00568nas a2200181 4500008004100000245004900041210004800090260001300138300001400151100002500165700001800190700002000208700001900228700001700247700001600264700002000280856008600300 2012 eng d00aAlignment-based querying of linked open data0 aAlignmentbased querying of linked open data bSpringer a807–8241 aJoshi, Amit, Krishna1 aJain, Prateek1 aHitzler, Pascal1 aYeh, Peter, Z.1 aVerma, Kunal1 aSheth, Amit1 aDamova, Mariana uhttps://daselab.cs.ksu.edu/publications/alignment-based-querying-linked-open-data01920nas 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/10.1145/2309996.231000402232nas a2200289 4500008004100000245009000041210006900131260003900200300001200239490000900251520133400260100001801594700001901612700001701631700002701648700002001675700002001695700001601715700002301731700002101754700003101775700001801806700002601824700002401850700001801874856005001892 2011 eng d00aContextual Ontology Alignment of LOD with an Upper Ontology: A Case Study with Proton0 aContextual Ontology Alignment of LOD with an Upper Ontology A Ca aHeraklion, Crete, GreecebSpringer a80–920 v66433 aThe Linked Open Data (LOD) is a major milestone towards realizing the Semantic Web vision, and can enable applications such as robust Question Answering (QA) systems that can answer queries requiring multiple, disparate information sources. However, realizing these applications requires relationships at both the schema and instance level, but currently the LOD only provides relationships for the latter. To address this limitation, we present a solution for automatically finding schema-level links between two LOD ontologies – in the sense of ontology alignment. Our solution, called BLOOMS+, extends our previous solution (i.e. BLOOMS) in two significant ways. BLOOMS+ 1) uses a more sophisticated metric to determine which classes between two ontologies to align, and 2) considers contextual information to further support (or reject) an alignment. We present a comprehensive evaluation of our solution using schema-level mappings from LOD ontologies to Proton (an upper level ontology) – created manually by human experts for a real world application called FactForge. We show that our solution performed well on this task. We also show that our solution significantly outperformed existing ontology alignment solutions (including our previously published work on BLOOMS) on this same task.
1 aJain, Prateek1 aYeh, Peter, Z.1 aVerma, Kunal1 aVasquez, Reymonrod, G.1 aDamova, Mariana1 aHitzler, Pascal1 aSheth, Amit1 aAntoniou, Grigoris1 aGrobelnik, Marko1 aSimperl, Elena, Paslaru Bo1 aParsia, Bijan1 aPlexousakis, Dimitris1 aDe Leenheer, Pieter1 aPan, Jeff, Z. uhttp://dx.doi.org/10.1007/978-3-642-21034-1_601035nas a2200205 4500008004100000245003600041210003600077260003600113520044200149100001800591700002000609700001900629700001700648700001600665700001800681700002400699700001800723700002400741856006400765 2010 eng d00aLinked Data Is Merely More Data0 aLinked Data Is Merely More Data aStanford, California, USAbAAAI3 aIn this position paper, we argue that the Linked Open Data (LoD) Cloud, in its current form, is only of limited value for furthering the Semantic Web vision. Being merely a weakly linked “triple collection,” it will only be of very limited bene- fit for the AI or Semantic Web communities. We describe the corresponding problems with the LoD Cloud and give directions for research to remedy the situation.
1 aJain, Prateek1 aHitzler, Pascal1 aYeh, Peter, Z.1 aVerma, Kunal1 aSheth, Amit1 aBrickley, Dan1 aChaudhri, Vinay, K.1 aHalpin, Harry1 aMcGuinness, Deborah uhttp://www.aaai.org/ocs/index.php/SSS/SSS10/paper/view/113001773nas a2200277 4500008004100000245004400041210004400085260003000129300001400159490000900173520102400182100001801206700002001224700001601244700001701260700001901277700003101296700001301327700002001340700001601360700001501376700001801391700001801409700001701427856005101444 2010 eng d00aOntology Alignment for Linked Open Data0 aOntology Alignment for Linked Open Data aShanghai, ChinabSpringer a402–4170 v64963 aThe Web of Data currently coming into existence through the Linked Open Data (LOD) effort is a major milestone in realizing the Semantic Web vision. However, the development of applications based on LOD faces difficulties due to the fact that the different LOD datasets are rather loosely connected pieces of information. In particular, links between LOD datasets are almost exclusively on the level of instances, and schema-level information is being ignored. In this paper, we therefore present a system for finding schema-level links between LOD datasets in the sense of ontology alignment. Our system, called BLOOMS, is based on the idea of bootstrapping information already present on the LOD cloud. We also present a comprehensive evaluation which shows that BLOOMS outperforms state-of-the-art ontology alignment systems on LOD datasets. At the same time, BLOOMS is also competitive compared with these other systems on the Ontology Evaluation Alignment Initiative Benchmark datasets.
1 aJain, Prateek1 aHitzler, Pascal1 aSheth, Amit1 aVerma, Kunal1 aYeh, Peter, Z.1 aPatel-Schneider, Peter, F.1 aPan, Yue1 aHitzler, Pascal1 aMika, Peter1 aZhang, Lei1 aPan, Jeff, Z.1 aHorrocks, Ian1 aGlimm, Birte uhttp://dx.doi.org/10.1007/978-3-642-17746-0_26