Value-added Knowledge layer for the Context

Value-added Knowledge layer for the Context-aware Personalized Services in the Next Generation Networks Yoo-mi Park, Aekyung Moon, Young-il Choi ETRI, 138, Kajong-no, Yusong-gu, Daejon, ROK, 305-700 and Sang-ha Kim Chungnam National University, 79, Daehak-no, Yusong-gu, Daejon, ROK, 305-764 ABSTRACT This paper proposes the new knowledge layer that can be easily overlaid on the service layer or the control layer of the existing networks to support context-aware personalized service in the next generation networks. Since the traditional communication services have been generally designed for large user groups, they don’t have sufficient capabilities to provide personalized services considering individual situation, intention, and preferences. To overcome the limitation, the proposed knowledge layer collects information from the underlying network and makes them value-added knowledge with knowledge processing technologies. In this paper, we design a network knowledge model to be manipulated on the knowledge layer and service architecture for handling the network knowledge. We also give a couple of examples of providing network knowledge through the knowledge layer for the context-aware personalized services. It is shown that contextaware personalized services can be more efficiently provided through the knowledge layer. We expect that the proposed layer will be an essential component in next generation networks. Keywords: Knowledge, Network knowledge, Context-aware personalized service, Next-generation communication service, Personalization, Contextualization

1. INTRODUCTION There have been a couple of big waves in the area of communication services. In the 1990s, the idea of the intelligent network service makes an innovation of the provision of valueadded call service beyond the conventional concept of the oneto-one voice call. The intelligent network service was the result of the new paradigm adding the intelligence to the basic call service as shown in the figure 1. Information type Knowledge

Contextualization Personalization

Context

Openness Convergence

Location

Open Service

Video

Intelligence Data

Intelligent Network Service

Text

Basic Call Service

Voice

e.g. voice call

1990

Context-aware Personalized Service

e.g. Parlay/OSA API, Parlay X web service,..

e.g. Toll free calls, Prepaid calling , Call screening,..

2000

2010

Year

Figure 1.Paradigm Shift of Communication Services This work was supported by the IT R&D program of the MKE and IITA in Korea [2008-S0007-01], Research and Development of Personalized Service Platform based on Network-wide Knowledge.

Around 2000, the open service architecture was introduced to the telecommunication. This architecture allows network operators to open their network capabilities to the third party service providers by means of providing standard interfaces between service layer and network control layer. The introduction of the open service architecture has the important meaning in which it was the first attempt for the service convergence between telecommunication and IT. Lately various attempts for providing the context-aware personalized service, which considering a situation and preference of a user, are leading a new paradigm of the nextgeneration communication services. It originally comes from the basic requirement of the next-generation communication services, i.e. “Service should be provided for the end-user considering his/her preference at the given situation and seamless service has to be guaranteed in any environment of the user.” However, the traditional communication service infrastructure has limits to support the new service paradigm as follows [1]; ƒ It is impossible to provide services which can be dynamically adapted to a user’s situation. Since the traditional communication services have been designed for large user groups, they didn’t consider individual situation, intention, or preferences. ƒ The user by oneself has to select the device which is suitable for a service and network environment. If the device or the network environment for getting a service is changed, a service is unable to be continuously provided. That is, it doesn't appropriately adapt with the heterogeneous network environment and the multimodality of devices. ƒ Since the user is able to use only subscribed service at the moment, he/she cannot use the unsubscribed service when it is necessary. In other words, it is impossible to provide the active service for the users by considering their current situation regardless of the subscription ƒ In the existing network, the solution which integrates individual static data and dynamic contextual information is not considered. Therefore, it doesn’t have the capability providing the knowledge-based service yet. To overcome the limitation of the existing network and to meet the requirements of the next-generation communication services, a new knowledge processing layer has to be introduced in the existing network with minimum changes of the existing network architecture. The knowledge processing layer provides applications in the service layer with the knowledge about the user. It includes the contextual information concerning the user to be required for the service personalization such as devices which he/she own or can use, service preferences, basic information, etc. This paper proposes the knowledge processing layer overlaid on the existing network to efficiently support context-aware personalized service for the next generation networks. We

named it “Knowledge Layer” as shown in the Figure 2. We also propose a knowledge ontology model to be dealt with in this layer. For modeling, we define that what is network knowledge and which information can be applicable for service personalization, and describe how to classify their types and how to get them from the underlying network. Application Layer

Context-aware Personalized Service Context-aware Personalized Service Context-aware Personalized Service

Knowledge Layer Control Layer or Service Layer Transport Layer

Access Layer

User Device

network information is limited to the presence in IMS. IST-DAIDALOS project [5] which studied B3G service infrastructure and the pervasive network technology for usercentric service provider, has mainly researched on the integration of network technologies from 2003 to 2008. DAIDALOS developed the platform for seamless services and contents delivery in the heterogeneous network environment. DAIDALOS proposed how to realize the requirements of network control layer and service layer from a practical point of view of research. However, the further research of knowledge modeling is the relatively insufficient. From 2004 to 2005, IST-Simplicity project [6] has researched device-free service framework in order to adapt the services to the device’s capabilities and user’s preference. However, they had not focused on the knowledge structure of the network. We analyze the requirements of the knowledge layer and modeled the network information manipulated in the knowledge layer as the first step of the research on the personalized service infrastructure for the next generation network.

3. NETWORK KNOWLEDEG MODELING Figure 2. Knowledge layer in the existing network This paper is organized as follows. Section 2 surveys the related projects. In Section 3, we describe the model of network knowledge and classification criteria. Section 4 proposes how to evolve the raw data into network knowledge. Section 5 concludes this paper with further issues.

2. RELATED WORK Recently, the studies of the vision of the next generation of communications services and the development of prototypes to verify the feasibility of the technologies have been performed in some of European projects. This section introduces the leading projects for service personalization and we briefly compares these projects with the knowledge layer proposed in this paper. WWRF [2], which is an open research forum, proposed the vision and strategy of the future telecommunication services of the I-Centric Service in 2005. The main purpose of I-Centric Service is to consider the human communication behavior, not the technologies that support communication, as the starting point for the design of telecommunication systems. The individual user, “I,” has to be put in the center of service provisioning. They offered the reference network model and described the requirements circumstantially as each technology necessary for providing I-Centric service. However, they didn’t consider the modeling of the knowledge that can be used for future service platform architecture. IST-MobiLife project [3] carried out by Nokia, Siemens, etc from 2004 to 2006 proposed service architecture of I-Centric Communications and made the outstanding results which were next-generation mobile communications service scenarios and requirements. The results of this project had an influence on the following related projects SPICE. However, it was insufficient to accommodate the requirements defined in the existing network because it didn't develop the service architecture they suggested. By the year 2008 the SPICE project [4] has been performed around FT and Orange from 2006. They have focused on the research of the knowledge-based service platform to provide a seamless delivery of variety of content and easy way to develop a new personalized service in B3G. It could be an important meaning that a knowledge-based service platform proposed by SPICE was implemented physically and was demonstrated to the public. They also had built 5 kinds of ontologies, physical space (location), service context, user profiles, recommendation and learning, presence. However, the data model reflecting the

In this section, we define the network knowledge and extract useful information for service personalization from data stored in the network. The extracted information is modeled as the network knowledge ontology.

What is network knowledge? Network knowledge consists of the network information itself and the manipulated network information by knowledge processing technologies such as reasoning, learning, and prediction. Network information is a raw data stored in the network-operator domain. Network information is basic and passive information to be used for providing telecom service, while network knowledge is active and value-added information to be invented for the service personalization and contextualization. Alberthal and Fleming said that ‘a collection of information is not knowledge’ in [7] and [8]. When a pattern relation exists amidst the data and information, the pattern has the potential to represent knowledge. It only becomes knowledge, however, when one is able to realize and understand the patterns and their implications. Therefore, network knowledge has to implicate some patterns of information to find appropriate service which a user is likely to want to use at the given situation. Network knowledge could be the inferred information, the learnt information, or predict information according as which knowledge processing technology is applied to the network information.

Classification of network knowledge In this section, we describe two kinds of network knowledge. As shown in the figure 3, network knowledge consists of low-level knowledge and high-level knowledge. Interred Knowledge (pattern, situation,..)

learning

Low-level Knowledge

prediction reasoning

Usage Behavior User Profile Network Profile

Rule/Policy Network Preference

Context Profile

Preference

Device Profile Service Profile

High-level Knowledge

Terminal Context

User Context

PCS Context

Environment Context

Device Preference Service Preference

Figure 3. Classification of the Network Knowledge

knowledge layer at the given examples in the next section. Low-level network knowledge All network information is included in a low-level knowledge. The low-level knowledge is classified into three concepts: ƒ Profiles are a collection of structured data that describe the static properties of an object, which are required for specific needs. A. User profile: User’s information which can identify the user, such as social security id, name, age, gender, job, etc. B. Device profile: Device’s information generated by manufacturer such as device model, type, capabilities (input/output Modality), etc A. Network profile: static information about network capabilities in which the user is interested from the point of using services, such as operator, coverage, bandwidth, access technologies, etc. B. Service profile: service information that describes the features of the service including service category, service fees, service provider, location where the service is available, etc. ƒ Preferences are user’s conditional choices of service characteristics of an object depending on context and ambient information. User preference consists of a set of policies {condition, actions} in order to apply that it can be dynamically changed according to the user’s situation. A. Network preference: a set of policies that a user prefers to use service on the specific network on the particular conditions B. Device preference: a set of policies that a user prefers to use special device on some conditions C. Service preference: a set of policies that a user prefers a service that is likely to appropriate for him/her. ƒ Context: any information that can be used to characterize the situation of an entity. An entity is a person, place, or object that is considered relevant to the interaction between a user and an application, including the user and applications themselves [9]. A. User context: dynamically changed status about a user such as presence including mood, activity, schedule, and location, etc. B. Device context: the contextual information which a user device can have such as device’s location & status, memory consumption, battery power, RSS (Received Signal Strength) C. Network context: The dynamically changed contextual information which a network can have including current bandwidth, call log, charging information D. Environment context: the environmental information including the weather, a date, and etc. High-level network knowledge To define high-level network knowledge, we first illustrate questions that are likely to occur in the next-generation mobile communications services. For example, the user requests “nearby available devices” or “nearby friends”. In another example, which service does the user usually use when she/he is doing something at location on time with computing entities (device/network). The former is an example of the inferred knowledge based on the location and the latter is an example of the learnt knowledge which has been acquired by keeping the user’s usage behaviors for a long time. How to get network information Table 1 shows how to get the network information from the underlying network or environment. In the future, some information can be taken implicitly and automatically using knowledge processing technologies such as reasoning and learning. We describe reasoning and learning process of the

Table 1. Network information context How to get it Source now In the future user presence user User input Reasoning context location user MLP* Server, IMS HSS** schedule user User input Learning device device’s HSS in detected context location & IMS automatically status HLR*** in cellular system memory device detected utilization automatically battery device detected power automatically network current device detected context bandwidth automatically request on Reasoning, call log network Learning OSS/BS demand S*** charging network request on Reasoning, informaOSS/BS demand Learning tion S environ- date device request on mental or web demand context time device request on or web demand weather web request on demand * MLP : Mobile Location Protocol ** HSS : Home Subscriber Server *** OSS/BSS : Operations and Billing Support Systems type

Network Knowledge Model Figure 4 shows graph illustration of conceptual model of the network knowledge at the user-centric. This model is upper level model to provide personalized services. That is to say, the proposed models can be extended according to specific application domains. Each class is represented by network information and has relationship with other classes. User is located in the special location and has devices more than one. And at the sometime, user does special activity and can use some services. availableAt

Service 0,1

use 1,* requires

locatedIn

availbleOn

Time

on 1,1

1,*

1,* 1,1

knows

User 0,*

1,* owns

Location locatedIn 0,1

Resource 1.*

0,1

Device

1.*

Network

1.*

on

Schedule

havePlan

do

Activity

Environment

Figure 4. Conceptual Model of the Network Knowledge

Table 2 shows the definition of the user who is provided with user-centric services according to user’s context. Table 2. Conceptual Model of User User ≡ ∀userId.xsd:anyURI Π ≤ 1userId Π ≥ 1userId Π ∀locatedIn.Location Π ( T⊆ ≤ 1locatedIn) Π ∀owns.Device Π ≥ 1owns Π ∀uses.Service Π ≥ 1uses Π ∀knows.User Π ≥ 1knows Π ∀do.Activity Π ≥ 1do Π ∃hasPlan.Schedule Π ∀hasPreference.Preference ≥ 1hasPreference In the table 2, user has preference information of services to recommend services with highest user preference. In the case of rating function R (R: Users × Items → Ratings), it is selected the item iu with the highest rating or a set of k highest-rated items for user u and recommend that item(s) to the user [10-11]: ∀u ∈Users, iu' = arg max R(u, i), i ∈ items . The previous recommendation approach to implement R can be categorized into two major classes: content-based approaches and collaborative approaches. Content-based approaches attempt to match items with the user preference and then recommend items with the highest user preference. In the case of collaborative filtering, it matches the user pattern with those of other users having the same tastes and then predicts the items the user will find most interesting. Note that it is difficult to acquisition of other’s behavior and preferences information due to privacy and abundance problems; hence, these limitations need to be considered of algorithms design. As a result, we deal with content-based filtering at the first step. We match the user’s preference to the service category and recommends service lists with highest rated. After designing a conceptual model for network knowledge to be implemented in the knowledge layer, we built an ontology model for them. Since it is widely accepted that ontology model is good for context reasoning recently and context reasoning is a necessary process in the knowledge layer that we suggested, we applied it for modeling of the network knowledge. In figure 5, top level classes of the ontology were shown. We built 56 OWL classes, 31 data properties and 16 object properties finally.

4. SERVICE ARCHITECTURE OF KNOWLEDGE LAYER In this section, we analyze the requirement of functionality of Knowledge layer for data processing and show how to support personalized service by the examples.

Requirements and Functionalities of Knowledge Layer Knowledge layer has the following requirements of functionality. ƒ It should not require altering the existing network architectures so that the knowledge layer could be easily overlaid on the service layer or the control layer. ƒ Knowledge and data processing technologies should be supported for collecting information from the underlying network and evolving them into the value-added knowledge. High level knowledge can be generated from the raw data through knowledge and data processing technologies such as accessing, reasoning, and learning. ƒ It has to provide a set of standard interfaces for application service developers to develop the contextaware personalized communication services. The services can utilize the knowledge about the user generated through this layer from communication network. Functionalities of the Knowledge layer to meet the requirements are as follows: ƒ reasoning :We classified the reasoning process into two types: context reasoning and situation reasoning. Context reasoning is the capability deducing new information from available information using a predefined schema, model or knowledge base. It includes collecting context from underlying network or user’s environment. And situation reasoning is the capability interpreting the situation which a user takes based on the information deduced by context reasoning. ƒ learning : Learning is the capability to keep the history of usage behavior to extract rules and patterns out of massive data sets. It is responsible to set up and maintain the situation model as patterns. Service usage behavior pattern of a user can be used in preferentially recommending the service when the user is in the situation which is similar to the situation in which a user is learnt.

Figure 5. Ontology Model of the Network Knowledge

ƒ

prediction : Prediction is an ability to predict the services which the user is likely to want to use in the near future. It is based on the user’s situation and his/her service usage behavior patterns. The result of prediction can be notified to the service applications that subscribe the event.

usage history of the knowledge layer (⑦-⑫).

Use cases The first use case shows that the call to the user in a meeting is automatically handled with pre-defined policy by ‘Contextaware Call Forwarding Service’. The knowledge layer supports that the service can get personal contextual information in order to decide if the call should be forwarded or not. 6

Context-aware Call Forwarding Service

1

Set Rule for forwarding a call to X

7 Feedback Notify “X is in a meeting” 5 Activity (X) = meeting

Location(X)=room Nearby(x)=people

Trigger Profile

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Inferred Knowledge

5. CONCLUSIONS

Learner

Learnt Knowledge

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Knowledge Layer

Presence server

HSS

LCS server

IMS

Figure 6. Context-aware Call Forwarding Service The inference rule for reasoning his current activity (①-②) in table 3 is described as the ontology model depicted in figure 5. After this rule is triggered by Reasoner(③-④), the service can be notified that user’s activity is a ‘meeting’ at the moment from the Predictor (⑤). Table 3. Inference rule for context reasoning ∀x,y,z. (rdf:type(x, User) ∧ locatedIn (x, y) ∧ rdf:type(y,  location:Room) ∧ Presence.placeType(y, Office) ∧  nearby(x,z) ∧ (rdf:type(z,User) ) ∧ Presence (x,Busy)  ΠInferred.Activity (x, meeting) The second one shows the example of context-aware personalized advertisement service. In figure 7, we assume that ‘Music Recommendation Service’ is one of context-aware personalized advertisement service. Advertisement

Send feedback

We proposed the knowledge layer that can be easily overlaid on the service layer or the control layer to support context-aware personalized service in the next generation networks. Through this knowledge layer, network operators can provide more intelligent knowledge about the user for the service providers and the service providers can develop value added service applications to be suitable for each user. On the other hand, we have still lots problems to be solved to realize the knowledge layer with regard to user privacy, accurate context acquisition, performance of the implementation of knowledge layer, etc. However, it is shown that context-aware personalized services can be more efficiently provided through the knowledge layer. We expect that the proposed layer will be an essential component in next generation networks. For further study, we have a plan to provide a couple of implementation approaches of the knowledge layer and compare performance evaluations for each implementation.

6. REFERENCES [1]

[2] Music Recommendation Service

Notify “X may want to listen to music”

6

3 Location(X)=Bus

Schedule(X)=none Time=5 pm

Table 5. Learnt rule for prediction If (Activity of x = AfterSchool) and (Location of x = Bus) and  (Device of x = mobilePhone)    then Learnt.Activity (x, listenToMusic) 

3

Presence.Activity (x)=Busy Presence.PlaceType(x) = Office

7 Send Music

Table 4. Inference rule for context reasoning ∀x,y,z.  (rdf:type(x,  User)  ∧  locatedIn  (x,  y)  ∧  rdf:type(z,  device:mobilePhone)  ∧  status(z,  device:Idle)  ∧  rdf:type  (y,location:Bus) ^ schedule (x, none) ΠInferred.Activity (x, AfterSchool) 

Predictor

Access 4 Personal information

Preference

2

Reasoner

Context

8

described in table 3(④). The result of the inference rule is applied to the learnt rule by Predictor to predict his next action(⑤). Predictor notifies to the service application that he usually likes listening to music at this moment (⑥). After notification, the service can send new music advertisement, receive feedback from the user and send it back to store in the

Context

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[3]

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feedback User’s usage

Activity (X) = AfterSchool Inferred

Predictor

Presence server

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1

Usage history

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Learnt Knowledge

AfterSchool (x) & OnBus(x) ÎListen to Music

LCS server

[4] [5]

[6]

IMS

Figure 7. Context-aware Personalized Advertisement Service Suppose that a student usually listens to music when he gets on the bus after school. His music service usage behaviors are stored increasingly in the usage history kept in the knowledge layer. Learner can make a learnt rule with behavior pattern from the usage history(①-②). When the usage pattern became mature and Reasoner catch his current situation that he is on the bus after school(③), Reasoner triggers the inference rule

[7] [8]

Sangki Kim and Byung-sun Lee, “Technology Trend of Knowledge-based Telecommunication Services Platform,” Electronics and Telecommunications Trends, Vol.23, No.5, Oct. 2008, pp.12-23. WWRF, Technologies for the Wireless Future: Wireless World Research Forum(WWRF), John Wiley & Son, 2005. Bernd Mrohs et al., “MobiLife Service Infrastructure and SPICE Architecture Principles,” In Proc. of IEEE Vehicular Technology Conf., Montreal, Canada, Sep. 2006, pp.3047-3051. Available at http://www.ist-spice.org/ Rui, L. Aguiar et al., “DAIDALOS: An Operator and Scenario Driven Integrated Project,” In Proc. of IST Mobile and Wireless Communications Summit 2004, Lyon, France, Jun. 2004, pp.1089-1093. G. Bartolomeo, N. Blefari Melazzi, F. Martire, S. Salsano, S Kapellaki, E. Koutsoloukas, G. N. Prezerakos1, N.D. Tselikas, I.S. Venieris, “The Simplicity project and its demonstrator: improving ease of use and personalization of ICT services,” IEEE GLOBECOMM 2006. Nov. 2006, pp.1 – 6. Alberthal, Les. Remarks to the Financial Executives Institute, Oct. 1995. Fleming, Neil. Coping with a Revolution: Will the Internet Change Learning?, Lincoln University, Canterbury, New Zealand.

[9]

Dey AK, “Understanding and using context,” Journal of Personal and Ubiquitous Computing 2001, Vol.5, No.1, 2001, pp.4-7. [10] G. Adomavicus, R. Sankaranarayanan, S. Sen and A. Tuzhilin, “Incorporating Contextual Information in Recommender Systems Using a Multidimensional Approach,” ACM Transactions on Information Systems, Vol.23, No.1, 2005, pp.103-145. [11] G. Adomavicus and A. Tuzhilin, “Toward the Next Generation of Recommender Systems: A survey of the State-of-the-Art and Possible Extensions,” IEEE Transaction on Knowledge and Data engineering, Vol.17, No.6, 2005, pp. 734-749.