inWalk: Interactive and Thematic Walksinside the Web of Data

Silvana CastanoUniversità degli Studi di Milano

DI - Via Comelico, 3920135 Milano, Italy

silvana.castano@unimi.it

Alfio FerraraUniversità degli Studi di Milano

DI - Via Comelico, 3920135 Milano, Italy

alfio.ferrara@unimi.it

Stefano MontanelliUniversità degli Studi di Milano

DI - Via Comelico, 3920135 Milano, Italy

stefano.montanelli@unimi.it

ABSTRACTThe goal of this paper is to demonstrate inWalk, an interac-tive web-based system for linked data exploration featuredby the notion of inCloud and thematic walk. The demon-stration focuses on the key functionalities of the system forsmart data aggregation and navigation.

Categories and Subject DescriptorsH.3 [INFORMATION STORAGE AND RETRIEVAL]:Information Search and Retrieval

General TermsThematic web-data exploration, similarity-based data ag-gregation

1. INTRODUCTIONThe availability of large datasets of linked data makes itpossible to access information, and knowledge on the se-mantic web through URIs (Universal Resource Identifier),RDF (Resource Description Framework), and new querylanguages like SPARQL [1]. However, users searching dataabout a specific topic of interest need usually to face a multi-step and loosely-intuitive browsing activity to build a com-prehensive picture of the data of interest [4, 7]. In this paper,we present inWalk, an interactive system for the explorationof linked data based on the notion of inCloud. An inCloudis a high-level thematic graph where nodes represent clus-ters of similar linked data and edges represent relations ofproximity between nodes. A system demonstration is avail-able online at http://islab.di.unimi.it/inwalk and it isarticulated as a sequence of user tasks corresponding to in-creasing levels of skills in using data-driven applications andincreasing levels of complexity of the inWalk functionalitiesthat are demonstrated.

2. THE INWALK SYSTEMinWalk is suited to support both skilled and non-skilledusers on two critical aspects related to linked data explo-

(c) 2014, Copyright is with the authors. Published in Proceeding of the 17thInternational Conference on Extending Database Technology (EDBT 2014)on OpenProceedings.org. Distribution of this paper is permitted under theterms of the Creative Commons license CC-by-nc-nd 4.0

ration. Two main contributions of inWalk are the targetof this demo. On one side, the goal of inWalk is to over-come the rigid web interfaces of linked data repositoriesby providing thematic, high-level data views built throughsimilarity-based aggregation techniques. On the other side,inWalk aims at supporting users that are interested in query-ing the linked data, but are unfamiliar with RDF-basedquery languages (e.g., SPARQL, MQL) by providing intu-itive keyword-based and SQL-like query tools.

2.1 System featuresThe inWalk system is characterized by the following keyfeatures.

Abstraction-by-aggregation Given a set of linked data S,the inWalk system provides a high-level, conceptual view ofsuch data through the notion of inCloud [2]. An inCloud is agraph iC = (N,E), where a node ni ∈ N represents a clusterof similar linked data whose meaning is expressed through asynthetic and representative description called cluster essen-tial, and an edge e(ni, nj) ∈ E represents a proximity linkbetween the clusters ni and nj . An inCloud is the resultof an abstraction process based on linked data aggregation.A node ni = (cli, di) of an inCloud represents a theme andit is defined as a similarity cluster cli, with an associatedessential description di. A cluster cli is a set of linked datathat are found to be similar according to a considered list ofproperties. An essential di is the set of the top-k most repre-sentative labels/types featuring the linked data belonging tocli. Each node ni ∈ N is also characterized by a prominencevalue pi, which expresses the importance of ni within theoverall inCloud. A proximity link e(ni, nj) is set betweenni and nj to denote a similarity-based relationship betweenthe involved nodes and a degree of proximity xij is associatedwith e to express the strength of such a relationship1.

Exploration-by-walks The inWalk system enforces linkeddata exploration by relying on two possible actions, namelythe inside walk and the thematic walk, defined over a con-sidered inCloud. The inside walk corresponds to the ideaof a “walk-in-the-deep” of a certain cluster cli to explore itscontents. A panel is shown on the right-hand side of theinWalk interface listing the complete essential di as well asall the linked data contained in cli. The user can select an

1Technical details about inCloud construction, cluster-essential definition, and proximity-link specification are outof the scope of this paper; technical details can be foundin [2, 3].

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web application engine(inCloud construction)

web application interface(inCloud exploration)

inWalk architecture

essential definitionproximity-link specification

abstraction manager

classification manager

data matchingdata clustering

cachingassistant

inCloud data accessin-memory data management

queryassistant

exploration setupquery processing

inWalkrepository

inCloudproducer

(data expert)

inCloudconsumer

(web user)

extractionmanager

seed specificationlinked data extraction

presentation assistant

inCloud visualizationinteractive resize

linked data set (S) inCloud (iC) inWalk interface

Figure 1: Architecture of the inWalk system

item of interest within the panel to visualize the correspond-ing linked data view in the repository of provenance (e.g.,the Freebase web interface). The thematic walk correspondsto the idea of a “walk-in-the-surface” of the inCloud. By se-lecting a cluster, the corresponding neighborhood is shownon the borderline of the cluster circle. The neighborhoodof a cluster cli is the set of clusters directly connected tocli through a proximity link in the inCloud. The borderlineof the cluster cli is segmented into slices and each slice is apointer to a neighbor cluster. Each slice is associated withthe name of the neighbor cluster. By clicking on a slice, theuser moves from the current cluster to the neighbor clusterwhich becomes active in turn. By moving from one clus-ter to another on the basis of the proximity links (i.e., byslice selection), the user describes what we call a “thematicpath” within the inCloud. We stress that cluster essentialsand names are automatically extracted from original linkeddata to bring out the theme/topic characterizing the clustercontents, by also maintaining the original terminology of therepository of provenance. This way, a thematic path chainsthe sequence of themes/topics explored by the user whileenabling a seamless shift from the thematic view of inWalkto the linked data view in the repository of provenance.

Filtering-by-patterns The inWalk system supports filter-ing operations over the inCloud structure to enable the userin focusing the exploration of a portion of inCloud that sat-isfy a certain selection criterion. The inCloud Query Lan-guage (IQL) has been developed to enforce user-friendly,assisted modalities of query formulation, namely keyword-based and query-based. Keyword-based editing allows theuser to specify a query “a la search-engine” where a listof target keywords is entered by the user. Only the clus-ters that contain all of them, either as an essential labelor a type, are shown in the result. Auto-completion andcontrolled-vocabulary mechanisms are enforced to supportthe user editing. A history of past keyword searches is alsoprovided. Query-based editing allows the user to specifyan IQL query according to a sort of SQL-like syntax. Four

different query patterns are provided in IQL to support i)cluster selection, ii) cluster join, iii) cluster path retrieval,and iv) intersection, union, and difference of sub-clouds.

2.2 System architectureThe inWalk architecture is featured by two main compo-nents, namely the web application engine and the web appli-cation interface (see Figure 1).

The web application engine is the back-end componentof inWalk and it is in charge of constructing inClouds. Ittransforms a raw set of linked data extracted from a givenrepository (e.g., Freebase - http://www.freebase.com, DB-

pedia - http://dbpedia.org) into a corresponding inCloudfor subsequent walk exploration through the application in-terface component. The extraction manager acquires from arepository R the linked data set S upon which the inCloudis built. The classification manager first executes a datamatching step over the set S to detect pairs of similar linkeddata through the HMatch 2.0 suite. An agglomerative hierar-chical clustering step is then performed to generate the setof similarity clusters that constitutes the inCloud (see [5] formore technical details). Finally, the abstraction manager fi-nalizes the construction of the inCloud graph structure outof similarity clusters.

The web application interface is the front-end compo-nent of inWalk and it has the role of supporting the webusers in the exploration of inClouds (see Figure 2). Theinterface is a HTML5+Javascript GUI and it can be visual-ized through any HTML5-compliant browser. The presen-tation assistant is in charge of managing the visual layout ofinClouds according to the number of cluster nodes and prox-imity links to represent on the screen. A cluster is visualizedas a circle whose name is the pair 〈label, type〉 correspondingto the first label and first type of the cluster essential, andwhose size is proportional to the cluster prominence. A prox-imity link is represented as an edge between two conceptswhose thickness on the screen is proportional to the proxim-

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active cluster F

inCloud clusters G

keyword search areaA

graphical option control panelE

resource relevanceD

resources preview & linksC

active cluster essentialB

show thematic pathHproximity linksI

Figure 2: The inWalk graphical user interface (GUI)

ity degree. The query assistant is responsible of supportingthe user in selecting the portion of the inCloud to visualizeas the starting point for exploration. This assistant is incharge of supporting query processing (both keyword-basedand query-based modalities). Finally, the caching assistanthas the role of managing data loading/unloading from/tothe inWalk repository and the application memory.

3. THE INWALK SEARCH FUNCTIONAL-ITIES

The web interface of inWalk supports users in exploring andsearching a given inCloud.

At startup, the inWalk interface asks the user to select theinCloud to walk from a list of available inClouds stored inthe inWalk repository. Since the inCloud to show can belarge in size, a threshold mechanism is enforced to choosethe top-k most prominent clusters to show in the defaultview. The demonstration is based on an inCloud generatedfrom a real dataset AC extracted from Freebase. The ACdataset is a collection of about 2900 linked data resourcesand 400 classes with more than 4000 predicates describingboth sports and movies celebrities. The inCloud generatedfrom AC is composed by 44 clusters, 368 proximity links withan average prominence of 4.28. In the demonstration, thedefault view is generated by using the average prominencein the whole inCloud as selection threshold. The completeinCloud can be visualized by selecting the Full view button ofthe interface (Figure 2(E)). The default portion of inCloud(sub-cloud) shown at the inWalk startup can be visualizedby selecting the Default view button and contains 13 clustersof which 7 about movie and music celebrities and 6 aboutsports athletes. The main functionalities of inWalk will bedescribed by addressing specific user tasks, corresponding toincreasing levels of complexity of the inWalk functionalities.The inWalk demo is organized in two main user tasks.

User task 1 (U1 - thematic walk). In this task, userscreate thematic paths corresponding to their search inter-ests on the default view of the inCloud, without typing textor queries. For example, we start by selecting the clusterlabeled producer/film. This action activates the cluster andhighlights its proximity links. At the same time, a panel onthe right-hand side is shown to visualize the details of the ac-tive cluster, namely full cluster essential, resource preview,and resource relevance in the cluster, (see Figure 2(B-D)).The walk starts by selecting the neighbor cluster labeledamerica/award(4)2. This action adds a step in the walk aswell as the cluster america/award(4) to the current thematicpath (see Figure 3(A)). For the next walk step, we choose toclick on the slice labeled kingdom, award of america/award(4).This action highlights the cluster kingdom/award, which con-tains British actors. This cluster is now activated and wego one step ahead in the walk by choosing the neighborcluster labeled people/award. We decide to stop here thewalk, which originated the thematic path producer/film →america/award(4) → kingdom/award → people/award (see inFigure 3(A)).

User task 2 (U2 - inside walk). In this task, userssearch for a cluster of interest by exploiting the keywordsearch functionalities of inWalk (Figure 2(A)), where theycan freely type any keyword for search with the help ofan auto-completion mechanism, of a list of recent searches,and of the list of the most relevant keywords in the dictio-nary (Figure 3(B-D)). In particular, if we are interested insearching for sports, we can type the string “spor” and wechoose “sports” from the keywords suggested by the auto-completion. The result is a sub-cloud of 16 clusters, con-taining data about athletes. We then activate the clusteritaly/pro athlete. Cluster information and the linked data

2The numeric index is used to distinguish the clusters re-sulting with the same 〈label, type〉 descriptor.

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auto-completion suggestionsB

recent searches C most relevant

labels D

editable areaE

IQL query editorkeyword search panel

in-line helpGresult summaryF

thematic path walking panel

links to activate clusters A

Figure 3: Main panels of the inWalk search interface

contained in this cluster are shown in the right-side panel ofthe interface (see Figure 2(B,D)). This cluster can be cho-sen now for a walk-in-the-deep: the essential tells us thatthe cluster is about Italian athletes and contains 24 peopleand we can access a more detailed description of one of theathletes by selecting the athlete’s name and visualizing hercorresponding Freebase web page.

More complex user tasks based on the IQL editor will beillustrated in the demo (Figure 3(E-G)). The online proto-type provides a how-to guide to support the interested webusers in exploiting the syntax of the IQL language and informulating complex queries on the underlying inCloud.

4. RELATED WORKIn the last years, a lot of effort has been focused on automat-ically matching, classifying and representing the contents ofa knowledge repository. Examples of interesting solutionsin the literature are Google Knowledge Graph, Parallax, gFacet,and Microsoft Pivot. Mainly, the idea of these tools is to workon the presentation aspects of the Web of Data and to pro-vide functionalities for smart browsing of single data in theform of visual interfaces based on graphs, mashups, and his-tograms. However, these solutions are limited in providingmechanisms for shifting the granularity from a single dataitem to an aggregated set of objects with a single represen-tative. For this reason, their possible application to real sce-narios, especially in large-scale environments, still requiresfurther investigations and development improvements. Aspossible alternatives, the use of newly-discovered knowledgeassociations and visual navigation paths is proposed in [6,8, 9] to provide aggregation-based tools for exploration ofDBpedia and Freebase repositories.

With respect to state-of-the-art, we stress that the use ofinClouds as data organization structures capable of repre-senting similarity clusters equipped with cluster essentialsand proximity links is an enabling solution towards “object-driven” analysis and exploration of linked data. Moreover,the capability of inWalk to combine visual, interactive, andthematic exploration functionalities with intuitive and flexi-ble query functionalities through IQL is a new feature in thefield of web applications for linked data exploration.

5. CONCLUDING REMARKSThe inWalk demo presented in the paper is available online(http://islab.di.unimi.it/inwalk). The inWalk systemis based on inCloud, a flexible data structure that is alsosuited to work with data other than linked data. For in-stance, we experienced the use of inWalk to explore a datasetof social data extracted from Twitter. This further datasetis available for exploration in the online inWalk system.

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