Representation of context-dependant knowledge in ontologies: A model and an application
Introduction
If business managers were asked for defining current information systems using one word, they would probably try to zip their answer in order to use two: connected and massive. Storages populated with Gbytes or even Tbytes of data are available across corporate networks. The situation gets even more complicated if the Internet is considered, as huge amount of valuable data can be harvested from it. Consequently, corporative Knowledge Based Systems (KBSs) – i.e. software systems which use massively expert knowledge in order to solve problems in specific application fields – are expected to incorporate these several information sources to provide complete, accurate, and up-to-date pieces of advice to decision makers.
As a result, functional KBSs usually manage so many resources to solve most of the requests that it is common that users accessing big-scale systems are supplied “excessive” information, in such a way that the time to filter it manually is too long or simply it cannot be processed. This issue has been pointed out in the literature with the name of “information overload” (Eppler & Mengis, 2004) and is a frequent cause of Knowledge Management (KM) failure, since it decreases individual’s performance and leads to unproductive and ineffective management procedures. Thus, the challenge for KBS technologies is to support tailoring and summarizing of information collected from massive, heterogeneous and distributed sources depending on user needs (Farhoomand & Drury, 2002).
Though it has not been so widely studied, information overload is especially dramatic in mobile systems, as handheld devices have reduced data transmission and presentation capabilities. All Decision Support Systems (DSSs) are expected to realize what people really need and to act consequently, but this requirement clearly becomes more critical when nomadic users are involved:
- •
Although new wireless technologies provide Mb/s bandwidth, screen size remains small given that this is mandatory for keeping proper size, weight and battery life of portable devices. Presentation of large volumes of data on a PDA display (not talking about a cell phone, where the state of affairs is even worse) is a critical factor for the success of mobile applications, since it is too easy to annoy users even with the littlest pieces of non-significant data – which could be assumable in a desktop monitor.
- •
On the other hand, it must be kept in mind that the scenario from where a nomadic user requests system support is completely different from those which only have stationary users. Mobile workforce has to face dynamical decision processes happening in the playing field, which almost always means real-time constrained choices with immediate consequences. Moreover, sometimes they will be interacting with a customer, so all their attention cannot be put on the device. Accordingly, ubiquitous users must not be overwhelmed by a bunch of irrelevant data nor it is acceptable to make them manually filter results.
Hence it is widely accepted that the silver bullet for mobile knowledge delivery is smart result filtering: to summarize available data to provide nomadic users with the smallest amount of information which is significant for the decision process. What is “significant” for a user will depend on his circumstances, which can be regarded as a mix of desires, needs and environment facts, i.e. (in a wide sense) the context. Managing context information – that is, being aware of the context – in mobile applications is both interesting and challenging, since situation changes frequently as the holder of the handheld device moves from one scenario to another. Likewise, context varies if the user’s activity does, which will happen often if the device is expected to be carried the most of the time by him.
In this work we will present a proposal to tackle the problem of information overload, paying special attention to mobile KBSs, by using context knowledge. The core of our approach is the Context-Domain Relevance (CDR) model, a formal pattern for representing relevance of information depending on use scenarios in ontological knowledge bases (KBs). Besides the formal semantics of the model, we also provide an algorithm to extract context-dependant summaries by reasoning within the ontology.
We will demonstrate the contributions of our approach describing the Intelligent ASsistant for Outdoors healthcare (IASO), a prototype KBS whose KB is based on this pattern. IASO behaves as an extension to the current information system used in the Clinical Hospital “San Cecilio” of Granada which allows nomadic physicians to get in their mobile devices compact summaries about patients’ health records and recommendations about further tests.
The remaining of this document is structured as follows. Section 2 presents a use case of a healthcare support KBS which illustrates the motivation of this work. In Section 3, we give an overview of the literature about environment awareness in mobile and pervasive computing, besides some current proposals concerning reasoning with contexts and micro-theories in ontological representations. Section 4 is devoted to the formalization of our model and the reasoning procedure, and an example is also depicted. In Section 5, we present the IASO prototype; application features, as architecture, implementation and functionalities, are detailed. Finally, in Section 6 some conclusions and directions for future work are pointed out.
Section snippets
Motivation
Let us suppose a physician who needs to consult a patient’s clinical data in order to set a proper treatment for him. If the healthcare act is taking place inside the hospital, the doctor will be allowed to access the Hospital Information System (HIS) and to retrieve all the patient’s Electronic Health Records (EHRs). Having enough time and knowledge – and depending on the usability of the software system – the specialist will rule out all the useless pieces of information and will get the ones
Related work
Nowadays, advent of new portable devices and wireless communication technologies has resulted in putting more emphasis in mobile applications, given raise to the so-called area of Pervasive Computing. Pervasive Computing aims to develop technologies that support every day routines unobtrusively using a swarm of reduced wireless-connected computing devices (Weiser, 1999). Possible applications of Pervasive Computing areas range from smart rooms to recommendation systems, teleassistance,
Antecedents
In this work we will use ontologies to materialize our model since, as mentioned in Section 3, they have been remarked to be a suitable formalism to build a KB including context knowledge. Ontologies, defined as “formal, explicit specifications of a shared conceptualization” (Studer, Benjamins, & Fensel, 1998), encode machine-interpretable descriptions of the concepts and the relations in a domain using abstractions as class, role or instance, which are qualified using logical axioms.
Properties
Intelligent ASsistant for Outdoors healthcare (IASO)
In this section, we present a prototype of the IASO1 (Intelligent ASsistant for Outdoors healthcare) system, an application which aims to improve specialized attention out of the hospital. We pretend to provide
Conclusions and future work
In this work, we have presented the CDR model, a formal pattern for the representation and management of context-relevant knowledge in ontologies. This model allows to cope with the problem of information overload in KBSs, which is critical in mobile systems due to their special features. Besides the model we also provide a reasoning procedure to infer which sections of the domain knowledge are interesting or significant in a given situation.
We have demonstrated the contributions of our
Acknowledgements
This research has been partially supported by the project TIN2006-15041-C04-01 (Ministerio de Educación y Ciencia). Fernando Bobillo holds a FPU scholarship from the Spanish Ministerio de Educación y Ciencia. Juan Gómez-Romero holds a FD scholarship from Consejería de Innovación, Ciencia y Empresa (Junta de Andalucía).
References (37)
- et al.
Contextualizing ontologies
Web Semantics: Science, Services and Agents on the World Wide Web
(2004) - et al.
A flexible architecture for distributed knowledge based systems with nomadic access through handheld devices
Expert Systems with Applications
(2005) - et al.
A service-oriented middleware for building context-aware services
Journal of Network and Computer Applications
(2005) - et al.
ACAI: Agent-based context-aware infrastructure for spontaneous applications
Journal of Network and Computer Applications
(2005) - et al.
Context-aware multi-agent approach to pervasive negotiation support systems
Expert Systems with Applications
(2006) - et al.
There is more to context than location
Computers and Graphics
(1999) - et al.
Knowledge engineering: Principles and methods
Data and Knowledge Engineering
(1998) - et al.
Improving medical protocols by formal methods
Artificial Intelligence in Medicine
(2006) - et al.
The Description Logic handbook: Theory, implementation, and applications
(2003) - et al.
The semantic web
Scientific American
(2001)
The Unified Medical Language system (UMLS): Integrating biomedical terminology
Nucleic Acids Research
Context in problem solving: A survey
The Knowledge Engineering Review
EasyLiving: Technologies for intelligent environments
The SOUPA ontology for pervasive computing
Ontologies for agents: Theory and experiences
AJAX in action
Manning
The future of computing
Scientific American
Cited by (41)
Semantifying formal concept analysis using description logics
2019, Knowledge-Based SystemsThe MOM of context-aware systems: A survey
2019, Computer CommunicationsOverview of YAM++—(not) Yet Another Matcher for ontology alignment task
2016, Journal of Web SemanticsProcess-aware approach for managing organisational knowledge
2016, Information SystemsSMARTMUSEUM: A mobile recommender system for the Web of Data
2013, Journal of Web SemanticsCitation Excerpt :The central research challenge for such a system is to deliver the right content for the users given the users’ information need and context. In order to achieve this, researchers have proposed recommender systems to deliver and present information about objects that are likely to be relevant for the users [4–7]. Context information can be utilized to enhance the accuracy of recommender systems through specification of information about the situation of the users [8].
Data summarization ontology-based query processing
2013, Expert Systems with ApplicationsCitation Excerpt :The traditional SQL supports information retrieval without taking account of application context. Recently, research (Bobillo, Delgado, & Gómez-Romero, 2008; Dong, Yang, & Su, 2011; Dragoni, Pereira, & Tettamanzi, 2012) has indicated that effective information retrieval must combine search technologies and application context into a single framework in order to provide the most appropriate answer for user’s information needs. A contextual query framework relies on various resources of the application background and environment for optimal retrieval accuracy and efficiency.