01378nas a2200193 4500008004100000245005700041210005700098260002100155300001400176520079900190653001900989653002601008653002701034100002301061700002001084700001601104700001801120856004601138 2013 eng d00aAutomatic Domain Identification for Linked Open Data0 aAutomatic Domain Identification for Linked Open Data aAtlanta, GA, USA a205–2123 a

Linked Open Data (LOD) has emerged as one of the largest collections of interlinked structured datasets on the Web. Although the adoption of such datasets for applications is increasing, identifying relevant datasets for a specific task or topic is still challenging. As an initial step to make such identification easier, we provide an approach to automatically identify the topic domains of given datasets. Our method utilizes existing knowledge sources, more specifically Freebase, and we present an evaluation which validates the topic domains we can identify with our system. Furthermore, we evaluate the effectiveness of identified topic domains for the purpose of finding relevant datasets, thus showing that our approach improves reusability of LOD datasets.

10adataset search10aDomain Identification10aLinked Open Data Cloud1 aLalithsena, Sarasi1 aHitzler, Pascal1 aSheth, Amit1 aJain, Prateek uhttp://dx.doi.org/10.1109/WI-IAT.2013.20601920nas a2200229 4500008004100000245007300041210006900114260002800183300001200211520122300223653002701446653001401473653002101487100001801508700002001526700001701546700001901563700001601582700002201598700002301620856004701643 2012 eng d00aMoving beyond SameAs with PLATO: Partonomy detection for Linked Data0 aMoving beyond SameAs with PLATO Partonomy detection for Linked D aMilwaukee, WI, USAbACM a33–423 a

The Linked Open Data (LOD) Cloud has gained significant traction over the past few years. With over 275 interlinked datasets across diverse domains such as life science, geography, politics, and more, the LOD Cloud has the potential to support a variety of applications ranging from open domain question answering to drug discovery.

Despite its significant size (approx. 30 billion triples), the data is relatively sparely interlinked (approx. 400 million links). A semantically richer LOD Cloud is needed to fully realize its potential. Data in the LOD Cloud are currently interlinked mainly via the owl:sameAs property, which is inadequate for many applications. Additional properties capturing relations based on causality or partonomy are needed to enable the answering of complex questions and to support applications.

In this paper, we present a solution to enrich the LOD Cloud by automatically detecting partonomic relationships, which are well-established, fundamental properties grounded in linguistics and philosophy. We empirically evaluate our solution across several domains, and show that our approach performs well on detecting partonomic properties between LOD Cloud data.

10aLinked Open Data Cloud10aMereology10aPart of Relation1 aJain, Prateek1 aHitzler, Pascal1 aVerma, Kunal1 aYeh, Peter, Z.1 aSheth, Amit1 aMunson, Ethan, V.1 aStrohmaier, Markus uhttp://doi.acm.org/10.1145/2309996.2310004