Semantic Network as Continuous System Technical University of Košice doc. Ing. Kristína Machová, PhD. Ing. Stanislav Dvorščák WIKT 2010.

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Presentation transcript:

Semantic Network as Continuous System Technical University of Košice doc. Ing. Kristína Machová, PhD. Ing. Stanislav Dvorščák WIKT 2010

2  Motivation  Semantic network  Discrete versus continuous network  Implementation  Conclusions Content Content

3   Quantity of web information cannot be processed by people.   Key search:   based on key words or phrases   results are depended on used vocabulary   search in prepared index   recalls web pages instead of searched information   recall and precision are usually low Motivation Motivation

4 MotivationMotivation Solution?  Web search which is provided automatically by machines (software).  Machines can process bigger amount of information.  Semantic search:  is based on semantic understanding of searched information and contextual information  uses semantic technologies (metadata, ontologies, nonmonotonic logics, semantic agents, semantic networks…)

5 Semantic network Why?  Semantic technologies (RDF, OWL…) represent higher standards.  But they need the creation of parallel web to current heterogeneous web (HTML documents).  Semantic network can represent documents in various forms (HTML, RDF, OWL,…):  our implementation uses Time Delay Neural Network (TDNN)  is enriched by contextual information

6 Semantic network Semantic network In our implementation:   digraf which contains nodes and edges represents SN   nodes represent symbols (pieces of information); have their own validity/invalidity   edges are used to signal transitions; are oriented from start points to end points   signal is a real value from the range

7 Semantic Network Semantic Network The signal propagation in numerical domain: Time delay of given signal (-1, -2, -3 ) causes that symbol “2” became valid before symbol “x” and it became valid before symbol “4”. that symbol “2” became valid before symbol “x” and it became valid before symbol “4”. The mathematic equation 2 x = 9 can be represented by the rule: p + (p x (2, 4), 1) = 9

8 Semantic Network Semantic Network The signal propagation in real estate domain: Time delay of given signals (-1, -2) causes that “garage” (more important demand for user) is fired before “1.floor”. that “garage” (more important demand for user) is fired before “1.floor”. The signal propagation in text document domain: Set of letters can be declared as a word only if these letters became valid in the given order (sequence). For example: “before” “eforeb”

9 Semantic network Semantic network The signal propagation in text document domain:

10 Semantic network Semantic network The signal propagation in text document domain: Approximately 100 squire metres? What does it means? 100-xor 100+x ? 50x2,5 – do not understand125 – understand machine deduces

11 Discrete versus continuous network Discrete versus continuous network Continuous semantic network represents one block of the “Intelligent Search Machine with the Semantic support” :   IM framework – variables of the program are continuous information (not discrete data)   semantic network “lives” some time and hidden knowledge is deduced   semantic network is a dynamic network (recurrent, heterogeneous) Some of the nodes are program modules (e.g. mathematic module).

12 Discrete versus continuous network Discrete versus continuous network Variables as discrete information:   At the time of calculation, the discrete values of variables are considered and the result is a discrete value.   Information z=x+y is forgotten after the discrete time of calculation. int x = 2; int y = 3; // z is 5 int z = xPlusY(x, y); x= 4; // z is still 5 … private int xPlusY(int x, int y) { return x+y; }

13 Discrete versus continuous network Discrete versus continuous network IntDelta x = new IntDelta(2); IntDelta y = new IntDelta(3); IntDelta z = xPlusY(x, y);// z is 5 z.getValue() returns actual value x.setValue(4);// z is 7 z.getValue() … private IntDelta xPlusY(IntDelta x, IntDelta y) { return IntDelta.plus(x, y); } Variables as continuous information : Z continually changes its value during the change of any of the arguments of the sum. Z does not represent static value but information z=x+y.

14 Sampling of the new value Sampling of the new value The implementation cannot avoid the sampling because: 1) semantic network works continuously 2) computer is a discrete machine. An operation can be dependent on time or not:  operation independent on time – considered value can be actualized at the time of access  operation dependent on time – considered value must be actualized continuously  pragmatic access – using of the time window (5 last values, comparable precision).

15 Implementation Implementation The current system version has these four main parts: 1) harvester (document collector) 2) document store 3) semantic network generator 4) inference mechanism (browser and reasoner) It is realized via 3-layer standard J2EE architecture:  DAO Layer  Business Layer  Presentation Layer.

16 Implementation DAO (Data Access Object) Layer is ORM framework using IBATIS and MySQL database. ORM – Object Relation Mapping IBATIS – bridge between objects and databases RDBMS – Relational Data Base Management System

17 Implementation Business Layer  is realized via framework IoC (Inversion of Control container).  It realizes the implementation of SOA (Service Oriented Architecture).  It has logging and transaction realized by AOP – Aspect Oriented Programming. Presentation Layer  presents generalization of a markup which is realized by component oriented framework.

18 Conclusions Conclusions  The possibility of the implementation of dynamic systems.  Novelty of the idea is:  using TDNN (computational intelligence) to meet the vision of the semantic web  using fuzzy - cognitive map for searching and reasoning.  For the future:  extension of the implementation with the transactions to avoid the mistakes during the connections of net’s nodes to other resources.

19 Conclusions Conclusions  The implementation:  has valid architecture which enables easy extension and next development  works correctly in a numeric domain  achieves interesting results in the domain of text documents (similarity of words, resistance against typos)  For the future:  design and realization of the extension based on ontology.

20 Thank you for your attention

21 Implementation - implementation details