Arndt, Dörthe

Bhatti, J

Bonte, Pieter

De Meester, Ben

De Turck, Filip

Dereuddre, W

Mannens, Erik

Ongenae, Femke

Schaballie, Jeroen

Van de Walle, Rik

Verborgh, Ruben

Verstichel, Stijn

English

Ghent University Academic Bibliography

Evaluation and Optimized Usage of OWL 2Reasoners in an Event-based eHealth Context.Pieter Bonte1, Femke Ongenae1, Jeroen Schaballie1, Ben De Meester1, Do¨rtheArndt1, Wim Dereuddre2, Jabran Bhatti2, Stijn Verstichel1, Ruben Verborgh1,Rik Van de Walle1, Erik Mannens1, and Filip De Turck11 Ghent University - iMinds, Gaston Crommenlaan 8, 9000 Ghent, BelgiumPieter.Bonte@intec.ugent.be2 Televic HealthCare, Leo Bekaertlaan 1, 8870 IzegemAbstract This paper evaluates the performance of the OWL 2 reason-ers Pellet and HermiT in an eHealth context where most of the ABoxis considered static and discrete transient events describing the environ-ment are incrementally added and processed. The considered use caseis the assignment of tasks and calls to nurses. To provide personalizedand optimized care, the selection process utilizes reasoning to make in-telligent assignment decisions based on the available information. Thishas been implemented using multiple SPARQL-queries to enable easyadaptation of the assignment algorithm. Since limited time is availableto perform the assignments, the decision should be made in at most fiveseconds. An analysis of the performance and scalability of the reasonersis presented. To deal with the limited time frame, several optimizationsare suggested, which exploit that most of the ABox is considered static.Keywords: eHealth, Evaluation, Event-based, OWL2 Reasoners1 IntroductionTo provide personalized care for patients, task and call assignment systems ben-efit from considering as much information as possible. Most of this info canbe considered relatively static, e.g., the patient’s profile and pathology and thecompetences of the staff. Discrete events representing tasks, calls and changesto the environment, e.g., person location updates, are incrementally added andprocessed. Scalable reasoning on this static data, which is incrementally up-dated with event data, is thus required to assign the most appropriate staff totasks and calls. The selection procedure, represented as a decision tree, has beenimplemented using multiple SPARQL-queries, allowing easy adaptation of thealgorithm. Since the selection procedure should happen as fast as possible, theallowed decision time has been limited to five seconds by domain experts.In this paper, the developed task and call assignment reasoning platformis evaluated in terms of scalability using the OWL-2 reasoners Pellet [10] andHermiT [8] based on hospital data with an increasing number of wards. Moreover,several optimizations to speed up the reasoning are proposed and evaluated.These optimizations are able to deal with the event-based and time-constraintscenarios and are able to execute the numerous SPARQL-queries efficiently.22 The ScenarioThe considered scenario consists of a hospital with wards, containing patientsand care staff. The patients execute calls to receive medical aid. The locationsof the medical staff are automatically tracked. When a patient is in need ofaid, the decision tree is checked to determine the most suited staff member,based on the patient’s pathology and profile, the location of the care staff, etc.The scenario consists of following steps, with the performed reasoning actionsbetween brackets:Call Launched: A patient launches a call (select nurse)Call Redirect: The nurse indicates that she is busy (select new nurse)Call Temporary Accept: The new nurse accepts the call (update call status)Corridor: The nurse moves towards the room of the patient (update location)Patient loc: The nurse arrives in the room (update location & turn on lights)Presence On: The nurse logs into the terminal (update call status & turn onlights)Presence Off: The nurse logs out (update call status & turn off lights)Corridor loc: The nurse leaves the room (update location & turn off lights)In the considered eHealth use case, the static data consists of the hospitalconfiguration and profile information of the patients and care staff. The dynamicdata are typically calls by the patients to receive aid, updates of the status ofthe call or location updates by the staff.3 The Accio OntologyTo represent the eHealth knowledge, the ACCIO ontology3 is used. An elaboratedescription can be found in Ongenae, et al. [7].We evaluate the scalability of the OWL 2 reasoners by executing the scenarioover an increasing number of wards. The details of the ontology loaded with thepreliminary data for each number of wards is summarized in Table 1. The loadeddata consists of the configuration of the different wards, including the personneland patient information. Note that these are the numbers of the static part ofthe ABox. During the scenario, the TBox is considered static.#Wards: 1 10 25 50 75 100Axioms 3412 11113 20349 41098 63869 85564Logical Axioms 2109 8167 15426 32389 49670 66741Individuals 270 1913 3890 8464 13166 17790Classes 332Object Properties 182Data Properties 51DL Expressivity SHOIQ(D)Table 1. Summary of the ACCIO ontology for different number of wards3 http://users.intec.ugent.be/pieter.bonte/ontology/accio.html34 ImplementationTo implement the scenario, we utilized the ModulAr, Service, Semantic & Flex-ible Platform (MASSIF), a data-driven platform that allows the easy develop-ment and collaboration of (ontology-based) services. Each service performs adistinguished reasoning task. Services exchange their knowledge over a SemanticCommunication Bus (SCB) [2]. A detailed description of the MASSIF platformcan be found in De Backere, et al. [1].Four Services, performing different reasoning tasks, were implemented. Theseare elaborated below, in followed by the number of queries needed to implementtheir logic.Presence Service: tracks the location of the staff – #queries: 2.Status Call Service: tracks the status of the calls – #queries: 4.Light Service: handles the lights in the patient rooms – #queries: 9.Help Selection Service: handles the staff assignment – #queries: 200.Even though the Help Selection Service implements 200 queries, the realnumber of executed queries depends on the number of branches that need to bechecked in the decision tree, which represents the nurse assignment algorithm.The OWL API [4] is used to internally represent the ontology in the MAS-SIF platform. It provides an OWLReasoner -interface, offering an uniform accesspoint to various reasoners. In this paper we evaluate the most popular reasonersimplementing the interface. Unfortunately, the OWL API does not provide anySPARQL support. To resolve this matter a Jena model [5] was used that al-lows SPARQL queries through Jena ARQ. This model requires synchronizationwith the OWL API. Only Pellet is able to convert its internal knowledge baseto Jena. Utilizing HermiT or a more optimized use of Pellet requires a directconversion from OWL API to Jena, this is achieved by writing the ontology toa outputstream in RDF/XML format and reading this stream in Jena.Different approaches were evaluated for using the reasoners, of which thedistinctive steps are visualized in Figure 1. Two flows can be discerned. A flowat the top describing the precomputation steps at start-up and one at the bot-tom describing the reasoning steps during the execution of the scenario. Beforediscussing them in detail, we elaborate the on two implemented optimizationsfor executing numerous queries in an event-based context.Figure 1. Workflow depicting the various approaches44.1 MaterializationMaterialization is the process of precomputing key sets of implicit assertions inthe knowledge base and is frequently employed by semantic query and reasoningengines to improve query performance [6]. Doing so requires more storage, butit allows easy look-up at run-time [9], because it can bypass reasoning whenexecuting queries. This step is depicted in Figure 1 as (g).Adding additional event data (ABox) to a materialized ontology requiresrealization, i.e., computing the direct types of the added individuals. Based ona previous classification, the class hierarchy can be used to retrieve all types.4.2 Subset ReasoningWhen adding new individuals to a materialized ontology, subset reasoning re-trieves a subset of ABox data in such a way that all necessary data to calculatethe types of the new individuals can be achieved with a minimal data set. Thesize of the subset is ontology dependent and determined through a precomputionstep, which finds the TBox axiom with the largest depth. This is depicted in Fig-ure 1 as step (h). The depth of an axiom is similar to the modal depth and definesthe deepest nesting of the operators. A formal definition can be found below.We define the axiom depth as d, R as the roles and C as the concepts.θ = C|∀R.θ|∃R.θd(θ1 ∧ θ2) = max(d(θ1), d(θ2))d(θ1 ∨ θ2) = max(d(θ1), d(θ2))d(C) = 0d(∀R.θ) = 1 + d(θ)d(∃R.θ) = 1 + d(θ)(1)The calculated depth defines the size of the dataset necessary to calculate thetypes of new individuals in the materialized ontology. If we view the ontology asa graph with the individuals as vertexes and the relations as edges, the subsetof data needed to calculate the types is a subtree of the graph with as root theindividual and as depth the calculated axiom depth. If the types of multipleindividuals need to be calculated, a union of subtrees can be considered.A subset is sufficient to determine the types of an individual. Only the in-dividuals/literals with whom the given individual has a relation have influence,considering the calculated depth and the fact that all needed inferred data hasbeen calculated in a previous materialization step. For this approach to preservecompleteness, the TBox describing the new individuals should be part of anontology definition T that can be seen as an extension of the static ontology def-inition T ′ and if T is local, it does not yield new consequences in T ′. A definitionof locality can be found in Grau, et al. [3]. Transitive relations to leafs in thesubtree could also cause incompleteness, since the transitivity could possibly notbe fulfilled because of the limited data in the subset. However, this never occursin our scenario. The maximum axiom depth for the ACCIO ontology is three.Compared to existing modularization techniques [11], our technique only ex-tracts ABox data, preserving the TBox allows dynamic and performant extrac-tion at runtime. Since the ontology has been materialized, the extracted datacan be limited to the data that directly influences the calculation of the newtypes. All other data has been inferred in a previous step.54.3 The ApproachesThe different approaches used to perform the reasoning are discussed below.Each approach is depicted in Figure 1 as a sequence of steps.1. Pellet is used to perform the necessary reasoning each time a query isexecuted. Pellet performs the conversion of its internal knowledge base to a JenaModel each time an event arrives, but is able to cache intermediate results. Thisis depicted in Figure 1 as step (a).2. To eliminate the reasoning at query time, a full materialization of theontology is calculated at arrival of an event and translated from the OWL APIto the Jena Model. Jena ARQ allows querying without any reasoning. This isdepicted as step (b) and has been evaluated with the HermiT reasoner.3. Instead of calculating the whole inferred model each time an event arrives,the static data is materialized as a pre-computation step. For each individual inthe arriving data, its types are calculated based on the materialized ontology andthe OWL API and the Jena model are incremented with this inferred knowledge.This is shown as steps (d)-(e)-(c) and has been evaluated with Pellet and HermiT.4. To determine the types of the arriving events, it is not necessary to analyzethe whole ontology. Starting from a materialized ontology, we compute the subsetof data that has influence on the calculation of the types of the newly arrivedindividuals, by utilizing the subset reasoning explained in Section 4.2. This step isdepicted as steps (f)-(d)-(e)-(c) and has been evaluated with Pellet and HermiT.It is important to note that in approaches 3 and 4, the completeness ofthe materialized ontology might become partly lost when the new events yieldconsequences in the static data. This is because the calculation of the types ofthe event data does not lead to a recalculation of the materialized static data.However, this is never the case in the eHealth scenario discussed in this paper.5 Evaluation Set-up and ResultsThe scalability of the reasoners is evaluated by increasing the number of wards,resulting in a growing ABox. Each ward consists of 10 rooms, two patients askingfor aid and three nurses. Each approach and each number of wards was evaluated35 times. The first three and last two results were dropped to eliminated theinfluence of the warm-up and cooling down period. The evaluation was done ona Debian server with an Intel Xeon CPU E5620 2.40GHz with 12 GB of memory.Figure 2 summarizes the performance of the reasoners. The correspondingapproach is indicated between brackets. On the Y-axis the time to complete thewhole scenario, which contains multiple reasoning steps, is indicated. Evaluatingover the sum of the various scenario steps, allows us to gain a clear understandingof the different trends for the various approaches. Since the results for Pellet donot meet the time constraint of five seconds for 1 ward, more than 10 wards werenot evaluated. As for HermiT, materializing the whole ontology every time anevent arrives is not feasible. Since the execution time for ten wards did not comeclose to the time constraints, the evaluation was not continued. Approach 3 is6020040060080010001 10 25 50time (s)Number of wards(1)(3)(4)(a) Pellet Reasoner(2)(4)(3)020040060080010001 10 25 50time (s)Number of wards(b) HermiT Reasoner02468101 10 25 50 75 100time (s)Number of wards(4)(c) HermiT + SubsetFigure 2. Evaluation of the various approachesmore performant than the Pellet approaches but still scales not well because ittakes the whole dataset into consideration when reasoning.The subsetting approach is the fastest and meets the time constraints. There-for it is shown in detail in Figure 2 (c). It is clear that the size of the dataset haslimited influence on the reasoning times due to the extraction of the minimaldataset through subsetting. Partly losing the completeness of the ontology hashuge performance benefits. We therefore analyze these results more in depth inFigure 3 which visualizes the average times for each service in each scenario stepfor a fixed number of wards (here 75). The presented times are the total servicetime. For the HelpSelectionService 55% of the time is spent on reasoning, 42%on quering and less than 3% on conversion. The execution time for the variousscenario steps differ. This is due to the fact that not all steps require the sameamount of reasoning or queries. We can see that the HelpSelectionService takesthe longest in the first two steps, as this service checks the biggest decision tree.6 Conclusion and Future WorkIn this paper the performance and scalability of the HermiT and Pellet reasonerin an event-based eHealth scenario were evaluated. Scalable reasoning over rel-atively static data that is incrementally updated with event data is reached bylimiting the data the reasoners need to calculate the types of the event data. It isclear that partly losing the completeness of the ontology has huge performancebenefits. The technique can be exploited in cases where performance is criticaland where the dynamic part of the ontology which describes the events has noor limited consequences on the rest of the ontology. Furthermore, it was shownthat HermiT is more performant in calculating the inferred types than the Pelletreasoner. In future work, we will focus on adapting the subset-algorithm to re-claim completeness of the whole ontology by incrementally increasing the subsetif possible changes to the static data have been detected.020040060080010001200a b c d e f g htime (ms)Scenario stepsHelpSelectionServicePresenceServiceLightServiceStatusCallServiceSCBMASSIF Overheada: CallLaunchedb: CallRedirectedg: Presence Offh: Corridorc: CallTempAcceptd: Corridore: Patient locf: Presence OnFigure 3. Evaluation of the HermiT reasoner for the subset approach7References1. 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Evaluation and optimized usage of OWL 2 reasoners in an event-based eHealth context

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Evaluation and optimized usage of OWL 2 reasoners in an event-based eHealth context

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