TY - JOUR T1 - The Enslaved Ontology: Peoples of the Historic Slave Trade JF - Journal of Web Semantics Y1 - 2020 A1 - Cogan Shimizu A1 - Pascal Hitzler A1 - Quinn Hirt A1 - Dean Rehberger A1 - Seila Gonzalez Estrecha A1 - Catherine Foley A1 - Alicia M. Sheill A1 - Walter Hawthorne A1 - Jeff Mixter A1 - Ethan Watrall A1 - Ryan Carty A1 - Duncan Tarr KW - data integration KW - digital humanities KW - history of the slave trade KW - modular ontology KW - Ontology Design Patterns AB -

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.

VL - 63 ER - TY - JOUR T1 - Machine learning for internet of things data analysis: a survey JF - Digital Communications and Networks Y1 - 2018 A1 - Mohammad Saeid Mahdavinejad A1 - Mohammadreza Rezvan ED - Mohammadamin Barekatain ED - Peyman Adibi ED - Payam Barnaghi ED - Amit P. Sheth KW - Internet of Things KW - Machine learning KW - Smart City KW - Smart data AB -

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.

VL - 4 UR - https://www.sciencedirect.com/science/article/pii/S235286481730247X ER - TY - CONF T1 - An Ontology Design Pattern for Cartographic Map Scaling T2 - The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings Y1 - 2013 A1 - David Carral A1 - Simon Scheider A1 - Krzysztof Janowicz A1 - Charles Vardeman A1 - Adila Krisnadhi A1 - Pascal Hitzler ED - Philipp Cimiano ED - Óscar Corcho ED - Valentina Presutti ED - Laura Hollink ED - Sebastian Rudolph KW - Map Scaling KW - Ontology Design Patterns KW - OWL AB -

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.

JF - The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings PB - Springer VL - 7882 UR - http://dx.doi.org/10.1007/978-3-642-38288-8_6 ER - TY - CONF T1 - Moving beyond SameAs with PLATO: Partonomy detection for Linked Data T2 - 23rd ACM Conference on Hypertext and Social Media, HT '12 Y1 - 2012 A1 - Prateek Jain A1 - Pascal Hitzler A1 - Kunal Verma A1 - Peter Z. Yeh A1 - Amit Sheth ED - Ethan V. Munson ED - Markus Strohmaier KW - Linked Open Data Cloud KW - Mereology KW - Part of Relation AB -

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.

JF - 23rd ACM Conference on Hypertext and Social Media, HT '12 PB - ACM CY - Milwaukee, WI, USA UR - http://doi.acm.org/10.1145/2309996.2310004 ER - TY - JOUR T1 - Computational Complexity and Anytime Algorithm for Inconsistency Measurement JF - International Journal of Software and Informatics Y1 - 2010 A1 - Yue Ma A1 - Guilin Qi A1 - Guohui Xiao A1 - Pascal Hitzler A1 - Zuoquan Lin KW - algorithm KW - computational complexity KW - inconsistency measurement KW - Knowledge representation KW - multi-valued logic AB -

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

VL - 4 UR - http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41&flag=1 ER -