01713nas a2200313 4500008004100000245006300041210005800104260001200162490000700174520076200181653002100943653002300964653003100987653002101018653002901039100001901068700002001087700001601107700002001123700003001143700002101173700002301194700002201217700001701239700001901256700001601275700001701291856009101308 2020 eng d00aThe Enslaved Ontology: Peoples of the Historic Slave Trade0 aEnslaved Ontology Peoples of the Historic Slave Trade c08/20200 v633 a
We present the Enslaved Ontology (V1.0) which was developed for integrating data about the historic slave trade from diverse sources in a use case driven by historians. Ontology development followed modular ontology design principles as derived from ontology design pattern application best practices and the eXtreme Design Methodology. Ontology content focuses on data about historic persons and the event records from which this data can be taken. It also incorporates provenance modeling and some temporal and spatial aspects. The ontology is available as serialized in the Web Ontology Language OWL, and carries modularization annotations using the Ontology Pattern Language (OPLa). It is available under the Creative Commons CC BY 4.0 license.
10adata integration10adigital humanities10ahistory of the slave trade10amodular ontology10aOntology Design Patterns1 aShimizu, Cogan1 aHitzler, Pascal1 aHirt, Quinn1 aRehberger, Dean1 aEstrecha, Seila, Gonzalez1 aFoley, Catherine1 aSheill, Alicia, M.1 aHawthorne, Walter1 aMixter, Jeff1 aWatrall, Ethan1 aCarty, Ryan1 aTarr, Duncan uhttps://daselab.cs.ksu.edu/publications/enslaved-ontology-peoples-historic-slave-trade02311nas a2200241 4500008004100000022001400041245006800055210006700123300001200190490000600202520156900208653002301777653002101800653001501821653001501836100003401851700002501885700002901910700001801939700002001957700002001977856007201997 2018 eng d a2352-864800aMachine learning for internet of things data analysis: a survey0 aMachine learning for internet of things data analysis a survey a161-1750 v43 aRapid developments in hardware, software, and communication technologies have facilitated the emergence of Internet-connected sensory devices that provide observations and data measurements from the physical world. By 2020, it is estimated that the total number of Internet-connected devices being used will be between 25 and 50 billion. As these numbers grow and technologies become more mature, the volume of data being published will increase. The technology of Internet-connected devices, referred to as Internet of Things (IoT), continues to extend the current Internet by providing connectivity and interactions between the physical and cyber worlds. In addition to an increased volume, the IoT generates big data characterized by its velocity in terms of time and location dependency, with a variety of multiple modalities and varying data quality. Intelligent processing and analysis of this big data are the key to developing smart IoT applications. This article assesses the various machine learning methods that deal with the challenges presented by IoT data by considering smart cities as the main use case. The key contribution of this study is the presentation of a taxonomy of machine learning algorithms explaining how different techniques are applied to the data in order to extract higher level information. The potential and challenges of machine learning for IoT data analytics will also be discussed. A use case of applying a Support Vector Machine (SVM) to Aarhus smart city traffic data is presented for a more detailed exploration.
10aInternet of Things10aMachine learning10aSmart City10aSmart data1 aMahdavinejad, Mohammad, Saeid1 aRezvan, Mohammadreza1 aBarekatain, Mohammadamin1 aAdibi, Peyman1 aBarnaghi, Payam1 aSheth, Amit, P. uhttps://www.sciencedirect.com/science/article/pii/S235286481730247X01810nas a2200289 4500008004100000245006000041210005700101260001300158300001200171490000900183520099400192653001601186653002901202653000801231100001801239700002001257700002401277700002201301700002101323700002001344700002101364700001901385700002401404700001901428700002301447856005001470 2013 eng d00aAn Ontology Design Pattern for Cartographic Map Scaling0 aOntology Design Pattern for Cartographic Map Scaling bSpringer a76–930 v78823 aThe concepts of scale is at the core of cartographic abstraction and mapping. It defines which geographic phenomena should be displayed, which type of geometry and map symbol to use, which measures can be taken, as well as the degree to which features need to be exaggerated or spatially displaced. In this work, we present an ontology design pattern for map scaling using the Web Ontology Language (OWL) within a particular extension of the OWL RL profile. We explain how it can be used to describe scaling applications, to reason over scale levels, and geometric representations. We propose an axiomatization that allows us to impose meaningful constraints on the pattern, and, thus, to go beyond simple surface semantics. Interestingly, this includes several functional constraints currently not expressible in any of the OWL profiles. We show that for this specific scenario, the addition of such constraints does not increase the reasoning complexity which remains tractable.
10aMap Scaling10aOntology Design Patterns10aOWL1 aCarral, David1 aScheider, Simon1 aJanowicz, Krzysztof1 aVardeman, Charles1 aKrisnadhi, Adila1 aHitzler, Pascal1 aCimiano, Philipp1 aCorcho, Óscar1 aPresutti, Valentina1 aHollink, Laura1 aRudolph, Sebastian uhttp://dx.doi.org/10.1007/978-3-642-38288-8_601920nas 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 aThe 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/10.1145/2309996.231000401617nas a2200229 4500008004100000245008100041210006900122300001100191490000600202520090100208653001401109653002901123653003001152653002901182653002301211100001201234700001501246700001701261700002001278700001701298856007201315 2010 eng d00aComputational Complexity and Anytime Algorithm for Inconsistency Measurement0 aComputational Complexity and Anytime Algorithm for Inconsistency a3–210 v43 aMeasuring inconsistency degrees of inconsistent knowledge bases is an important problem as it provides context information for facilitating inconsistency handling. Many methods have been proposed to solve this problem and a main class of them is based on some kind of paraconsistent semantics. In this paper, we consider the computational aspects of inconsistency degrees of propositional knowledge bases under 4-valued semantics. We first give a complete analysis of the computational complexity of computing inconsistency degrees. As it turns out that computing the exact inconsistency degree is intractable, we then propose an anytime algorithm that provides tractable approximations of the inconsistency degree from above and below. We show that our algorithm satisfies some desirable properties and give experimental results of our implementation of the algorithm
10aalgorithm10acomputational complexity10ainconsistency measurement10aKnowledge representation10amulti-valued logic1 aMa, Yue1 aQi, Guilin1 aXiao, Guohui1 aHitzler, Pascal1 aLin, Zuoquan uhttp://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=1