@conference {888, title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids}, booktitle = {Proceedings of the 29th International Conference on Advances in Geographic Information Systems}, year = {2021}, pages = {305{\textendash}308}, publisher = {Association for Computing Machinery}, organization = {Association for Computing Machinery}, address = {New York, NY, USA}, abstract = {

We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.

}, keywords = {Hierarchical Grids, Knowledge Graphs, RCC5}, isbn = {9781450386647}, doi = {10.1145/3474717.3483965}, url = {https://doi.org/10.1145/3474717.3483965}, author = {Zalewski, Joseph and Hitzler, Pascal and Janowicz, Krzysztof} } @article {864, title = {Semantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)}, year = {2021}, abstract = {

We propose the combining of region connection calculi with nested hierarchical grids for representing spatial region data in the context of knowledge graphs, thereby avoiding reliance on vector representations. We present a resulting region calculus, and provide qualitative and formal evidence that this representation can be favorable with large data volumes in the context of knowledge graphs; in particular we study means of efficiently choosing which triples to store to minimize space requirements when data is represented this way, and we provide an algorithm for finding the smallest possible set of triples for this purpose including an asymptotic measure of the size of this set for a special case. We prove that a known constraint calculus is adequate for the reconstruction of all triples describing a region from such a pruned representation, but problematic for reasoning with hierarchical grids in general.

}, keywords = {Hierarchical Grids, Knowledge Graphs, RCC5}, author = {Joseph Zalewski and Pascal Hitzler and Krzysztof Janowicz} } @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} } @article {775, title = {Multimodal mental health analysis in social media}, journal = {PLoS ONE}, year = {2020}, abstract = {

p.p1 {margin: 0.0px 0.0px 0.0px 0.0px; font: 9.5px Helvetica}

Depression is a major public health concern in the U.S. and globally. While successful early

identification and treatment can lead to many positive health and behavioral outcomes,

depression, remains undiagnosed, untreated or undertreated due to several reasons,

including denial of the illness as well as cultural and social stigma. With the ubiquity of social

media platforms, millions of people are now sharing their online persona by expressing their

thoughts, moods, emotions, and even their daily struggles with mental health on social

media. Unlike traditional observational cohort studies conducted through questionnaires

and self-reported surveys, we explore the reliable detection of depressive symptoms from

tweets obtained, unobtrusively. Particularly, we examine and exploit multimodal big (social)

data to discern depressive behaviors using a wide variety of features including individuallevel

demographics. By developing a multimodal framework and employing statistical techniques

to fuse heterogeneous sets of features obtained through the processing of visual,

textual, and user interaction data, we significantly enhance the current state-of-the-art

approaches for identifying depressed individuals on Twitter (improving the average F1-

Score by 5 percent) as well as facilitate demographic inferences from social media. Besides

providing insights into the relationship between demographics and mental health, our

research assists in the design of a new breed of demographic-aware health interventions.

}, keywords = {Explainable Machine Learning, Hypothesis Testing, National Language Processing, Prediction, Regression}, url = {https://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248\&type=printable}, author = {Amir Hossein Yazdavar and Mohammad Saeid Mahdavinejad and Goonmeet Baja and William Romine and Amit Sheth and Amir Hassan Monadjemi and Krishnaprasad Thirunarayan and John M. Meddar and Annie Myers and Jyotishman Pathak and Pascal Hitzler} } @conference {758, title = {Tracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption}, booktitle = {2nd International Conference on Sensors, Signal and Image Processing (SSIP 19)}, year = {2019}, publisher = {ACM}, organization = {ACM}, address = {Prague, Czech Republic}, abstract = {

People are living longer than ever due to advances in healthcare, and this has prompted many healthcare providers to look towards remote patient care as a means to meet the needs of the future. It is now a priority to enable people to reside in their own homes rather than in overburdened facilities whenever possible. The increasing maturity of IoT technologies and the falling costs of connected sensors has made the deployment of remote healthcare at scale an increasingly attractive prospect. In this work we demonstrate that we can measure the consistency and regularity of the behaviour of a household using sensor readings generated from interaction with the home environment. We show that we can track changes in this behaviour regularity longitudinally and detect changes that may be related to significant life events or trends that may be medically significant. We achieve this using periodicity analysis on water usage readings sampled from the main household water meter every 15 minutes for over 8 months. We utilise an IoT Application Enablement Platform in conjunction with low cost LoRa-enabled sensors and a Low Power Wide Area Network in order to validate a data collection methodology that could be deployed at large scale in future. We envision the statistical methods described here being applied to data streams from the homes of elderly and at-risk groups, both as a means of\  early illness\  detection\  and\  for\  monitoring\  the well-being of those with known illnesses.

}, keywords = {Ambient Assisted Living, Home Monitoring, Internet of Things, Sensor Applications, Sensor Networks}, author = {Quinn, Se{\'a}n and Murphy, Noel and Smeaton, Alan F.} } @conference {610, title = {A Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation}, booktitle = {Workshop on Ontology Design Patterns (WOP)}, year = {2016}, month = {10/2016}, abstract = {

The Hazardous Situation ontology design pattern models the consequences of exposure of an object to a hazard. In its current form, the ODP is well suited for representing the consequences of exposure after the fact, which is very useful for applications such as damage assessment and recovery planning. In this work, we present a modification to this pattern that enables it to additionally support proactive questions central to risk assessment and mitigation planning.

}, keywords = {hazard, Ontology Design Pattern, risk assessment, risk mitigation}, author = {Michelle Cheatham and Ferguson, Holly and Charles, Vardeman and Cogan Shimizu} } @conference {133, title = {All But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles}, booktitle = {Proceedings of the 11th International Workshop on OWL: Experiences and Directions (OWLED 2014) co-located with 13th International Semantic Web Conference on (ISWC 2014), Riva del Garda, Italy, October 17-18, 2014.}, volume = {1265}, year = {2014}, month = {10/2014}, pages = {97-108}, publisher = {CEUR-WS.org}, organization = {CEUR-WS.org}, abstract = {We show that occurrences of the universal quantifier in the left-hand side of general concept inclusions can be rewritten into EL++ axioms under certain circumstances. I.e., this intuitive modeling feature is available for OWL EL while retaining tractability. Furthermore, this rewriting makes it possible to reason over corresponding extensions of EL++ and Horn-SROIQ using standard reasoners.}, keywords = {description logics, Horn Logics, OWL}, url = {http://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf}, author = {David Carral and Adila Krisnadhi and Sebastian Rudolph and Pascal Hitzler}, editor = {C. Maria Keet and Valentina A. M. Tamma} } @article {60, title = {Complexities of Horn Description Logics}, journal = {ACM Trans. Comput. Log.}, volume = {14}, year = {2013}, pages = {2}, abstract = {Description Logics (DLs) have become a prominent paradigm for representing knowledge bases in a variety of application areas. Central to leveraging them for corresponding systems is the provision of a favourable balance between expressivity of the knowledge representation formalism on the one hand, and runtime performance of reasoning algorithms on the other. Due to this, Horn description logics (Horn DLs) have attracted attention since their (worst-case) data complexities are in general lower than their overall (i.e. combined) complexities, which makes them attractive for reasoning with large sets of instance data (ABoxes). However, the natural question whether Horn DLs also provide advantages for schema (TBox) reasoning has hardly been addressed so far. In this paper, we therefore provide a thorough and comprehensive analysis of the combined complexities of Horn DLs. While the combined complexity for many Horn DLs studied herein turns out to be the same as for their non-Horn counterparts, we identify subboolean DLs where Hornness simplifies reasoning. We also provide convenient normal forms for Horn DLs.}, keywords = {computational complexity, description logics, Horn logic}, doi = {10.1145/2422085.2422087}, url = {http://doi.acm.org/10.1145/2422085.2422087}, author = {Markus Kr{\"o}tzsch and Sebastian Rudolph and Pascal Hitzler} }