01537nas a2200157 4500008004100000245009300041210006900134520093500203653002301138653002101161653000901182100002101191700002001212700002401232856012301256 2021 eng d00aSemantic Compression with Region Calculi in Nested Hierarchical Grids (Technical Report)0 aSemantic Compression with Region Calculi in Nested Hierarchical 3 a

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.

10aHierarchical Grids10aKnowledge Graphs10aRCC51 aZalewski, Joseph1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://daselab.cs.ksu.edu/publications/semantic-compression-region-calculi-nested-hierarchical-grids-technical-report01566nas a2200193 4500008004100000020001800041245007400059210006900133260005900202300001400261520093500275653002301210653002101233653000901254100002101263700002001284700002401304856004401328 2021 eng d a978145038664700aSemantic Compression with Region Calculi in Nested Hierarchical Grids0 aSemantic Compression with Region Calculi in Nested Hierarchical  aNew York, NY, USAbAssociation for Computing Machinery a305–3083 a

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.

10aHierarchical Grids10aKnowledge Graphs10aRCC51 aZalewski, Joseph1 aHitzler, Pascal1 aJanowicz, Krzysztof uhttps://doi.org/10.1145/3474717.348396501713nas 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-trade02855nas a2200277 4500008004100000245005400041210005400095520195300149653003302102653002302135653003302158653001502191653001502206100002802221700003402249700001902283700002002302700001602322700002802338700003202366700002102398700001702419700002302436700002002459856009802479 2020 eng d00aMultimodal mental health analysis in social media0 aMultimodal mental health analysis in social media3 a

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.

10aExplainable Machine Learning10aHypothesis Testing10aNational Language Processing10aPrediction10aRegression1 aYazdavar, Amir, Hossein1 aMahdavinejad, Mohammad, Saeid1 aBaja, Goonmeet1 aRomine, William1 aSheth, Amit1 aMonadjemi, Amir, Hassan1 aThirunarayan, Krishnaprasad1 aMeddar, John, M.1 aMyers, Annie1 aPathak, Jyotishman1 aHitzler, Pascal uhttps://journals.plos.org/plosone/article/file?id=10.1371/journal.pone.0226248&type=printable02265nas a2200193 4500008004100000245009900041210006900140260003200209520153800241653002801779653002001807653002301827653002401850653002001874100001701894700001701911700002201928856012101950 2019 eng d00aTracking Human Behavioural Consistency by Analysing Periodicity of Household Water Consumption0 aTracking Human Behavioural Consistency by Analysing Periodicity  aPrague, Czech RepublicbACM3 a

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.

10aAmbient Assisted Living10aHome Monitoring10aInternet of Things10aSensor Applications10aSensor Networks1 aQuinn, Seán1 aMurphy, Noel1 aSmeaton, Alan, F. uhttps://daselab.cs.ksu.edu/publications/tracking-human-behavioural-consistency-analysing-periodicity-household-water01175nas a2200193 4500008004100000245009200041210006900133260001200202520048400214653001100698653002800709653002000737653002000757100002300777700002000800700002200820700001900842856012000861 2016 eng d00aA Modification to the Hazardous Situation ODP to Support Risk Assessment and Mitigation0 aModification to the Hazardous Situation ODP to Support Risk Asse c10/20163 a

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.

10ahazard10aOntology Design Pattern10arisk assessment10arisk mitigation1 aCheatham, Michelle1 aFerguson, Holly1 aCharles, Vardeman1 aShimizu, Cogan uhttps://daselab.cs.ksu.edu/publications/modification-hazardous-situation-odp-support-risk-assessment-and-mitigation01097nas a2200229 4500008004100000245008000041210006900121260002500190300001100215490000900226520039900235653002300634653001600657653000800673100001800681700002100699700002300720700002000743700001600763700002800779856006000807 2014 eng d00aAll But Not Nothing: Left-Hand Side Universals for Tractable {OWL} Profiles0 aAll But Not Nothing LeftHand Side Universals for Tractable OWL P bCEUR-WS.orgc10/2014 a97-1080 v12653 aWe 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.10adescription logics10aHorn Logics10aOWL1 aCarral, David1 aKrisnadhi, Adila1 aRudolph, Sebastian1 aHitzler, Pascal1 aKeet, Maria1 aTamma, Valentina, A. M. uhttp://ceur-ws.org/Vol-1265/owled2014_submission_13.pdf01631nas a2200181 4500008004100000245004400041210004400085300000600129490000700135520112800142653002901270653002301299653001501322100002201337700002301359700002001382856004701402 2013 eng d00aComplexities of Horn Description Logics0 aComplexities of Horn Description Logics a20 v143 aDescription 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.10acomputational complexity10adescription logics10aHorn logic1 aKrötzsch, Markus1 aRudolph, Sebastian1 aHitzler, Pascal uhttp://doi.acm.org/10.1145/2422085.2422087