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

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Presentation on theme: "Multi-Phase Reasoning of temporal semantic knowledge Sakirulai O. Isiaq and Taha Osman School of Computer and Informatics Nottingham Trent University Nottingham."— Presentation transcript:

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.g geonames) 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:type cas: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

15 Biblography S. Spranger and F. Bry, "Temporal Data Modeling and Reasoning for Information Systems," 2008. C. Welty, R. Fikes and S. Makarios, "A reusable ontology for fluents in OWL," Frontiers in Artificial Intelligence and Applications, vol. 150, pp. 226, 2006. C. A. Welty, "Augmenting abstract syntax trees for program understanding," in Automated Software Engineering, 1997. Proceedings., 12th IEEE International Con-ference, 1997, pp. 126-133. A. Artale and E. Franconi, "A temporal description logic for reasoning about ac-tions and plans," Arxiv Preprint arXiv:1105.5446, 2011. C. Lutz, "NExpTime-complete description logics with concrete domains," Au-to-mated Reasoning, pp. 45-60, 2001. V. Milea, F. Frasincar and U. Kaymak, "Knowledge engineering in a temporal semantic web context," in Web Engineering, 2008. ICWE'08. Eighth International Conference on, 2008, pp. 65-74. M. Klein and D. Fensel, "Ontology versioning on the semantic web," in Pro-ceed-ings of the International Semantic Web Working Symposium (SWWS), 2001, pp. 75-91. Batsakis, K. Stravoskoufos and E. Petrakis, "Temporal reasoning for sup-porting temporal queries in OWL 2.0," Knowledge-Based and Intelligent Infor-mation and Engineering Systems, pp. 558-567, 2011. M. Dahchour and A. Pirotte, "The semantics of reifying n-ary relationships as classes," ICEIS’02, pp. 580-586, 2002.

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