Multi-Phase Reasoning of temporal semantic knowledge

1. Multi-Phase Reasoning of temporal semantic knowledge Sakirulai O. Isiaq and Taha Osman School of Computer and Informatics Nottingham Trent University Nottingham United Kingdom.

2. Outlines • Dynamics of information (the web) • Application example • Possible solution • Semantic Web • Simple semantic query • Challenges • Proposed solution • Multiphase Reasoning Framework • Multi-phase Temporal reasoning Model • Future work and Issues of MPR Framework.

3. Dynamics of web Information • Collection of globally distributed text, files, media documents and network links. • The world wide web information is so enormous that personalised information is now required by individual user. • Personalising information may required individual user’s context(s)in service(s) recommendation. • One context might depend on the other in event of Multi-Contexts.

4. Dynamics of Information (cont.) • A large proportion of this information are subjected to changes due to their dynamism • This changes are also time-critical, which can be due to the temporal state inherent from dynamic application domains or increased in the use of nomadic smart devices capable of recording real-time user context • Time-critical temporal information requires automation.

5. Application Example • Consider a context-aware system using obtained information from multiple sources (e.ggeonames) and locally sourced event from the service provider application domain in-line with user’s device information to enrich contextual data on services offer to the users.

6. Solution • Solution to such application involves two stages: • Information exploration: • Exploring the temporal information in the service’s provider domain with regards to relevant information extracted from the user’s device • Exploration the publicly published information in extracting the relevant part • Match-making explored information to produce adequate service.

7. Semantic Web • Simply helps in converting current web information in an form of unstructured and semi-structured documents into web of data • This is done through hierarchical classification and relationship; i.e. description of concepts, terms, and relationship within a knowledge domain. • Built on RDF Framework • Semantic web is built on RDF, which uses XML Syntax, an easy machine understandable language. • Helps to improve automation through using machine understandable meta-data.

8. Semantic web (Cont.) • This advantages of semantic web now encourages people and organisation to publicly publish semantically tagged information. • Lots of the current information are time-critical due to: • Temporal state inherent in dynamic application domain e.g. media, news etc. • Increased demand in the used of nomadic smart devices that are capable of recording real-time devices. • Hence, representing and interlinking time-critical multi-domain and location-aware information play a critical role when enabling and enacting such data.

9. Simple Query Information Retrieval • Such information should be retrieved using a simple Query as below: • Select ?service , ?location Where { ?service cas:relates?context ?context rdf:typecas:Context }

10. Challenges • Exploration and match-making of publicly source information and application domain information • Representing time-critical information semantically. • Temporal Context definition and identification • Multi reasoning and classification of inter-dependent contexts. • Comprehensive coupling and handling of multi contextual information in service recommendation. • Multi-Phase reasoning of inter-dependent multi-contextual information.

11. Proposed Solution

12. Multi-Phase Reasoning Framework • We proposed a multi-phase frame work in conjunction with the current semantic technology to deal with the problems such as: • Abstraction (temporal knowledge abstraction) • Supplementary reasoning of extracted temporal multi-contextual information. • Alignment and match-making the extracted temporal information. • It wraps the service provider application domain in relating users information and published data.

13. Multi-phase Temporal Reasoning Model • Multi-phase temporal reasoning model • Adoption of modelling techniques to overcome the owl limitation through temporal concept isolation • The introduction Validity domain (an element of time) used to • The introduction of Context domain (concepts to for the aggregation of context and segregation of static and temporal contexts)

14. MPR Framework issues and future work • Issues anticipated for MPR framework include: • Scalability • Performances issue for large scale datasets • Quality of services • Anticipated solution applying Aristotle concepts of change • The generation of a substance is the perishing of the other and vice-versa • We are trying to apply this concept contextual work i.e. the generation of one context can be the perishing of the other, then check the effect the performance of the system

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