A Semantics-Based Dialogue for Interoperability of ...

A Semantics-Based Dialogue for Interoperability of User-Adaptive Systems in a Ubiquitous Environment Federica Cena1 and Lora M. Aroyo2 1

Department of Computer Science – University of Turin, Italy {Federica.Cena}@di.unito.it 2 Department of Computer Science – Free University of Amsterdam, The Netherlands {l.m.aroyo}@cs.vu.nl

Abstract. In this paper we present an approach to enable interoperability of user-adaptive systems (UASs) in a ubiquitous environment. We model the interactions between systems as a semantics-based dialogue for exchanging user model and context data. We focus on the user data clarification and negotiation tasks, and show how semantics enables, on the one hand, the understanding among user-adaptive systems in a distributed ubiquitous setting, and on the other hand indirectly improves their effectiveness in producing end user results. We deploy and evaluate our approach in the UbiquiTO mobile adaptive tourist guide.

1 Introduction Adaptation is particularly important for applications in ubiquitous environments, since the variety of users, contexts, and devices implies a huge diversity of user needs to be met [12]. As the users interact with various adaptive systems there are different chunks of user data residing in each of them. There is no common “memory” to keep track of user activities and maintain up-to-date set of user preferences and characteristics. Such a ‘memory-bank’ would allow for an adequate adaptation to the user and her current context. One way of achieving a rather complete picture of the user’s experience is to allow the systems in the ubiquitous setting to share user data and thus provide sufficient information for better adaptation [12]. Systems can share user data by accessing a common storage [9]; alternatively, we can allow direct communication between the applications. In open ubiquitous environment, treating interaction between UASs as a method invocation (as in object-oriented programming) is not appropriate [1]. “These methods assume providing a fixed functionality defined at design time independently of the conditions of their use” [12]. Instead, one of the most important requirements in ubiquitous settings is to gather information at run time about the context in which the interaction occurs (e.g. among users, devices, places, systems involved). At the same time, we need some mechanism for managing critical situations (e.g. clarify or negotiate the data) and being reactive to the context. C. Conati, K. McCoy, and G. Paliouras (Eds.): UM 2007, LNAI 4511, pp. 319–323, 2007. © Springer-Verlag Berlin Heidelberg 2007

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To address such environment requirements, we propose to model the interaction between UASs as a semantic dialogue1 and in this way to achieve: user-awareness, semantic-awareness, context-awareness, and reactive behaviour beneficial for the negotiation and clarification of user data in critical situations. Section 2 sketches a typical interaction scenario to outline the requirements for its realization. In Section 3 we present the semantics-based conversational framework, which deployment is further illustrated in the example presented in Section 4. Finally, Section 5 discusses evaluation aspects within the context of the UbiquiTO test bed.

2 Interoperability Scenario Mario is an art student at the University of Torino. He has an assignment to write about religious buildings in Torino. He uses his mobile adaptive tourist guide UbiquiTO [6] to gather quickly information about the religious buildings in Torino. To provide this information tailored to Mario’s features and context, UbiquiTO needs prior information, i.e. buildings Mario has already seen; what does he so far know about religious art; what is his personal interest in this and related topics. UbiquiTO has already a user profile of Mario but it does not contain all the information necessary to answer those questions. Thus, UbiquiTO searches for other systems (e.g. ARRS [5]) used by Mario and sends them requests for the needed information. UbiquiTO and ARRS first agree on Mario identity. Then, UbiquiTO requests from ARRS the values for Mario’s interest in the concept “churches”. To provide the correct values, ARRS needs to clarify the request (e.g. churches as sightseeing or as religious objects). UbiquiTO receives the values and the level of certainty for each of them. If this level is not sufficient, it continues the dialogue exploring the domain and Mario’s profile for related relevant concepts (e.g. different church styles and religious historical buildings). If, however, it detects that Mario is in a hurry, it uses the values from ARRS despite their low confidence level in order to optimize the response time.

3 Semantics-Based Conversational Framework In this section, we illustrate our approach for user-adaptive systems interaction in ubiquitous environments. First, we show how we model systems interactions as a dialogue; second, we demonstrate the enriching of this the model with semantics. Dialogue Games. We base the systems interaction in our scenario on the notion of a dialogue game as introduced by Levin, Moore [10] and Dimitrova [8], “a Dialogue Game (DG) represents an interaction episode concerning a particular goal and discussion topic”[8], and is formally defined as a 5-tuple [2], where C= is the focus, the set of all possible concepts that can be exchanged during the dialogue; P = the set of pre-conditions necessary to trigger the game; R = the actions to perform at the end of the game (post-conditions); U= tactics is a set of rules to produce speech acts, thus for selecting the moves and the scope of each move, and S= 1

A dialogue is a set of Speech Acts [11] performed by actors with the intention to accomplish certain purposes. The basic idea of dialogue-based approaches is to represent the interaction process with the conversational conventions used by humans in natural conversation [3].

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scopes is the set of the concepts from the focus to be used in speech acts. The systems interact with a set of speech acts (SA), defined by the tactics of the specific game. A statement about the user model is represented as a triple . To meet the requirements in section 2, we identify three main games (details in [4]): i) a concept-exploratory game supports semantic-awareness by collecting information about the concepts and relations in the knowledge base. It can be used to negotiate the response, when an exact match is not available; or to clarify the request. ii) a value-informative game supports the context awareness and user awareness requirements. It informs the actors of the values or beliefs of the other system’s knowledge base. iii) an explicative game supports also the semantics awareness requirement. It is used when there are a discrepancy in the actors’ believes, that needs to be justified. Semantic-based Dialogue. To address the limitations that emerge in the application of this approach in an ubiquitous environment [4], we express the data model in terms of a common language and shared vocabularies. This provides a common understanding of the exchanged data among the systems and allows them to reason on the semantics of data (on the concepts relations and on the properties value) to decide the next steps of a dialogue. Reasoning with concepts relations refers to probing for typed properties of a given concept and thus creating the focus of the game and the scope of the next SA. Typed properties are considered here as upper-level concepts (parents), low-level concepts (children), and directly related concepts at the same level (siblings). In our scenario, UbiquiTO requests the concepts museum and historical_building because they are siblings of the topic (church) and they share the property has_style. The implication here is that if there is no exact match of the requested concepts, the value of some of the children concepts can be used instead. Reasoning with properties values refers to considering the property values to select the appropriate scope concepts (S) and to order them according to the game tactics (U). For example, the property informative factor expresses the level of usefulness with respect to a specific goal. This allows us to rank the concepts according to their informative factor. In the scenario, UbiquiTO asks ARRS for a username and a birth place, since they are the concepts with the highest informative factors for the goal of user identification. To conclude, the reasoning with concept relations and with property-values allows to improve the efficiency of the dialogue, by providing the means to rationally explore a knowledge base and using more efficient tactics for the SAs sequencing.

4 Example: Interoperability Scenario with Semantics Dialogue Mario selects visit Æ churches in UbiquiTO. Since it does not have all the required information to satisfy Mario’s request, UbiquiTO initiates a dialog with ARRS2 to find the missing values. UbiquiTO accesses the public dialogue API and implements strategies rules to decide which dialogue game to play. 2

The searching of external system does not influence the dialogue management. Thus, we focus only on what happens when the system is found and the interaction starts.

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USER IDENTIFICATION - opening a value-informative game UbiquiTO: Do you have username=”Mario”? ARRS: I have username=”Mario”. UbiquiTO: Do you have birthplace=”Turin” where username=”Mario”? ARRS: I have birthplace =”Turin” and username=”Mario”

() () () ()

UbiquiTO asks ARRS about the username of Mario. ARRS confirms and UbiquiTO asks for the next relevant concept for user identification (birthplace). The dialogue ends when, according to its identification algorithm, UbiquiTO can reliably assume that they are speaking of the same user. Scope concepts in the dialogue are ordered by a properties-value reasoning considering the informative factor of each value property. Once the user is identified, the () is sent to ARRS. Not having an exact match, ARRS initiates a concepts-explorative game to clarify the request. QUERY REFINEMENT- starting a concept-explorative game ARRS: Do you want concept=”church” in context = ”religious celebration”? () UbiquiTO: No, I do not. () ARRS: Do you want concept=”church” in context=” place to visit”? () UbiquiTO want information about concept=”building”. ()

ARRS inquires for super-concepts of “church” (e.g. church as religious celebration, or place to visit). The choice of concepts here is done considering the relations among the concepts with concepts-relation reasoning. ARRS answers does not satisfy UbiquiTO’s request. According to the strategies rules, UbiquiTO starts an exploration of related concepts for achieving similar/equivalent results. RESPONSE NEGOTIATION- starting a concept-explorative game UbiquiTO: Do you know user interest value in romanic_church? ARRS: yes, value is 0.6 UbiquiTO: Do you have user interest in gothic_church? ARRS: No, I don’t UbiquiTO: Do you know user interest in historical_building? ARRS: yes, it is value=0.4

() () () () () ()

UbiquiTO inquires the interest values for the children of church, (e.g. Romanic, Gothic), and then for its sibling historical_buildings. Even without an exact interest value, UbiquiTO can infer it as an average of the values of related concepts. At each dialog step, UbiquiTO determines the commitment on the context. If context change occurs UbiquiTO changes the focus, e.g. from children to siblings. The dialogue ends when UbiquiTO detects a critical context condition.

5 Evaluation and Discussion Here we discuss the results of the approach evaluation with the UbiquiTO system (for details see [4]). In sec. 4 we saw that, in the interaction with ARRS, UbiquiTO is able to: i) have more reliable user data; and ii) provide the user with better results. We focus only on the first aspect as producing results depends on many additional parameters, besides the input data (e.g. system internal strategies). The test subjects consisted of 15 ARRS’s users selected by an availability of sampling strategy. The

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experimental tasks were organized in three steps. STEP 1: users interact with UbiquiTO as a stand-alone system. STEP 2: users perform the same tasks as in STEP 1 while UbiquiTO interacts with ARRS. Finally, we compared the results from STEP 1 and STEP 2 with respect to the user model dimensions in order to see how the user model changes after the interaction dialogue for additional user data. To estimate in a numerical fashion the changes in the user model, we measured the Confidence Level (i.e. a measure of the “subjective validity” of the value, expressed as a system belief in how much the value is reliable). The Confidence Level is calculated in the situation i) and ii): the value moved from 0.37 to 0.55 with an average increment of the 17.9 %. We can now conclude from the tests that a dialogue is useful to improve user model data (and as a consequence adaptation results) only if the exchanged data are good. Thus, it needs to be supported by some mechanism for data evaluation [7]. Advantages of the semantic dialogue: it is suitable to model the interaction among UASs in ubiquitous environments since it addresses context requirements; it improves adaptation results, since it allows efficient interoperability interactions for reaching more reliable user data. Disadvantages of the semantic dialogue: it can occur only among known systems; it supports only bilateral interactions; it is time consuming and requires lots of computational efforts (to consider all contextual conditions).

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