=Paper=
{{Paper
|id=Vol-2786/Paper37
|storemode=property
|title=IISWS: Integrative Intelligent System for a Multi-Domain Diversified Semantic Search
|pdfUrl=https://ceur-ws.org/Vol-2786/Paper37.pdf
|volume=Vol-2786
|authors=Gerard Deepak,Santhanavijayan A
|dblpUrl=https://dblp.org/rec/conf/isic2/DeepakA21
}}
==IISWS: Integrative Intelligent System for a Multi-Domain Diversified Semantic Search==
https://ceur-ws.org/Vol-2786/Paper37.pdf
296
IISWS: Integrative Intelligent System for a Multi-Domain
Diversified Semantic Search
Gerard Deepak a, Santhanavijayan A a
aDepartment of Computer Science and Engineering, National Institute of Technology, Tiruchirappalli, India
Abstract
The information density is steeply rising over the World Wide Web and there is an urgent need of an integrative
intelligent approach for facilitating recommendations from the Web owing to the data density of the Web.
Knowledge-Driven Web-based recommendation systems are required to reduce the cognitive gap between the user
query and the recommendable contents. An Integrative Intelligent approach for knowledge-centric
recommendations from the Web has been proposed with the unification of SPARQL Endpoints from several
heterogeneous sources through a domain indexing service that yields reasonable dynamic knowledge into the
recommendation framework. The approach integrates classification, Synonymization, Intertwining with RDF, and
a cognizable semantic similarity model for recommendation of queries and thereby the user relevant web pages
with the diversification of search results. The proposed IISWS furnishes an overall accuracy of 91.84% with a small
FDR of 0.1 for the WebKB Corpus dataset. A Normalized Discounted Cumulative Gain of 0.91 has been achieved
by the IISWS.
Keywords
Content Based Filtering, Hash Table, Knowledge Modeling, Ontologies, Semantic Latent Analysis, Semantic Web
1. Introduction These systems do not facilitate the diversification of
search results as there is a sparsity of real-world
The World Wide Web is the largest storehouse of knowledge that is being instilled into the system. Owing
Information. The Web 2.0 is revolutionizing into the to the evolution of the existing Web 2.0 into a
Semantic Web which is the “Web of Data” with very perspicuous Semantic Web, a large amount of data
high data density and cohesiveness. Information available on the current structure of the Web and to
Extraction from the current configuration of the World reason out, a strong semantic approach with can
Wide Web is not just a laborious task but also involves a transform the existing data into knowledge or which can
lot of effort to link the user queries with the contents in incorporate knowledge for the recommendation. A
the World Wide Web. Even if there is a proper learning knowledge-centric web search is the most amicable in
mechanism incorporated, most web-based search the current situation where there is an evident transition
algorithms tend to lag owing to the density of the from the conventional Web to the Semantic Web. There
information that looks quite similar. Most of the Web- are also a large number of domain-centric knowledge
based recommendation system are either only query- bases and RDF stores that are authorized and a few of
centered or user-centered. The query-centric web page them are collective revised and newer versions are
recommendation systems focus only on the relevance of released progressively. It is strategic when the
the web pages to the query that is being entered into the knowledge that has been dynamically integrated based
system. However, when the web page recommendation on Intertwining or Interlinking of the RDF structures
system is user-centric, the focus is mainly on the from the World Wide Web to the semantic inference
personalization and satisfying the needs of the user. Most algorithm for facilitating knowledge-driven web search
of these queries centric and user-centric systems are quite and yield diversified and yet contents that are relevant to
efficient and have tackled the problems of synonymy and the query and also satisfy the user needs, based on the
polysemy. diversification of contents.
Motivation: Knowledge-Driven paradigms
ISIC’21: International Semantic Intelligence Conference, February with low computational complexity is the need of the
25-27, 2021, Delhi, India hour in Web-based Recommendation Systems to cater to
:gerard.deepak.christuni@gmail.com(Gerard Deepak);
vijayana@nitt.edu (Santhanavijayan A) the demanding informational needs of users. Also, the
ORCID: 0000-0003-0466-2143 (Gerard Deepak) Web-based Recommendation Systems needs to be quite
©️ 2021 Copyright for this paper by its authors. Use permitted under Creative
Commons License Attribution 4.0 International (CC BY 4.0). lightweight by transforming the traditional learning
CEUR Workshop Proceedings (CEUR-WS.org) paradigm into reasoning based inferential strategy for
recommending query relevant and user-relevant
information from the Web.
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Contributions: A dynamic knowledge-centric domain. In [8-11] Deepak et al., have incorporated
model for recommending queries and web pages based several intelligent semantics paradigms in combination
on the standards and constructs of Web 3.0 has been with ontologies for recommending web contents in web
proposed. The approach integrates an initial search and personalization. Deepak et al., [12] have
classification model that carefully selects the top-25% of proposed the Differential Semantic Algorithm (DSA)
the most relevant results to cognitively enrich an RDF that uses differential threshold on semantic similarity
driven real-world knowledge from a series of SPARQL algorithms for facilitating personalized web search.
Endpoints based on the domain of relevance of the
Query. The strategy blends a WordNet 3.0 based Elshaweesh et al., [13] have personalized and
Synonymization as well as Intertwining Real-World recommended web pages based on user profile analysis,
Knowledge through indexing services and SPARQL latent semantic analysis, and the browsing behavior of
Endpoints that are quite heterogeneous. Also, an the user with semantic knowledge via the Ontologies.
inferential cognizable semantic similarity by fusing the Sumathi et al., [14] have proposed the IFWIAR for query
Jaccard and the NPMI model is used to inferentially recommendation based on the usage of a domain specific
select the recommendable entities. Experimentations are ontology with improved weighted fuzzy iterative rule-
conducted on the WebKB Corpus and an overall based ontology processing. Though they have achieved
accuracy of 0.91 with a reasonably small FDR of 0.1 has results that are appreciable, the knowledge sparsity is
been achieved. The proposed IISWS is validated by two visible as it is highly domain specific and integration of
baseline models and knowledge-centric variations to real-world knowledge isn’t visible. Omar et al., [15] have
prove the efficacy of derived knowledge and its role in introduced a personalized approach for integrating
web page recommendation. domain knowledge and user profile-based ontology for
transportation domain using WordNet API and semantic
Organization: The rest of paper is organized as conceptual knowledge.
follows. The Literature Survey is detailed in Section 2.
The Proposed Architecture is described in Section 3. The
Implementation in represented is in Section 4. The 3. Proposed Architecture
Performance Evaluation are portrayed in Section 5. The
Conclusions are formulated in Section 6. Figure 1 judiciously describes the architecture of the
proposed which intelligently integrates the phases of
initial classification and integration of real-world
2. Related Literature knowledge to facilitate web page recommendation. The
proposed IISWS does not make use of any static
Soto et al., [1] have put forth a semantic search engine
which is domain-specific for biomedical abstracts that ontologies or knowledge resources, instead a dynamic
update auxiliary knowledge from PubMed on a daily ontology modeling scheme for upper ontology derivation
basis. The search engine uses a NER approach for has been imbibed into the approach using existing real-
biomedical entities and a context-sensitive Acronym world collaborative cognitive knowledge to ensure that
Resolution for concept recognition. Xie et al., [2] have recommendations are not restricted to the conceptual
recommended web pages based on two-fold clustering level but also yield enough individuals when there is a
where the relationship between the user behavior and
conflict of interest, specifically when homonymous
topics are correlated. Bhavithra et al., [3] have
formulated an approach for case-based reasoning terms are identified by the Web Search System. The core
focusing on clustering as a paradigm which is based on principle behind the IISWS is to overcome the Sparsity
weighted association rule mining for recommending web problem in Web Search and strategically overcome the
pages. Singh et al., [4] have imbibed sequential rules problems of synonymy, polysemy, and homonymy.
partially ordered web page recommendation which Also, the design of IISWS is in away such that
predicts the future interests of the users’ web page
Serendipity problem is also overcome by ensuring the
information. Katarya et al., [5] have incorporated the
fuzzy c-means clustering technique for the user query is subject to Query Pre-processing such as the
recommendation of web pages that makes use of users’ Tokenization, Stop Word
navigation details of web pages.
Removal, and Lemmatization. On the pre-processing of
Ontologies have been used in several possible the query terms, a query word set is furnished. In order
combinations in several Web Search paradigms. Ali et to enhance the probability of integration of likewise
al., [6] have used Fuzzy Ontologies in combination with entities, the Query Word Set is subject to
SVM for web content classification. Thanapalasingam et Synonymization where several Synsets for a specific
al., [7] have recommended editorial products based on
query word is anchored with the Query word. The
the domain ontology model. They have depicted the
usefulness of domain ontology and its impact in Synonymization was done based on the integration with
recommendation specific to the computer science a WordNet 3.0 synset generator, and several
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relationships like the Homonym terms, and terms with
polysemous contents has been identified at this stage.
However, the synonyms generated with polysemous and
homonym terms are reserved for future knowledge
enrichment during the Intertwining phase.
Figure 1: Architecture of the Proposed IISWS
The initial query words generated are subject to initial owing to the reason that classification need not be
level classification of the labelled dataset that has been emphasized for being highly accurate, and the
cleaned and pre-processed. The classification is done methodology requires approximate classification for
based on a traditional Naïve Bayes Classifier by fixing future reasoning and inferencing during
the class labels as the pre-processed query word set. The recommendation. For each of the classes, top 25% of
reason for using a conventional classifier is mainly classification results under each label, i.e., the query
owing to the reason that classification need not be word is taken into consideration and is transformed into
emphasized for being highly accurate, and the its equivalent RDF structure. Once the classification
methodology requires approximate classification for results in the top 25% is transformed into RDF, the
future reasoning and inferencing during process of Intertwining is performed where the RDF is
recommendation. For each of the classes, top 25% of linked with real-world SPARQL Endpoint server to
classification results under each label, i.e., the query associate the real-world entities along with their
word is taken into consideration and is transformed into equivalent class labels and formulate a linked RDF
its equivalent RDF structure. Once the classification structure with dynamic aggregation of real-world
results in the top 25% is transformed into RDF, the ontological knowledge in the form of RDF data.
process of Intertwining is performed where the RDF is
linked with real-world SPARQL Endpoint server to The SPARQL Endpoint is linked to Wikidata, DBPedia,
associate the real-world entities along with their LinedCT, voiD Data, and UniProt based on the query
equivalent class labels and formulate a linked RDF centric domain relevant identification of entities.
structure with dynamic aggregation of real-world However, each of these data stores and their domain
ontological knowledge in the form of RDF data. availability is identified with the help of an interim
repository that has been indexed with the domain and
The initial query words generated are subject to initial sub-domain terminologies. Once the Intertwining has
level classification of the labelled dataset that has been been successfully staged, there is synonym induction that
cleaned and pre-processed. The classification is done has been computed previously in order to further enrich
based on a traditional Naïve Bayes Classifier by fixing the density of query centered domain knowledge based
the class labels as the pre-processed query word set. The on dynamic Ontology Generation through RDF
reason for using a conventional classifier is mainly Intertwining. The RDF Intertwining generates extensive
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Cognitive Knowledge for formulation of queries and queries and further choose the relevant web pages. The
recommending query words based on Bigram and Jaccard Similarity is depicted by Equation (1) and the
Trigram integration. The methodology requires an NPMI is depicted in Equation (2). The Normalized
intelligent recommendation strategy for regulating the pointwise mutual information is dependent on the
query formulation which is done based on the pointwise mutual information measure portrayed by
computation of the standard Jaccard Similarity and the Equation (3). Equation (4) illustrates the intersection
Normalized Pointwise Mutual Information Computation incidence of both the Jaccard Similarity and the
Scheme, wherein the top 15% integration of common Normalized Pointwise Mutual Information measure.
elements from either of the two schemes has been
| 𝑆 ∩ 𝑇|
formulated. The recommendation strategy has been 𝐽𝑎𝑐𝑐𝑎𝑟𝑑(𝑆, 𝑇) (1)
|𝑆| + |𝑇| − |𝑆 ∩ 𝑇|
depicted in the Intelligent Inference Algorithm that has
been depicted in the further sections. The
recommendation results are re-arranged in the declining
order of the values of semantic similarity, and further 𝑝𝑚𝑖(𝑥; 𝑦)
𝑛𝑝𝑚𝑖(𝑥; 𝑦) = (2)
yielded to the user. The results have a high degree of − log[𝑝(𝑥, 𝑦)]
diversity as the user has a lot of choices to select because
of the strategies followed in the proposed approach.
Further, if the user is dissatisfied with the yielded results, 𝑝𝑚𝑖(𝑥; 𝑦) = ℎ(𝑥) + ℎ(𝑦) − ℎ(𝑥, 𝑦) (3)
then the query words are re-substituted by the current
user click sub-topics in the recommendation, and thereby
driving the recommendation to a much-focused topic and
SemanticSimilarity= |Jaccard | ∩ | NPMI | (4)
thereby altering the feasible recommendable terms until
the user is satisfied. The semantic similarity is computed
using the intersection of the Jaccard Similarity and the
Normalized Pointwise Mutual Information (NPMI). The 4. Implementation
Jaccard Similarity is computed with a threshold of 0.75
The implementation has been achieved using the Python
while the NPMI is considered between 0 and 1 without
NLTK library and the OntoSpy library. However, the
yielding to its negative values. The threshold of 0.5 is
SPARQL Wrapper interface was modeled with an agent
considered for the NPMI. Further the intersection of the
such that the SPARQL endpoints can be queried for
datapoints between the individual threshold of the
Intertwining. However, a customized domain index
Jaccard Similarity and the NPMI is taken into
repository has been used for mapping and indexing the
consideration for recommending the terms to formulate
terms.
Table 1: Proposed Integrative Intelligent Algorithm (IIA) for Web Page Recommendation
Input: Initially Classified Dataset based on the Query Words as Labels, Query Word Set w, Real
World Knowledge Bases,
Output: Recommended Expanded Queries and their corresponding Web Pages
Begin
Step 1: while (Qw.next()!=NULL)
for each Qw as Label
HashSet R’ ←Select Top 25% and Convert into RDF
end for
end while
Step 2: for each in R’
2.1Select the domain index for R’.current() from a Thesauri
2.2 Based on Index Domain trigger SPARQLEndpoint
2.3 Generate Intertwined Knowledge as Tree Iw
end for
Step 3: Generate SynSets of Qw as Qsyn
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Step 4: while (Qsyn.next()!=NULL)
Tree IEnriched←Repeat Step 2 for Qsyn.current()
end while
Step 5: while (Qw.next()!=NULL)
Set J←JaccardSim(Qw, IEnriched.pos())
Set N←NPMI ((Qw, IEnriched.pos())
return R←J∩N
end while
Step 6: Choose any two levels from R as either super concepts or sub concepts or
From its neighbor and recommend by applying Bigram & Trigram and
Recommend to the user.
Step 7: Repeat Step 5 by matching elements of user-click and by not visiting a single
node more than once.
Step 8: If there exist user clicks, then substitute the maximum topic of user click as
Qw and begin from Step 1 until there are no further user clicks.
End
The proposed Integrative Intelligent Algorithm for Web 5. Performance Evaluation
Page Recommendation is depicted in Table 1 which
takes input as the Classified Datasets based on the Query The experimentations for the proposed IISWS has been
Labels, Query Word Set, and SPARQL Endpoints via a carried out for the WebKB Corpus with 492 benchmark
domain indexing service for the Real-World Knowledge test queries. 72 users were given 40 queries each to give
Stores. The Algorithm furnished the recommended their top 10 recommendations in terms of both query
expanded queries at first, and further their corresponding categories and individual web pages. However, each user
web pages. The algorithm selects the top 25% of the got different variations of the same query, and finally for
classified data from each of the classes with query words each query, top 10 frequently occurring webpage and the
as a label and is transformed into its equivalent RDF. queries were considered as ground truth for
Further, the domain index for the class labels and experimentation.
randomized classes is selected, and based on the domain
index a set of SPARQL Endpoints are selected for The Precision, Recall, Accuracy, F-Measure, and False
including relevant real-World Knowledge for Discovery Rate (FDR) were used as standard metrics for
Intertwining the RDF and formulate a Knowledge Tree, evaluating the performance of the proposed IISWS.
which is further enriched based on the initial set of Also, nDCG (Normalized Discounted Cumulative Gain)
synonyms generated. The generated synonyms are also was used to measure the diversity of the results yielded.
intertwined into real-world knowledge. The selection of Equations (5), (6), (7), (8), and (9) depict the Precision,
recommendable query entities is realized on the basis of Recall, Accuracy, F-Measure, and FDR. Equations (10)
the semantic similarity computation and the and (11) represent the Normalized Discounted
recommendation of queries is done by bigram and Cumulative Gain and the Discounted Cumulative Gain
trigram formulation based on user query clicks. The web respectively.
pages are selectively displayed based on the user-click of
Retrieved∩Relevant
the recommended queries. The process is continued until Precision= (5)
Retrieved
each node of the enriched knowledge tree is visited or
until the user has no clicks recorded. If all the nodes of Recall=
Retrieved∩Relevant
(6)
Relevant
the current knowledge tree have been visited, then the
last few user clicks are once again considered as query Precision+Recall
words to facilitate newer recommendations until the user Accuracy= (7)
2
is satisfied, i.e., till no further user clicks are recorded.
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2×Precision×Recall models and other knowledge level variations in the same
F-Measure= (8)
Precision+Recall
environment as of the proposed IISWS.
FDR=1-PPV (9) It is evident from Table 2 that the proposed IISWS
furnishes an average precision, recall, and accuracy of
DCGα
nDCG = (10) 90.21%, 93.47%, and 91.84% respectively. IISWS
IDCGα furnished a very low FDR of 0.1 with an nDCG of 0.91
∝ which is better performing than the baseline models and
𝑅𝑒𝑙𝑖 its variants. The IISWS yields a 13.6% higher accuracy
𝐷𝐶𝐺 = ∑ (11) than DSA and 6.38% higher accuracy than the IFWIAR.
𝑙𝑜𝑔(𝑖 + 1)
𝑖=1 The reason for a better performance of the IISWS than
Since Query Recommendation is followed by the web the DSA is due to the reason that IISWS has an efficient
page recommendation in the proposed IISWS, the semantic model and heterogenous integrative knowledge
performance measures were computed for the final web form several real-world knowledge resources through
pages that have been furnished by the proposed system. SPARQL Endpoints.
The average performance evaluation for the WebKB
Corpus has been depicted in Table 2 where the proposed The DSA has a strong strategy of regulating the
IISWS and has been benchmarked with several other recommendation by a combination of semantic similarity
models and variants of knowledge. Also, a variation measures with varied thresholds, however, the DSA only
without specific knowledge model has been depicted. focuses on personalization, and does not use any external
The IISWS is also baselined with the Differential static knowledge or dynamic inferential knowledge.
Semantic Algorithm (DSA) [12] that uses a semantic IFWIAR encompasses Ontologies with fuzzy weighted
strategy for recommending web pages and IFWIAR [14] iterative association rule with static domain ontologies.
that uses ontologies for yielding the web pages. The However, owing to the sparsity of domain ontologies
experimentations are conducted for every baseline supplied into the IFWIAR, there is a predominant lag in
its performance.
Table :2 Performance Analysis of the Proposed IISWS Framework
Model/ Variants Precision Recall Accuracy FDR nDCG
% % %
DSA 76.14 79.74 78.24 0.24 0.76
IFWIAR 84.17 86.74 85.46 0.16 0.86
Static Ontological Model 84.12 87.74 85.93 0.16 0.85
Fuzzy Ontologies 83.14 85.74 84.44 0.17 0.84
Without any Knowledge Model 79.47 81.14 76.98 0.21 0.74
Single Source SPARQL Endpoint 82.14 84.32 83.23 0.18 0.82
Proposed IISWS 90.21 93.47 91.84 0.1 0.91
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Recommendations for the proposed IISWS framework
and its variants of the baseline models is depicted in
Figure 2 from where it is clearly evident that the F-
Measure Distribution of the proposed IISWS is much
higher when compared to the other variants in terms of
knowledge and Ontological Models and the baseline
systems namely the DSA and the IFWIAR. The
proposed IISWS framework not just performs based on
the relevance of the finally recommended results but also
focuses on the diversification of results based on the
integration of knowledge from varied sources. The
diversity of results is quantitatively measured using the
nDCG. It is evident from Figure 3 that the nDCG is the
highest for the proposed IISWS framework and it
measures to 0.91. However, the DSA and IWFIAR have
a nDCG of 0.76 and 0.86 respectively. Even the variants
Figure 2: F-Measure Vs Number of of knowledge like that of Static Ontology Models and the
Recommendations Fuzzy Ontologies have a nDGC of 0.85 and 0.84
respectively. The Single Source SPARQL Endpoint has
Since the strategic amalgamation of auxiliary knowledge a nDGC of 0.82. The absence of any knowledge model
along with a strong inferential semantic similarity model has the lowest nDGC of 0.74. The reason for the
is a requisite in the viscinity of the linked semantic data diversification is owing to the rich amount of entities that
for achieving a higher performance, variations in regard are dynamically generated from a system of knowledge
to the knowledge models were demonstrated to show the stores and repositories based on the nature of domain
effectiveness of the IISWS model which has been put through SPARQL Endpoints. Since there is diversity and
forth. The replacement of the heterogeneous dynamic the density in the supplied auxiliary knowledge, the
knowledge model with a single source SPARQL proposed IISWS outperforms the baseline approaches
Endpoint decreased the Accuracy of the IISWS by and the other variants of the same algorithm in terms of
8.61%. Further, the use of Fuzzy Ontologies and the the knowledge models.
Static Ontologies, decreased the Accuracy of IISWS by
7.4% and 5.91% respectively. When a Single Source 6. Conclusions
SPARQL Endpoint is incorporated, there is no sufficient
knowledge amalgamation for the current query. An Intelligent Integrative approach for Knowledge
However, when the Static Ontological Model is used, Centric web page recommendation has been proposed.
there is sufficient auxiliary knowledge but modeling the The IISWS system initially recommends diversified
same is cumbersome. The Fuzzy Ontologies are queries and then the web pages on the basis of the
comparatively less domain-specific than that of the Static knowledge rendered by heterogenous SPARQL
Ontologies thereby exhibiting a lesser accuracy than that Endpoints. The IISWS intelligently encompasses an
of the Static Ontologies. Finally, in the absence of a initial classification of the dataset to choose the top 25%
standard knowledge model in the environment of the of each of the classification, which is further converted
inferential recommendation model of the IISWS, the into RDF that is further interlinked with real-world
accuracy decreased by 14.86% which clearly ensures that knowledge stores through the Multi-source SPARQL
amalgamation of real-world dense cognitive knowledge Endpoints based on selective domain integration. The
reduces the strategic gap between the user-query and the dynamic generation of the query relevant entities,
finally recommended items. synonymization, and an efficient semantic similarity-
based recommendation of the web pages makes IISWS
The accuracy directly correlates with the Precision, as the most desirable web page recommendation system.
Recall, and F-Measure and is inversely proportional to Moreover, the enrichment of knowledge into the system
the FDR. With the increase in the Accuracy, there is a ensures diversified and yet query relevant
significant decrease of the FDR. The IISWS has the recommendations. An overall accuracy of 0.91 with a
lowest FDR of 0.1 when compared to that of DSA and reasonably small FDR of 0.1 and a nDCG of 0.91 has
IFWIAR and all the other variants. The distribution of
the percentage of F-Measure Vs the Number of
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been attained by the proposed IISWS Framework for the
WebKB Corpus dataset.
[9]Deepak, G., & Priyadarshini, J. S. (2018).
Personalized and Enhanced Hybridized Semantic
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