An Ontological Inference Driven Interactive Voice Response System Mohammad Ababneh Duminda Wijesekera Department of Computer Science Department of Computer Science George Mason University George Mason University Fairfax, VA, USA Fairfax, VA, USA mababneh@gmu.edu dwijesek@gmu.edu Abstract— Someone seeking entry to an access controlled accusations of bias from rejected entrants. Ideally, a facility or through a border control point may face an in successful interview should accommodate differences in person interview. Questions that may be asked in such an accents and provide assurance that it is unbiased against interview may depend on the context and vary in detail. One similar attributes. of the issues that interviewers face is to ask relevant questions that would enable them to either accept or reject entrance. Repeating questions asked at entry point Given the recent success of interactive voice response interviews may render them useless because most (IVR) systems such as auto attendants, satellite interviewees may come prepared to answer common navigation,   and   personal   assistants   such   as   Apple’s   Siri,   questions. As a solution, we present an interactive voice Google’s  Voice,  Microsoft’s   Speech, we investigated the response system that can generate a random set of questions possibility of specializing IVR systems for access control that are contextually relevant, of the appropriate level of such as: Visa interviews, entry point interviews, biometric difficulty and not repeated in successive question answer enrollment interviews, password reset, etc. sessions. Furthermore our system will have the ability to limit the number of questions based on the available time, Although IVR systems have come a long way in degree of difficulty of generated questions or the desired subject concentration. Our solution uses Item Response recognizing human voice, and responding to human Theory to select questions from a large item bank generated requests as if responses come from another human, most by inferences over multiple distributed ontologies. of the existing IVR systems are pre-programmed with questions and their acceptable answers, and consequently have limited capability in satisfying the Use Case at hand. Keywords—Ontology; Semantic Web; OWL; Dialogue; Question Answering; Voice Recognition; IVR; VXML; Access The first minor limitation of current IVR systems Control Policy; Security; Item Response Theory. comes from the fact that, the human starts and drives the conversation. The second limitation is that most IVR I. INTRODUCTION systems have a finite number of pre-programmed conversations. Therefore the set of questions generated by such a system are the same for every conversation. This Physical control points such as human guarded gates, limitation may expose the set of questions so that aspiring border control points and visa counters provide entry into entrants may come with prepared question-answer pairs, facilities or geographical regions to those that can be even if the subject matter of the questions may be admitted legitimately. Legitimacy is usually determined unfamiliar to them. Consequently, having the ability to by rules, regulations or policies known to entry control select questions from a large pool may resolve this personnel whose duty is to ensure that these policies are limitation. The third limitation is that when selecting a enforced while admitting people. In order to do so, they random set of questions from a large pool, the set of hold an interview, in which an aspiring entrant is asked a questions asked may not have the desired overall level of series of questions, and possibly show some documents difficulty to challenge the user. Solving this issue is and demonstrate some knowledge about the contents of relevant because all aspiring entrants expect to have a fair the documents or attributes contained in them. Successful interview. The forth limitation is that questions must be interviews should have questions that are relevant, of a able to discriminate between someone that knows the reasonable level of difficulty (i.e. not too difficult or subject matter from someone who guesses an answer. common knowledge) and not to have been asked in prior interviews for the same purpose without drawing STIDS 2013 Proceedings Page 125 As a solution we created an ontological inference CAT/IRT reduces the number of questions necessary to based IVR system that uses item response theory (IRT) to reach   a   credible   estimation   of   the   examinee’s ability by select the questions [13, 3]. Our system uses the XACML 50%. CAT/IRT can be used to control the number and language as a base to establish entry policies that consist order   of   questions   to   be   generated   based   on   examinee’s   of rules to specify the attributes that must be possessed by previous answers [4, 5]. permitted entrants [7]. The IVR system has the responsibility of determining access by asking questions Our goal in this work is to demonstrate and build an generated using ontological inferences and IRT. access control system using dialogues of questions and answers generated from a suitable collection of In previous work, we introduced a policy-based IVR ontologies. Table I shows a sample dialogue that is system for use in access control to resources [1]. Later, generated from our research. Our prototype automated we presented an enhancement that uses IRT to select IVR system can help immigration enforcement at a border queries from a large set of attributes present in a policy control point making a decision to permit or deny a [2]. Here we introduce ontology-aided access control person asking for entry. Through a dialogue of questions system by including questions related to the base and answers, the interviewee will be assigned a numerical attributes in order to ascertain the interviewee’s score that will then serve as a threshold in the decision familiarity, and provide a score for the entire set of making process. This score is calculated using IRT, which answers [8]. We also have the added capability to takes into account the correctness of the user’s  responses   generate the succeeding question based on the accuracy of and the weight of the individual questions. the preceding question. We do so by aligning each The rest of the paper is written as follows. Section II attribute with an ontology that encodes the subject matter describes an ontological use case, Section III describes expertise on that attribute and derive facts from these the response theory. Section IV describes the system ontologies using reasoners to generate questions. We then architecture. Section V describes our implementation. assign weights to these derivations based on the axioms Section VI is about experimental results and section VII and rules of derivations used in the proof tree. concludes the paper. Usually ontologies have a large number of axioms II. Motivating Use Case and assert even more facts when using reasoners. Consequently, blindly converting such an axiom base to In this section, we describe an example ontology used human-machine dialogue would result in very long in our work to generate efficient dialogues of questions conversations with many disadvantages. The first is that TABLE I. A SAMPLE DIALOGUE human users would become frustrated of being subjected to long machine driven interrogations, and thereby reducing the usability of the system. The second is that long conversations take longer time to arrive at an accept/reject decision, and likely to create long queues at points of service, such as Airports and guarded doors. In addition, having a line of people behind one person in close proximity may leak private information of the interviewee. Also, others may quickly learn the set of questions and answers that would get them mistakenly authorized, thereby gaining unauthorized access. We use IRT, which provides the basis for selecting tests from large number of potential questions. Psychmotricans in social sciences and standardized test preparation organizations such as the Educational Testing Services that administer standardized test examinations like SAT, MCAT, GMAT etc. have developed methodologies   to   measure   an   examinee’s   trust   or   credibility from answers provided to a series of questions. In traditional tests, the ability of the examinee is calculated by adding up the scores of correct answers. Currently, Computerized Adaptive Testing (CAT) that relies on IRT has been used to better estimate an examinee’s  ability.  It  has  also  been  shown  that  the  use  of   STIDS 2013 Proceedings Page 126 and answers that are used in assigning a numerical value We use this ontology in our work because it serves as a to  an  interviewee’s  ability  or  trust  level. good example showing the strength of our system. First, it shows the possibility of generating valuable questions Fig. 1 illustrates a class diagram of our under- from asserted or inferred facts. Second, it enables the development ontology for homeland security. The implementation of the theory under consideration (to be purpose of this ontology is to collect, organize and infer discussed later in the background section) to generate information that can help deterring possible attacks, efficient and secure dialogs that are used in: (1) making enforcing strict entry and enabling faster reach to entry control decisions, (2) assigning numerical values to suspects. The ontology defines classes, individuals, ability or trust in the shortest time possible and (3) load properties and relationships using OWL 2 Web Ontology distribution among interviewers and diverting people for Language (OWL) [9]. The major entities in the ontology further investigation. are: x Person: defines humans in general and has The use of ontology in such an application provides subclasses like; International Student and Friend. many benefits. The most important amongst them is x Event: defines an event that has a location, date, time reasoning. Using a reasoner we are able to derive facts and type like terrorist attack from asserted ones. These facts are used to generate x International Student: is a person who is on an F-1 questions to measure the knowledge or ability level of an or J-1 Visa type interviewee on a subject under questioning. In IRT, better x University: defines a university. Some of its current item selection and ability estimation happens when a large members are MIT and GMU set of items is available to draw questions from. Using x City: defines a city like Boston ontology, the large number of derivable facts provides us x Country: defines a country like USA, Russia, with the ability to increase the number of questions, and Dagestan, Kazakhstan, etc. also control the quality and difficulty of questions. x State: defines a state like Massachusetts x Visa: defines visa types like F-1 and J-1 student visas Although there are many reasoners such as FaCT++, and maybe others. JFact, Pellet, RacerPro, we use HermiT [12] in our work. Given an OWL file, HermiT can determine whether or not This ontology represents many kinds of data classes the ontology or an axiom is consistent, identify and relationships between these major classes and subsumption relationships between classes and deduce individuals. For example, we   define   the   “Boston   other facts. Most reasoners are also able to provide Marathon   Bombing”   as   a   “Terrorist   Attack”   that   explanations of how an inference was reached using the happened  in  “Boston”,  which  is  a  city  in  “Massachusetts”   predefined axioms or asserted facts. state.   Another   fact   is   that   “Dzhokhar   Tsarnaev”   is   an   “Event   Character”   in   the   “Boston   Marathon   Bombing”   One such fact derived from asserted ones in our “Terrorist   Attack”.   Also   we   have   an   “International   ontology, is finding the friends that hold a student visa of Student”   who   is   a   friend   to   “Event   Character”   in   the   a person involved in a terrorist attack. To explain this, we “Boston  Marathon  Bombing”. have   “dzhokhar is   friend   of   Dias”,   “Dias   is   friend   of   Azamat”,   “Dias   has   F-1   visa”,   “Azamat   has a J-1   visa”,   “dzhokhar   is   an   “Event Character” in the “Boston Marathon   Bombing”,   “Boston   Marathon   Bombing”   is   a   “Terrorist Attack”.   Thus   we   infer   (using   the HermiT reasoner) that Azamat and Dias are the friends of the Boston Bomber and therefore need to be questioned at any entry point. We use this chain of derivations to generate specific questions from them. Reasoners and the explanations that they provide are very important components in our work to generate relevant and critical questions from ontology that measure knowledge and estimate ability from a response in order to grant access or assign trust. In the example above, the reasoner provided an explanation of the inference using 11 axioms. We use such a number in defining the Fig. 1. The Homeland Security Ontology in Protégé difficulty of questions generated from such inferences, as STIDS 2013 Proceedings Page 127 characterization of what happens when an individual meets an item, such as an exam or an interview. In IRT, each person is characterized by a proficiency parameter that represents his ability, mostly denoted by (T) in literature. Each item is characterized by a collection of parameters mainly, its difficulty (b), discrimination (a) and guessing factor (c). When an examinee answers a question,   IRT   uses   the   examinee’s   proficiency   level   and   the   item’s   parameters   to   predict   the   probability   of   the   person answering the item correctly. The probability of answering a question correctly according to IRT in a three-parameter model is shown in (1), where e is the constant 2.718, b is the difficulty parameter, a is the discrimination parameter, c is the guessing value and Tis the ability level [3]. 𝑃 = 𝑐   + (1 − 𝑐)   ( ) (1) Fig. 2. A sample explanation of an inferred axiom in Protégé using the HermiT reasoner In IRT, test items are selected to yield the highest information content about the examinee by presenting will be explained in section V. Fig. 2 shows the HermiT items with difficulty parameter values that are closer to reasoner explanation of our inferred fact. his ability value. This reduces time by asking fewer and relevant questions rather wider range ones while III. BACKGROUND satisfying content considerations such as items or rules that are critical for a decision of access or scoring. A. IVR Systems 1) IRT parameter estimation The main purpose of an IVR system is to interact with humans using a voice stream. An IVR environment In order to determine the difficulty and discrimination consists of a markup language to specify voice dialogues, parameters of a test item, IRT uses Bayesian estimates, a voice recognition engine, a voice browser and auxiliary maximum likelihood estimates or similar methods (MLE) services that allow a computer to interact with humans [3, 4]. In the original IRT, an experiment is conducted to using voice and Dual Tone Multi-Frequency (DTMF) estimate these values for each item and at an assumed tones with a keypad enabling hands-free interactions level of ability for various groups with associated values between a user and a host machine [13]. Recently, many of IRT parameters using his judgment and experience. applications such as auto attendant, satellite navigation, Nevertheless, by using our system we can also revise any and   personal   assistants   such   as   Apple’s   Siri,   Google’s   initial values for these parameters. We model rule Voice,   Microsoft’s   Voice,   etc.,   have   started   using   IVR   attributes as test items and rely on the policy systems. The IVR language we use is VoiceXML, administrator to provide the estimated probabilities. sometimes abbreviated as VXML [14]. Briefly, Voice XML is a Voice Markup Language (comparable to 2) IRT ability estimation HTML in the visual markup languages) developed and standardized   by   the   W3C’s   Voice   Browser   Working   In IRT, responses to questions are dichotomously Group to create audio dialogues that feature synthesized scored. That is, a correct answer gets a score of “1”   and speech, digitized audio, recognition of spoken and an   incorrect   answer   gets   a   score   of   “0”.   The   list   of   such   (DTMF) key inputs, recording of spoken input , results consist an item response vector. To estimate the telephony, and mixed initiative conversations. examinee’s   ability,   IRT utilizes maximum likelihood estimates (MLE) using an iterative process involving a B. Item Response Theory priori value of the ability, the item parameters and the response vector as shown in (2). Here, 𝜃 is the estimated IRT, sometimes called latent trait theory is popular ability within iteration s. 𝑎 is the discrimination among psychometricians for testing individuals, and a parameter of item i, where i=1,2,...,N. 𝑢 is the response score assigned to an individual in IRT is said to measure of the examine (1/0 for correct/incorrect). 𝑃 𝜃 is the his latent trait or ability. Mathematically, IRT provides a STIDS 2013 Proceedings Page 128 probability of correct response from (1). 𝑄 𝜃 is the probability of incorrect response = 1- 𝑃 𝜃 [3,4].  [ ] 𝜃 =   𝜃 +   (2)       Then, the ability estimate is adjusted to improve the computed   probabilities   with   the   examinee’s   responses   to   items. This process is repeated until the MLE adjustment becomes small enough so that the change becomes negligible. IRT accommodates multiple stopping criteria such as: fixed number of questions, ability threshold or a standard error confidence level. The result is then considered   an   estimate   of   the   examinee’s   ability   and   the   estimation procedure stops. The ability or trait usually ranges from -∞ to +∞, but for computational reasons acceptable values are limited to the range [-3, +3]. Fig. 3. Ontology-based IVR using IRT C. Access Control and XACML into VoiceXML and plays to the user. Then the system waits  for  the  user’s  utterance, and if the user provides one, the system’s voice recognition software attempts to Access control policies specify which subjects may recognize the input and checks the correctness of the access which resources under some specified conditions answer. Based on the answer, the IRT estimation [6]. An attribute-based access control policy specifies procedure either increases a priori ability score or subjects, objects and resources using some attributes. decreases it. The process continues until a predetermined XACML is an OASIS standard XML-based language for level of ability or accuracy specified according to the specifying access control policies [7]. In a typical application is reached. XACML usage scenario, a subject that seeks access to a resource submits a query through an entity called a Policy Because ontologies produce a large number of facts, it Enforcement Point (PEP), which is responsible for would be impractical to run a dialogue that lasts hours in controlling access to the resource. It forms a request in the order  to  estimate   user’s  ability.  In  our  homeland  security   XACML request language format and sends it to the a ontology uses 167 axioms. The reasoner was able to infer policy decision point (PDP), which in turn, evaluates the 94 facts raising the total number of axioms and candidate request and sends back one of the following responses: to generate questions to 273. accept, reject, error, or unable to evaluate. We use IRT to manage and control dialogue questions IV. USING IRT TO MANAGE AND CONTROL generated from a large pool of ontologically derived facts DIALOGUES FROM ONTOLOGIES in a way that shortens the length of dialogues while keeping   the   maximum   accuracy   in   estimating   the   user’s   Fig. 3 shows the overall architecture of our system. trust. The IRT-based estimated  (θ)  represents  the  trust  or   We use derived or axiomatic facts of the ontology to confidence of the system in the person answering the create questions asked by our IVR system. Given that a questions in order to make an access decision. large number of facts can be derived from our ontology, but only few questions can be asked during an interview, We have used the OWL annotation property to assign we use IRT to select the facts that are used to generate IRT parameters to axioms. Annotations were selected in questions. order to keep the semantics of the original ontology and structure intact. We annotate every asserted axiom in the Our questions are automatically created without ontology with IRT parameters, which are: difficulty (b), human involvement by combing English words or phrases discrimination (a) and guessing (c). Currently, we assume such as “Does”   or   “Is-a”   with   ones chosen from the all asserted axioms have the same default degree of ontology of (subject, property, object) triples. The difficulty and discrimination values of 1. The code expectation is a dichotomous answer of either (yes, no) or snippet in Fig. 4 illustrates our annotation using Java with (true, false). The ontological property names such as “is- OWL API. An improvement to this approach would be to a”,   “has-something”   are prime candidates for creating assign different values for difficulty and discrimination by true/false questions. Our system transforms the question using domain experts. STIDS 2013 Proceedings Page 129 OWLAnnotationProperty irtDifficultyAP = Set df.getOWL inferredAxioms=inferredOntology.getAxioms(); AnnotationProperty(IRI.create("#irt_difficulty" DefaultExplanationGenerator explanationGenerator )); =new DefaultExplanationGenerator( OWLAnnotation irtAnnotation = manager, factory, ontology, reasoner, new df.getOWLAnnotation( SilentExplanationProgressMonitor()); irtDifficultyAP , df.getOWLLiteral(1.0)); for (OWLAxiom axiom : inferredAxioms) { for (OWLAxiom axiom : axioms) { Set explanation = OWLAxiom axiom2 = axiom.getAnnotatedAxiom explanationGenerator.getExplanation(axiom); (Collections.singleton(irtAnnotation)); //Annotate inferred axioms using the number of manager.addAxiom(ontology, axiom2); explanation } OWLAxiom tempAxiom = axiom.getAnnotatedAxiom(Collections.singleton(irt Fig. 4. Java code for asserted axiom annotation Annotation)); manager.addAxiom(inferredOntology, tempAxiom); We weigh inferred facts more during the estimation Fig. 5. Java code for inferred axiom annotation process. We are calculating these parameter values from the number of explanation axioms used in each individually inferred fact. Our current scheme of The resultant decision is based on the IRT difficulty value assignment is shown in Table II; where characteristics of the axiom and not on the number or the higher values or weights are assigned according to the percentage of correctly answered questions as in number of explanation axioms used to infer a fact, and traditional testing. The ability estimate produced by our consequently the question generated from it is considered implementation also comes with a standard error (SE) to be more difficult than one generated from an asserted value that is a measure of the accuracy of the estimate. fact. Fig. 5 illustrates a code snippet for inferred axiom Equation (3) presents the formula used for standard error annotation. calculation [7]. In our current work and for testing purposes we use a 𝑆𝐸 𝜃 =          ∑   ( ) default   value   of   “1.0”   for   discrimination   and   “0.0”   for   guessing, which practically neutralizes them leaving the difficulty parameter as the sole factor in estimating ability Higher standard error indicates that the estimate is not using equation 2. However, our solution and algorithm are very accurate, while lower values indicate higher based on the IRT two-parameter model, which relies on confidence in the estimation. This too can be used as a the  item’s  difficulty  and  discrimination  parameters. Fig. 6 means to discontinue the dialogue or use an alternate shows our algorithm to estimate ability based on equation decision method. 2 [3]. Our system estimates the ability of a user after every answer to a question generated from an axiom V. IMPLEMENTING THE ONTOLOGY-BASED IVR before selecting and asking the next question. If the SYSTEM FOR ENTRY CONTROL ability estimate exceeds the threshold then access is granted. If the threshold is not reached then additional Here, we present a prototype of our system showing questions   are   offered.   If   the   estimated   ability   doesn’t   the major components. It is not yet validated as a reach the threshold the dialog stops and access is denied. deployable system, but it works for the sample use case. Depending on the application, the dialog might be run Algorithm 1: IRT Ability estimation again giving a second chance. When the ability estimation Input:a priori theta, Difficulty, Discrimination, again reaches a predefined threshold, the system Answer concludes the dialog and conveys the decision. Output: posteriori theta, standard error /* calculate theta and standard error*/ TABLE II. IRT DIFFICULTY ASSIGNMENT BASED ON 1:for (counter < items.length) do NUMBER OF AXIOMS IN EXPLANATION 2: itemDifficulty=parseFloat(difficultyArray[i]); Number of IRT 3:itemDiscrimination=parseFloat(discriminationArr ay[i]); explanations Difficulty 4:answer=parseFloat(answerArray[i]); 1 0 Easy 5:probTheta=calculateProbability(itemDiscriminati 2-3 1 on,aTheta,itemDifficulty); // equation 1 4-5 1.5 Moderate 6:thetaSplus1= claculateTheta(probTheta, thetaS); //equation 2 6-7 2 7:endfor; 8-9 2.5 8:estimatedTheta = thetaSplus1; >=10 3 Hard 9:return thetaSplus1; Fig. 6. Algorithm for ability estimation in IRT STIDS 2013 Proceedings Page 130 1) Voice Platform (Voxeo)
We   use   the   Voxeo’s   Prophecy   local   server   as   our   Welcome to the United States. To accelerate your entry, we will appreciate your responses to voice platform for voice recognition and to run the some questions to verify your identity and dialogues. Java, Java Server Pages (JSP), and Java Script eligibility (JS) are used to implement the architecture modules and ability/trust scores.
Voxeo’s   Prophecy   is   a   comprehensive   IVR   and   Fig. 7. A sample Homeland security VoiceXML greeting form standards-based platform [15]. Some of the capabilities integrated into the platform are: automatic speech The conversation starts with a menu in VoiceXML recognition, speech synthesis (Text-to-Speech), Software hosted on the local Voxeo Prophecy web server. The Implemented Phone (SIP) browser and libraries to create voice browser connects to the web server and converts and deploy IVR or VoIP applications using VXML text to speech and speech to text. Fig. 7 shows a sample CCXML. It supports most of server side languages and VoiceXML code. has a built-in web server. Fig. 8 shows our algorithm integrating ontology, IVR 2) Item bank and IRT. This algorithm was successfully implemented using JavaScript and Java Server Pages (JSP) embedded In our work, we start with ontology, annotate every in VoiceXML pages. The main steps are as follows: axiom with an “irt_difficulty” property  of  value  “1”.  Then x Load the ontology and parse the XML into Document we use this ontology in the HermiT reasoner to infer Object Model (DOM). implicit axioms and their explanations. The inferred facts x Extract  the  axiom’s  triplet  (subject,  property,  object) are themselves annotated with “irt_difficulty” property x Extract   the   axiom’s   IRT   difficulty   value   from   the   and values calculated by factoring the number of annotation explanation axioms using the schema stated in Table II. x Establish  a  VoiceXML  “For”  loop  that  synthesizes  a   question from string or text values to speech (TTS). For example, when annotating the inferred fact “the The question consists of an auxiliary verb, object, friends of the Boston Attack Bomber”, which has an property and subject to test the correctness of an explanation that includes 11 axioms shown in Fig. 2, the axiom. irt_difficulty annotation   would   be   “3.0”;;   which   is   the   x The system waits for a response. If there is one it highest value on the scale of IRT difficulty parameter converts it to text and recognizes it. If it adheres to values in Table II. We assume that answering a question grammar then a value is assigned as an answer. generated from a high-valued fact is a difficult task. x If there was no answer then VXML re-prompts the Consequently, if the answer to a question derived from question up to a programmed number of times. If this fact is correct, the ability estimate would be impacted exceeded then an appropriate VXML is executed. more positively than a correct, but easy one and more x The vector of binary answers is used to estimate the negatively if the opposite happens. An example is the IRT ability. asserted  axiom  that  “Boston  is  located  in  Massachusetts”.   Because this is an asserted fact, it is annotated with value x The loop continues until a threshold of T or the “1.0”;;  which  makes  a  question  generated  from  it  an  easy   maximum number of questions is reached. one and thus not affecting the ability estimate greatly. x The IRT ability estimation algorithm, as illustrated in This process is basically generating the item bank in Fig. 6, takes the variables: answer vector, a priori T, CAT/IRT terminology. Each item in the item bank difficulty, discrimination and calculates a posteriori contains a question, an answer and IRT parameters. In 𝜃. addition to saving it as ontology in any of the supported x If the   answer   is   correct   (“yes”   or   “true”),   a value of formats, this item bank can also be supported by using a “1”  is  assigned.  If  not,  a  “0”  is  assigned. more specialized CAT/IRT platform like Cambridge x The last posteriori 𝜃 in the loop is the estimated University’s  Concerto [16]. user’s  ability   T and can be compared to a threshold value set by an administrator. Access is granted if ( T 3) Generating dialogues from an ontology >  𝑡ℎ𝑟𝑒𝑠ℎ𝑜𝑙𝑑) and denied otherwise. STIDS 2013 Proceedings Page 131 voice dialogs from ontologies for entry control. We have Algorithm 2: dialogue access evaluation Input: a priori theta, Difficulty, used IRT to generate shorter dialogues between the Discrimination, Answer system and a human speaker. IRT is useful in Output: access control decision compensating for inaccurate voice recognition of answers /* make access control decision from ontology*/ during dialogs or accidental mistakes. Our entry control 1: domDocument=parse(ontology); // DOM decisions are made based on an estimation of a level of 2: subjectArray=getAxiomSubject(axiom); trust in a subject derived from the importance or 3: propertyArray=getAxiomProperty(axiom); relevance of axioms in ontology. The use of IRT also 4: objectArray=getAxiomObject(axiom); 5: difficultyArray=getAxiomDifficulty(axiom); enables the reordering of questions with the purpose of 6: /*use voiceXML , JSP to generate dialog*/ preserving privacy in IVR systems. With the advancement 7: for (counter < items.length) do in the fields of mobile, cloud and cloud based voice 8: ‘[auxiliary verb]’ recognition such systems become important in defence +propertyArray[i]  +  “ ” + objectArray[i] +“ ”+ subjectArray[i]; and physical security applications [17, 18, 19]. 9: = user_utterance; 10: response[i] = REFERENCES Field.voiceRecognition(user_utterance); 11: if response[i]= ‘Yes’  or  ‘true’ [1] M.   Ababneh,   D.   Wijesekera,   J.   B.   Michael,   “A   Policy-based 12: resultVector[i]=1; Dialogue System for Physical Access Control”,   The   7th STIDS 13: else 2012), Fairfax, VA, October 24-25, 2012. 14: resultVector[i]=0; 15: endfor; [2] M.   Ababneh,   D.   Wijesekera,   “Dynamically Generating Policy Compliant Dialogues for Physical Access Control”,   CENTERIS 16: theta = IRT_algorithm(resultVector, difficulty, discrimination,aPrioriTheta); 2013 - Conference on Enterprise Information Systems – aligning 17: if theta > thetaThreshold technology, organizations and people, Lisbon, Portugal. 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The success of dialog system depends upon http://www.windowsphone.com/en-us/how-to/wp7/basics/use- multiple timing factors and scalability of supporting speech-on-my-phone, accessed September 3,2013. multiple users. Our on-going research addresses these two [18] Apple Siri, URL: http://www.apple.com/ios/siri, accessed aspects. September 3, 2013. [19] Google Android Mobile Search, URL: http://www.google.com/mobile/search/, accessed September 3, VII. CONCLUSION 2013. We have designed and implemented a novel IVR system that can dynamically generate efficient interactive STIDS 2013 Proceedings Page 132