berlin.de/lodcloud/state/

Subject (?s) Predicate Object Triple scoredbpediar:Charles Leickert rdf:type dbpediac:LandscapeArtists 0.397849462dbpediar:Bernard Hailstone rdf:type dbpediac:LandscapeArtists 0.376344086dbpediar:John Marin rdf:type dbpediac:LandscapeArtists 0.376344086

dbpediar:Lucius Richard OB́rien rdf:type dbpediac:LandscapeArtists 0.333333333

Figure 8: Answer(?s) of the SPARQL query with Triple-score

In this paper, we have proposed a framework forranking entities as well as triples. For this we studiedpractical usage of owl:sameAs predicate and how it’suse being violated while designing ontologies and RDFdatasets. We used our findings to calculate score oftriples, and stored this data in the form of N-Quads.

The current framework is limited to ranking indi-viduals because owl:sameAs can not be used betweenpredicate resources. Future work includes extendingthe current framework to rank predicate resources us-ing vocabularies and extending the functionality ofSPARQL processor to query the data produced in formof N-Quads. Also, if a resource occurs in multipledatasets, the link structure between these resourcescan be unidirectional or bidirectional. In case of uni-directional links there is possibility of either removingthe link if it is not valid or converting it to a bidirec-tional link. Link discovery in linked data is a concernand several research efforts are going on in this direc-tion. Considering the huge size of the evolving linkeddata, this becomes a difficult problem. Our algorithmmakes use of links available in datasets, there is pos-sibility of explicitly discovering owl:sameAs links us-ing available framework like SILK [9]. Then we canuse this information for our calculation. This paperdoes not consider transitive nature of owl:sameAs re-lationships. It would be interesting to to extend theapproach to use these relationships.

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