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A Novel Model for Mining Association Rules from Semantic
Web Data
Abstract
The amount of ontology’s and semantic annotations for
various data of broad applications is constantly growing. This type
of complex and heterogeneous semantic data has created new
challenges in the area of data mining research. Association Rule
Mining is one of the most common data mining techniques which
can be defined as extracting the interesting relation among large
amount of transactions. Since this technique is more concerned
about data representation, we can say it is the most challenging data
mining technique to be applied on semantic web data. Moreover, the
Semantic Web technologies offer solutions to capture and efficiently
use the domain knowledge. So, in this paper, we propose a novel

2. method to provide a way to address these challenges and enable
processing huge volumes of semantic data, perform association rule
discovery, store these new semantic rules using semantic richness of
the concepts that exist in ontology and apply semantic technologies
during all phases of mining process.
Existing System
 The topic coverage of TREC profiles was limited. The TREC user
profiles had good precision but relatively poor recall performance.
 Using web documents for training sets has one severe drawback: web
information has much noise and uncertainties.
 As a result, the web user profiles were satisfactory in terms of recall,
but weak in terms of precision. There was no negative training set
generated by this model.
 Thus, semantic annotated data does not have a rigid structure. As a
result, there would be structural heterogeneity problems. Moreover,
traditional data mining algorithms work with homogeneous datasets
which include transactions, subsets of items.
 The problem of mining to discover all association rules w confidence
greater than the user-specified support and minimum confidence
respectively.

3. DISADVANTAGE
 We should point out that even though using the IIS can significantly
alleviate both the local interface schema inadequacy problem and the
inconsistent label problem, it cannot solve them completely.
 For the first problem, it is still possible that some attributes of the
underlying entities do not appear in any local interface, and as a
result, such attributes will not appear in the IIS.
 If one or more of these annotations are not local attribute
names in the attribute mapping table for this domain, then using the
IIS cannot solve the problem and new techniques are needed.

4. PROPOSED SYSTEM
 Each of these annotators exploits one type of features for annotation
and our experimental results show that each of the annotators is
useful and they together are capable of generating high quality
annotation.
 A large portion of the deep web is database based, i.e., for many
search engines, data encoded in the returned result pages come from
the underlying structured databases. Such type of search engines is
often referred as Web databases (WDB). A typical result page
returned from a WDB has multiple search result records (SRRs).
 Specifying temporal constraints, specifically non sequenced
semantics, in the temporal data dictionary as metadata.
 Our proposed approach provides a mechanism to represent telic/atelic
temporal semantics using temporal annotations.

5.  Using IISs has two major advantages. First, it has the potential to
increase the annotation recall.
 Since the IIS contains the attributes in all the LISs, it has a better
chance that an attribute discovered from the returned results has a
matching attribute in the IIS even though it has no matching attribute
in the LIS
Advantage
 One advantage of this model is its high flexibility in the sense
that when an existing annotator is modified or a new
annotator is added in, all we need is to obtain the applicability
and success rate of this new/revised annotator while keeping
all remaining annotators unchanged.
 We propose a clustering-based shifting technique to align
data units into different groups so that the data units inside the
same group have the same semantic.
 Instead of using only the DOM tree or other HTML tag tree
structures of the SRRs to align the data units.

6.  We also employ a probabilistic model to combine the results
from different annotators into a single label.
Hardware Requirements
SYSTEM : Pentium IV 2.4 GHz
HARD DISK : 40 GB
RAM : 256 MB
Software Requirements
Operating system : Windows XP Professional
IDE : Microsoft Visual Studio .Net 2008
Database : Sql server 2005
Coding Language : C#.NET