%0 Journal Article %J Journal of Web Semantics %D 2020 %T The Enslaved Ontology: Peoples of the Historic Slave Trade %A Cogan Shimizu %A Pascal Hitzler %A Quinn Hirt %A Dean Rehberger %A Seila Gonzalez Estrecha %A Catherine Foley %A Alicia M. Sheill %A Walter Hawthorne %A Jeff Mixter %A Ethan Watrall %A Ryan Carty %A Duncan Tarr %K data integration %K digital humanities %K history of the slave trade %K modular ontology %K Ontology Design Patterns %X

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.

%B Journal of Web Semantics %V 63 %8 08/2020 %G eng %R https://doi.org/10.1016/j.websem.2020.100567 %0 Journal Article %J Digital Communications and Networks %D 2018 %T Machine learning for internet of things data analysis: a survey %A Mohammad Saeid Mahdavinejad %A Mohammadreza Rezvan %E Mohammadamin Barekatain %E Peyman Adibi %E Payam Barnaghi %E Amit P. Sheth %K Internet of Things %K Machine learning %K Smart City %K Smart data %X

Rapid 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.

%B Digital Communications and Networks %V 4 %P 161-175 %G eng %U https://www.sciencedirect.com/science/article/pii/S235286481730247X %R https://doi.org/10.1016/j.dcan.2017.10.002 %0 Conference Paper %B The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings %D 2013 %T An Ontology Design Pattern for Cartographic Map Scaling %A David Carral %A Simon Scheider %A Krzysztof Janowicz %A Charles Vardeman %A Adila Krisnadhi %A Pascal Hitzler %E Philipp Cimiano %E Óscar Corcho %E Valentina Presutti %E Laura Hollink %E Sebastian Rudolph %K Map Scaling %K Ontology Design Patterns %K OWL %X

The 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.

%B The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings %I Springer %V 7882 %P 76–93 %G eng %U http://dx.doi.org/10.1007/978-3-642-38288-8_6 %R 10.1007/978-3-642-38288-8_6 %0 Conference Paper %B 23rd ACM Conference on Hypertext and Social Media, HT '12 %D 2012 %T Moving beyond SameAs with PLATO: Partonomy detection for Linked Data %A Prateek Jain %A Pascal Hitzler %A Kunal Verma %A Peter Z. Yeh %A Amit Sheth %E Ethan V. Munson %E Markus Strohmaier %K Linked Open Data Cloud %K Mereology %K Part of Relation %X

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.

%B 23rd ACM Conference on Hypertext and Social Media, HT '12 %I ACM %C Milwaukee, WI, USA %P 33–42 %G eng %U http://doi.acm.org/10.1145/2309996.2310004 %R 10.1145/2309996.2310004 %0 Journal Article %J International Journal of Software and Informatics %D 2010 %T Computational Complexity and Anytime Algorithm for Inconsistency Measurement %A Yue Ma %A Guilin Qi %A Guohui Xiao %A Pascal Hitzler %A Zuoquan Lin %K algorithm %K computational complexity %K inconsistency measurement %K Knowledge representation %K multi-valued logic %X

Measuring 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

%B International Journal of Software and Informatics %V 4 %P 3–21 %G eng %U http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=1