A knowledge-based model for context-aware smart service systems

Figures & data

Table 1. Typical context-aware approaches.

Approach	Authors	Context type	Context representation	Knowledge management
User-based	Aaltonen et al. (2005)	• User context • Environment context	• Key-value	• No
Sensor data based	Henricksen and Indulska (2006)	• Sensor data • Static context • User profile	• Graphical modelling	• Relational model
User situation based	Mo et al. (2010)	• User data • User situation	• Database	• Reasoning rules
Goal-based	Kim et al. (2012)	• Goal • Role • Action • Status • Location • Time	• Ontology based model	• Knowledge base
Location-based	Dobson et al. (2016)	• Location context	• Object-based model • Space model	• Knowledge base • Spatial knowledge
User location based	Schmidtke (2020)	• User location	• Ontology based • Network hierarchy	• Context knowledge management

Table 2. Knowledge components of the CAK model.

Level	Objective	Knowledge components	Corresponding types of context
Network of service systems	Service proposal	Know-with	Secondary context
		Know-who	Identity (primary context)
Service system	Service consumption	Know-where	Location (primary context)
		Know-when	Time (primary context)
Service	Service operation	Know-what	Activity (primary context)
		Know-how
		Know-why

Table 3. Concepts of the CAK model at the Network of service systems level.

Concept	Knowledge component	Definition
Entity	Know-who	A stakeholder of the network and has distinct goals.
Resource		Having the responsibility to carry out a contract related to a service.
Contract	Know-with	Defining what to offer and to whom, such as a SLA (service level agreement).

Table 4. Concepts of the CAK model at the Service system level.

Concept	Knowledge component	Definition
Location	Know-where	Situational knowledge about positional relationships of resources that indicates where to request and consume services.
Time frame	Know-when	Situational knowledge representing the period in which certain services are expected to offer or in which consumers are expected to consume services.

Table 5. Concepts of the CAK model at the Service level.

Concept	Knowledge component	Definition
Object	Know-what	A thing or product that consumers can use or consume
Attribute		A piece of information determining the properties of an object.
State		Conditions, modes or situations during which certain business activities are ‘enabled’ and others ‘disabled’
Operation	Know-how	An operation of a service that is used to transit from a state to another state of an object.
Rule	Know-why	A rule represents the implementation of a business rule in a service system.

Figure 1. Elements of the CAK model and their interrelationships.

Table 6. Knowledge components of chatbots at the Service level.

Concept	Knowledge component	Examples
Object	Know-what	Smabot: smartphones like iPhone 11, Samsung Galaxy A32, etc. Adobot: Adobe Acrobat, images, documents
Attribute		Smabot: attributes of smartphones such as price, battery, screen, camera, keyboard, etc. Adobot: image attributes such as background color, layer
State		Smabot: Smartphone devices: Brand new, Sold, Repaired, In Stock, Out Stock Adobot: Image in editing process: original, editing, finished states
Operation	Know-how	Smabot: make the keyboard bigger in Samsung A32; replace the battery of an iPhone 8 Adobot: change the background color; install Photoshop

Figure 2. Architecture of Adobot.

Figure 3. Snapshot of ontology representing the knowledge-based model in Adobot.

Table 7. Comparison of the CAK approach and context-aware approaches.

Approach	Know-What	Know-How	Know-Why	Know-Where	Know-When	Know-Who	Know-With
CAK	*	*	*	*	*	*	*
User-based	*			*	*	*
Sensor data based	*			*	*	*
User situation based	*			*	*	*
Goal-based	*	*	*	*	*	*
Location-based	*			*		*
User location based	*			*	*	*

Figure 4. Charts of user’s assessment of the dialogue quality and of user satisfaction. (a) The dialogue quality from 28 users (b) The satisfaction from 28 users.

Table 8. Measurement of user message understanding of the algorithms used.

Chatbot	Algorithm	Precision	Recall	F1-Score
Adobot	Supervised Embedding	96.35%	94.5%	94.6%
	Ner_spacy	84.31%	87.77%	86.01%
Smabot	Supervised Embedding	95.6%	94.6%	93.9%
	Ner_spacy	77.9%	76.7%	76.1%

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Mo, T., Li, W., Chu, W., & Wu, Z. (2010). CABS3: Context-awareness based smart service system. In 2010 6th International Conference on Wireless Communications Networking and Mobile Computing (WiCOM) (pp. 1-4), IEEE.

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Schmidtke, H. R. (2020). Location-aware systems or location-based services: A survey with applications to CoViD-19 contact tracking. Journal of Reliable Intelligent Environments, 6(4), 191–214. https://doi.org/https://doi.org/10.1007/s40860-020-00111-4

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