Agent-based User Personalization Using ContextAware Semantic Reasoning Fran Frkovic1, Vedran Podobnik1, Krunoslav Trzec2, and Gordan Jezic1 University of Zagreb, Faculty of Electrical Engineering and Computing, Croatia 2 Ericsson Nikola Tesla, R&D Center, Croatia {fran.frkovic, vedran.podobnik, gordan.jezic}@fer.hr,
[email protected] 1
Abstract. The future of mobile telecommunications is aimed at creating a usercentric wireless world which takes into account user’s preferences, as well as communication context (e.g., network and terminal heterogeneity or user location). In order to enable context-aware personalization of communication services offered by next-generation mobile networks, we propose an agentbased approach in combination with semantic reasoning techniques from the Semantic Web. In particular, we use ontology-based user profiles to create an agent-based context-aware service which supports personalization according to user’s preferences. An ontology is created which contains knowledge regarding terminal capabilities and user preferences, as well as a software agent which manages user personalization according to the context extracted from the user profile. Keywords: Software Agents, User Personalization, Context-aware Semantic Reasoning, Ontology-based User Profiles
1 Introduction Third-generation (3G) mobile networks and beyond are characterized by a large number of content-rich services delivered by an infrastructure based on the Internet Protocol (IP). From a user’s perspective, efficient content selection and presentation on mobile devices are tasks difficult to achieve, mainly due to the insensitivity of service offers to communication context or device capabilities [1]. Consequently, adaptive support for delivering services to mobile users is needed. This could enable more efficient service behavior based on communication context, as well as user preferences. Combining the Semantic Web and agent technologies provides a promising solution for the automation of user personalization tasks which offers both semantic-aware and context-aware information processing related to content-rich mobile services [2]. The level at which modern computers manage information can be described as the data level in the DIKW (Data, Information, Knowledge and Wisdom) information hierarchy [3] shown in Fig. 1. Data is the most basic level which comes in the form of raw observations or measurements, while information adds semantics to data. Knowledge tells us how to use it, and wisdom tells us when to use it.
Frkovic, Fran; Podobnik, Vedran; Trzec, Krunoslav; Jezic, Gordan. Agent-based User Personalization Using Context-aware Semantic Reasoning. Lecture Notes in Computer Science. 5177 (2008); 166-173.
Fig. 1. The DIKW hierarchy. Modern computers process at the data level. Semantic Web technologies bring computers to the information level, while software agents introduce the knowledge layer [4].
The development of the idea of semantic reasoning has resulted in a large number of data models and languages. Among them are RDF1 (Resource Description Framework), RDFS2 (RDF Schema) and OWL3 (Web Ontology Language). This paper is organized as follows. In Section 2, we present the technologies of the Semantic Web which enable semantic reasoning. Section 3 describes related work regarding user personalization issues in mobile networks. In Section 4, we present ontology-based user profiles which contain terminal capabilities and user preferences, as well as case studies which demonstrate agent-based user personalization. Section 5 proposes ideas for future research work and concludes the paper.
2 The Semantic Web Currently, Web accessible resources are mainly described using HTML, and presented to human users via Web browsers. HTML, however, does not enable computers to fully interpret the information. Internet pioneer Tim Berners-Lee speaks of a “dream” of the future in which computers are truly capable of analyzing data on the Web [5] and presenting it in a human-friendly way. The Semantic Web is a vision in which knowledge is organized into conceptual spaces according to meaning, and keyword-based searches are replaced by semantic query answering [6]. Formally, an ontology is a statement of logical theory. An ontology in the context of information science is a data model which represents certain concepts within a domain of interest, as well as relationships between these concepts. Ontologies are used in the areas of AI, the Semantic Web, etc. RDF is a family of W3C specifications generally used for modeling information. The RDF model is based on statements or triples, which include a subject, a verb and an object (SVO). A collection of RDF statements is represented by a labeled, directed pseudo-graph. While RDF allows users to describe resources using their own vocabulary and does not make assumptions on any particular domain, RDFS is used to define the semantics of a domain. The main RDFS constructs are Class and subClass relations, as well as the ability to define domains and a range of properties. 1
http://www.w3.org/RDF/ http://www.w3.org/TR/rdf-schema/ 3 http://www.w3.org/TR/owl-features/ 2
OWL can be considered an evolution of RDF/RDFS in its ability to represent machine-processable semantic content. OWL adds a number of features to RDF/RDFS, such as the local scope of properties, disjointness of classes, cardinality restrictions and special characteristics of properties. Semantic queries are the main means of information retrieval used in current research in this area. Inspiration for a query-based style of reasoning stems directly from the widespread propagation of RDBMS (Relational Database Management Systems). Semantic query languages have a number of features in which they differ from SQL queries due to Semantic Web knowledge, which can be either asserted (explicitly stated) or inferred (implicit), being network structured, rather than relational. Also, the Semantic Web assumes an OWM (Open World Model) in which the failure to derive a fact does not imply the opposite [7], in contrast to closed world reasoning where all relations that can not be found are considered false [8].
3 Related Work The W3C is working on the CC/PP4 (Composite Capabilities / Preferences Profile), an RDF-based specification which describes device capabilities and user preferences used to guide the adaptation of content presented to that device. It is structured to allow a client to describe its capabilities by reference to a standard profile, accessible to an origin server or other sender of resource data, and a smaller set of features that are in addition to or different than the standard profile. A set of CC/PP attribute names, permissible values and associated meanings constitute a CC/PP vocabulary. OMA’s (Open Mobile Alliance) UAProf5 (User Agent Profile) specification, based on the CC/PP, is concerned with capturing classes of mobile device capabilities which include the hardware and software characteristics of the device. Such information is used for content formatting, but not for content selection purposes. The UAProf specification does not define how the user preferences part of the profile is structured. An intelligent software agent capable of performing semantic reasoning is supposed to manage user personalization tasks (i.e., both content selection and formatting) according to user profiles described by an UAProf schema-based OWL ontology. The concept of software agents appeared in the mid-1990’s [9] and resulted in the application of an agent-based computing paradigm in various research domains [10, 11, 12, 13]. However, multi-agent systems have recently become very relevant with the advent of the Semantic Web.
4 User Personalization An explicit user request or an event triggered in a user's device (i.e., terminal) describing e.g., its battery status or location initiates a reaction from the agent managing the course of service provisioning. RDQL6 (RDF Data Query Language) 4 5
http://www.w3.org/Mobile/CCPP/ http://www.openmobilealliance.org/
and SeRQL7 (Sesame RDF Query Language) semantic queries are constructed and performed on two types of information: general device capabilities and individual user preferences. An overview of the agent’s desired functionality is shown in Fig. 2. Using the retrieved information, the appropriate content can be selected, formatted, and presented to the user. In order to achieve proof-of-concept agent-based contextual user personalization, CC/PP compliant user profiles are created according to the OWL ontology based on the UAProf schema.
Fig. 2. User personalization overview. An event from the user’s device is processed by the intelligent software agent and required semantic queries are performed upon knowledge base. Semantic matchmaking mechanism determines appropriate response.
4.1 Ontology Modeling According to the CC/PP and UAProf schema, the main elements of a user profile are components and their corresponding attributes. With reference to that fact, a class named Component, intended to be an upper-class for every component created, was placed in the UAProf schema-based profile ontology and extended by components representing the software and hardware capabilities of a user’s device (classes BrowserUA, HardwarePlatform, NetworkCharacteristics, PushCharacteristics, SoftwarePlatform, WapCharacteristics), as well as individual user preferences (class UserPreferences), which are not included in the UAProf schema. A class Profile was created with the constraint that it must have a relation with at least one component. A fragment of the created ontology’s taxonomy is shown in Fig. 3. Also, a great number of properties representing attributes from the CC/PP and UAProf schema were created and linked to the appropriate components. In addition to the OWL-based mapping of the UAProf specification, classes representing content, content type, information type, and quality of service were added. The Content class contains available content while the InformationType, ContentType and QoS (Quality of Service) classes contain instances which are used to describe available content. For example, a specific instance of the Content class can represent the weather forecast in the form of a low resolution streaming video.
6 7
http://www.w3.org/Submission/2004/SUBM-RDQL-20040109/ http://www.openrdf.org/doc/sesame/users/ch06.html
Fig. 3. A fragment of a class taxonomy of our OWL ontology including three attributes which have the UserPreferences class as the domain, and the InformationType, ContentType and QoS classes as the range. Additionally, three instances of the QoS class are shown.
4.2 User Profiles A great number of user profiles which are in compliance with UAProf specifications can be found in the W3Development’s UAProf repository8. However, these profiles had to be processed in order to comply with the OWL ontology described in subsection 4.1. The UAProf schema was expanded to support user preference description, which enables us to not only format, but also select the appropriate information for each user. Profile components, attributes, and values refer to an ontology, while the profile itself is an instance of a schema.
Fig. 4. A fragment of a user profile showing three components: HardwarePlatform, SoftwarePlatform and UserPreferences, each with three related attributes.
The profiles we created to test our approach covered a variety of mobile devices, ranging from older text and audio-only phones, to newer models capable of reproducing high quality multimedia content. The user preference parts of the profiles also included diverse possibilities regarding the requested quality of service, information and content type. Part of an individual profile, showing how the profile is built from various components and a set of designated attributes, is shown in Fig. 4. 8
http://w3development.de/rdf/uaprof_repository/
We can see how the profile brings together the technical capabilities of the device (which affect content presentation) and the user preferences (which direct the content selection process). 4.3 Proof-of-Concept Implementation In order to demonstrate the inter-play between knowledge contained in the OWL ontology and individual profiles composed of the device capabilities and user preferences (i.e., context information), we conducted multiple agent-based user personalization case studies. Information was retrieved by means of RDQL and SeRQL queries. A Sesame [14, 15] repository with OWL support [16] was utilized to store the required knowledge. The program component implemented in Java provides an interface for repository management, querying and the processing of results. Retrieving and Ranking Content for a Particular User. Considering that user profiles store device capabilities and user preferences, a service provider should be able to deliver the most suitable content in the appropriate form for each user. Semantic queries are used to retrieve the available content which is then ranked according to user preferences and, finally, delivered to the user, as shown in Fig. 5.
Fig. 5. An Intelligent agent communicates with the user’s device. Queries are created (1) and sent to the Sesame repository (2). The Matchmaking algorithm ranks the acquired information (3) according to user preferences. Finally, the preferred content is sent to the user (4).
Consider the following example. Suppose it is necessary to retrieve information regarding all users who are interested in the weather forecast and their device’s screen size. A simple RDQL query can be formed as follows: SELECT ?x,?z WHERE (?x, , ?y), (?x, , ?z) AND ?y="WeatherForecast"
An important role in this process is played by the semantic matchmaking mechanism which enables a more expressive ranking of content by taking into account the meaning, relation and semantic similarity of resources introduced by utilizing Semantic Web languages for information representation.
Content Formatting for Users. Context information and ontology-based user profiles are first used to find users interested in a certain category of content, and then to format the appropriate content making it presentable to each user. It is also verified whether the discovered users have the minimum required technical capabilities. For example, semantic queries can be generated to find all those users who are interested in any kind of images (low or high resolution, different number of colors) and are able to display them. Available images are processed to fit each user’s terminal depending on their technical specifications, as shown in Fig. 6.
Fig. 6. Information formatting demonstrated on an image file representing the weather forecast. Device capabilities stored in the HardwarePlatform component of a profile guide user personalization. Media information is processed to match application-specific requirements.
Finding Users Interested in Specific Content. This scenario supposes that we need to find users who might be interested in a given specific content. Users are chosen if they prefer the same content type, information type or quality of service that the particular content has. Content properties are discovered through semantic query creation and ontology querying similar to that in the previous example, while user preferences are found in the RDF-based knowledge database. The case study is expanded by using the semantic matchmaking algorithm in order to find a wider base of users who might be interested in the information with slightly reduced probability.
5 Conclusion and Future Work In this paper, we describe how context-aware semantic reasoning can affect the course of telecommunication service provisioning and enable agent-based contextual user personalization. The UAProf schema was mapped to an OWL ontology, while features for user preferences support were added. User profiles, describing a number of different mobile devices and user preferences, were created. Finally, case studies demonstrating the advantages of combining the Semantic Web and agent technologies were implemented and described. Future research will include further development of the terminal capabilities ontology by expanding it to provide more context information. Another important step will be to find better and more precise algorithms for semantic matchmaking and ranking of eligible resources which should result in major improvements with respect to telecommunication service provisioning.
Acknowledgements. This work was carried out within research projects 0360362027-1639 "Content Delivery and Mobility of Users and Services in New Generation Networks", supported by the Ministry of Science, Education and Sports of the Republic of Croatia, and "Agent-based Service & Telecom Operations Management", supported by Ericsson Nikola Tesla, Croatia.
References 1. Podobnik, V., Trzec, K., Jezic, G.: Auction-Based Semantic Service Discovery Model for E-Commerce Applications. In: Meersman, R., Tari, Z., Herrero P., et al. (eds.) OTM 2006. LNCS, vol. 4277, pp. 97-106. Springer, Heidelberg (2006) 2. Podobnik, V., Trzec, K., Jezic, G.: Context-Aware Service Provisioning in Next-Generation Networks: An Agent Approach. International Journal of Information Technology and Web Engineering 2(4), 41-62 (2007) 3. Ackoff, R. L.: From Data to Wisdom. Journal of Applied System Analysis 16, 3-9 (1989) 4. Dimkovski, M., Deeb, K.: Knowledge Technology through Functional Layered Intelligence. Future Generation Computer Systems 23(3), 295-303 (2007) 5. Berners-Lee, T., Fischetti, M.: Weaving the Web. Harper San Francisco, New York, USA (1999) 6. Antoniou, G., van Harmelen, F.: Semantic Web Primer. MIT Press, Cambridge, Massachusetts, USA (2004) 7. Walton, C.: Agency and the Semantic Web. Oxford University Press, New York, USA (2007) 8. Grimm, S., Motik, B.: Closed World Reasoning in the Semantic Web through Epistemic Operators. In: Proc. of the Workshop OWL: Experiences and Directions (OWLED), Galway, Ireland (2005) 9. Nwana, H.S.: Software Agents: An Overview. Knowledge and Engineering Review 11(3), 205-244 (1996) 10. Trzec, K., Lovrek, I., Mikac, B.: Agent Behaviour in Double Auction Electronic Market for Communication Resources. In: Gabrys, B., Howlett, R.J., Jain, L.C. (eds.) KES 2006. LNCS, vol. 4251, pp. 318-325. Springer, Heidelberg (2006) 11. Petric, A., Podobnik, V., Jezic, G.: The CrocodileAgent: Designing a Robust Trading Agent for Volatile E-Market Conditions. In: Nguyen, N.T. et al. (eds.) KES-AMSTA 2007. LNCS, vol. 4496, pp. 597-606. Springer, Heidelberg (2007) 12. Trzec, K., Lovrek, I.: Field-based Coordination of Mobile Intelligent Agents: An Evolutionary Game Theoretic Analysis. In: Apolloni, B., Howlett, R.J., Jain, L.C. (eds.) KES 2007. LNCS 4692, pp. 198-205. Springer, Heidelberg (2007) 13. Jurasovic, K., Kusek., M.: Verification of Mobile Agent Network Simulator. In: Nguyen, N.T. et al. (eds.) KES-AMSTA 2007. LNCS, vol. 4496, pp. 520-529. Springer, Heidelberg (2007) 14. Broekstra, J., Kampman, A., van Harmelen, F.: Sesame: A Generic Architecture for Storing and Querying RDF and RDF Schema. In: Horrocks, I., Hendler, J.A. (eds.) ISWC 2002. LNCS, vol. 2342, pp. 54-68. Springer, Heidelberg (2002) 15. Broekstra, J., Kampman, A.: RDF(S) Manipulation, Storage and Querying Using Sesame. In: Demo Proc. of the 3rd Int. Semantic Web Conference (ISWC), Hiroshima, Japan (2004) 16. Kiryakov, A., Ognyanov, D., Manov, D.: OWLIM - A Pragmatic Semantic Repository for OWL. In: Dean, M., et al. (eds.) WISE 2005. LNCS, vol. 3807, pp. 182-192. Springer, Heidelberg (2005)
