@booklet {990, title = {An Ontology for Representing Curriculum and Learning Material}, year = {2025}, abstract = {Educational, learning, and training materials have become extremely commonplace across the Internet. Yet, they frequently remain disconnected from each other, fall into platform silos, and so on. One way to overcome this is to provide a mechanism to integrate the material and provide cross-links across topics. In this paper, we present the Curriculum KG Ontology, which we use as a framework for the dense interlinking of educational materials, by first starting with organizational and broad pedagogical principles. We provide a materialized graph for the Prototype Open Knowledge Network use-case, and validate it using competency questions sourced from domain experts and educators. }, keywords = {education, modular ontology design, ontology}, doi = {10.48550/arXiv.2506.05751}, url = {https://arxiv.org/abs/2506.05751}, author = {Antrea Christou and Chris Davis Jaldi and Joseph Zalewski and Hande Kucuk McGinty and Pascal Hitzler and Cogan Shimizu} } @article {784, title = {The Enslaved Ontology: Peoples of the Historic Slave Trade}, journal = {Journal of Web Semantics}, volume = {63}, year = {2020}, month = {08/2020}, abstract = {

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.

}, keywords = {data integration, digital humanities, history of the slave trade, modular ontology, Ontology Design Patterns}, doi = {https://doi.org/10.1016/j.websem.2020.100567}, author = {Cogan Shimizu and Pascal Hitzler and Quinn Hirt and Dean Rehberger and Seila Gonzalez Estrecha and Catherine Foley and Alicia M. Sheill and Walter Hawthorne and Jeff Mixter and Ethan Watrall and Ryan Carty and Duncan Tarr} } @conference {129, title = {An Ontology Design Pattern for Cartographic Map Scaling}, booktitle = {The Semantic Web: Semantics and Big Data, 10th International Conference, ESWC 2013, Montpellier, France, May 26-30, 2013. Proceedings}, volume = {7882}, year = {2013}, pages = {76{\textendash}93}, publisher = {Springer}, organization = {Springer}, abstract = {

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.

}, keywords = {Map Scaling, Ontology Design Patterns, OWL}, doi = {10.1007/978-3-642-38288-8_6}, url = {http://dx.doi.org/10.1007/978-3-642-38288-8_6}, author = {David Carral and Simon Scheider and Krzysztof Janowicz and Charles Vardeman and Adila Krisnadhi and Pascal Hitzler}, editor = {Philipp Cimiano and {\'O}scar Corcho and Valentina Presutti and Laura Hollink and Sebastian Rudolph} } @conference {97, title = {Moving beyond SameAs with PLATO: Partonomy detection for Linked Data}, booktitle = {23rd ACM Conference on Hypertext and Social Media, HT {\textquoteright}12}, year = {2012}, pages = {33{\textendash}42}, publisher = {ACM}, organization = {ACM}, address = {Milwaukee, WI, USA}, abstract = {

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.

}, keywords = {Linked Open Data Cloud, Mereology, Part of Relation}, doi = {10.1145/2309996.2310004}, url = {http://doi.acm.org/10.1145/2309996.2310004}, author = {Prateek Jain and Pascal Hitzler and Kunal Verma and Peter Z. Yeh and Amit Sheth}, editor = {Ethan V. Munson and Markus Strohmaier} } @article {83, title = {Computational Complexity and Anytime Algorithm for Inconsistency Measurement}, journal = {International Journal of Software and Informatics}, volume = {4}, year = {2010}, pages = {3{\textendash}21}, abstract = {

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

}, keywords = {algorithm, computational complexity, inconsistency measurement, Knowledge representation, multi-valued logic}, url = {http://www.ijsi.org/ch/reader/view_abstract.aspx?file_no=i41\&flag=1}, author = {Yue Ma and Guilin Qi and Guohui Xiao and Pascal Hitzler and Zuoquan Lin} }