Enhancing the Results of Recommender Systems

using Implicit Ontology Relations

Lamiaa Abdelazziz and Khaled Nagi

Dept. of Computer and Systems Engineering, Faculty of Engineering, Alexandria University, Alexandria, Egypt

Keywords: Recommender Systems, Ontology Mapping, Quality of Recommendation, Performance Analysis.

Abstract: Sharing unstructured knowledge between peers is a must in virtual organizations. The huge number of doc-

uments available for sharing makes modern recommender systems indispensable. Recommender systems

use several information retrieval techniques to enhance the quality of their results. Unfortunately, every peer

has his/her own point of view to categorize his/her own data. The problem arises when a user tries to search

for some information in his/her peers’ exposed data. The seeker categories must be matched with its re-

sponders categories. In this work, we propose a way to enhance the recommendation process based on using

simple implicit ontology relations. This helps in recognizing better matched categories in the exposed data.

We show that this approach improves the quality of the results with an acceptable increase in computation

cost.

1 INTRODUCTION

A Virtual Organization is a temporary network or-

ganization, consisting of independent enterprises

that come together swiftly to exploit an apparent

market opportunity. The enterprises utilize their core

competencies in an attempt to create a best-of-

everything organization in a value-adding partner-

ship, facilitated by information and communication

technology (Fuehrer and Ashkanasy, 1998). Due to

the autonomous nature of the participants of virtual

organizations, knowledge sharing cannot be done in

a structured and centralized way. Peers in a virtual

organization have a large amount of documents and

each peer (or group of peers) have their own way of

classifying them. These two facts create great chal-

lenges to any document recommender system. A

typical Recommender System (RS) acting in this

environment should be distributed and autonomous

in order to match the nature of virtual organizations.

Typical RS use several Information Retrieval (IR)

techniques to generate good results. Moreover, RS

should also match the category structure of the seek-

er with that of the responder. For this to work, the

search engines lying within the heart of the RS

should be extended.

We base our work on KARe; which stands for

Knowledgeable Agent for Recommendations

(Gomez Ludermir et al., 2005), (Guizzardi-Silva

Souza et al., 2007). It is a multi-agent recommender

system that supports nomadic users sharing

knowledge in a peer-to-peer environment with the

support of a nomadic service. We extend this system

in order to enhance the quality of the results coming

from search component of the RS. This is done by

enriching the search query and enhancing the rank-

ing process of the result set. Our means is employing

extra ontological information provided by the peers.

Ontology has been used a lot in harmonizing

knowledge sharing where it shows great success. In

our distributed and autonomous scenario, we restrict

ourselves to using simple implicit ontological rela-

tions since we do not want to burden the peer with

defining their own elaborate ontology (or else they

will simply not do it) or force them to use a central-

ized ontology (since it is not applicable in such a

heterogeneous environment).

However, improving the quality of results often

involves more computation. For this reason, we test

our extension against the original system using the

same dataset to quantify the increase in quality ver-

sus the increase in computation cost during indexing

and searching. We also use a second dataset to veri-

fy the generality of our solution.

The rest of the paper is organized as follows.

Section 2 provides a background on recommender

systems. Our proposed system is presented in Sec-

tion 3. Section 4 contains an assessment of our pro-

Abdelazziz L. and Nagi K..

Enhancing the Results of Recommender Systems using Implicit Ontology Relations.

DOI: 10.5220/0004105700050014

In Proceedings of the International Conference on Knowledge Engineering and Ontology Development (KEOD-2012), pages 5-14

ISBN: 978-989-8565-30-3

 2012 SCITEPRESS (Science and Technology Publications, Lda.)

posed system and its implementation while Section 5

concludes the paper.

2 BACKGROUND

2.1 Recommender Systems

Recommender Systems (RS) are software tools and

techniques providing suggestions for items to be of

use to a user. The suggestions relate to various deci-

sion-making processes, such as what items to buy,

what music to listen to, or what online news to read

(Ricci et al., 2011). (Burke, 2007) provides a good

taxonomy for distinguishing between recommender

systems. Recommender Systems can be categorized

in the following classes: content-based, collabora-

tive filtering, demographic, knowledge-based, com-

munity-based, and hybrid recommender systems.

In content-based RS, the system learns to rec-

ommend items that are similar to the ones that the

user liked in the past. The similarity of items is cal-

culated based on the features associated with the

compared items. Content-based RS can be even

found in early standard literature as in (Balabanovic

and Shoham, 1997).

Collaborative filtering RS are also called "peo-

ple-to-people correlation". In their simplest form,

implementations of this approach recommend to the

active user the items that other users with similar

tastes liked in the past (Schafer et al., 2007). The

similarity in taste of two users is calculated based on

the similarity in the rating history of the users.

Demographic RS recommend items based on the

demographic profile of the user. Many web sites

dispatch their users to particular pages based on their

language or country. Other criteria include age, gen-

der, etc., if this information is collected in the user

profile.

Knowledge-based RS recommend items based on

specific domain knowledge about how certain item

features meet users needs and preferences. Notable

knowledge based recommender systems are con-

straint based or case-based (Bridge, 2006). In these

systems, a similarity function estimates the matching

degree of the recommendations to the user needs.

Here the similarity score can be directly interpreted

as the utility of the recommendation for the user.

Community-based RS recommend items based

on the preferences of the user friends. The emer-

gence of social networks, such as Facebook, gave

rise to this type of systems. Social networks contain

billions of records holding user behavioral patterns

and combining them with a mapping of their social

relationships.

Hybrid RS are based on the combination of the

above mentioned techniques. Collaborative filtering

methods suffer from new item problems, i.e., they

cannot recommend items that have no ratings. This

does not limit content-based approaches since the

prediction for new items is based on their descrip-

tion (features) that are typically available. Given two

(or more) basic RS techniques, several ways have

been proposed for combining them to create a new

hybrid system. Four different recommendation tech-

niques and seven different hybridization strategies

are compared in (Burke, 2007).

2.1.1 Complementary Role of Information

Retrieval

Information Retrieval (IR) assists users in storing

and searching various forms of content, such as text,

images and videos (Manning, 2008). IR generally

focuses on developing global retrieval techniques,

often neglecting the individual needs and prefer-

ences of users.

Nevertheless, both IR and RS are faced with sim-

ilar filtering and ranking problems. That's why at the

heart of RS usually lies a search engine, such as the

open source Lucene (Hatcher and Gospodnetic,

2004). Queries submitted to the search engine are

enriched with RS-relevant attributes collected by the

RS and associated to the resultset.

Nowadays, various search engines also apply

some form of personalization by generating results

to a user query that are not only relevant to the query

terms but are also tailored to the user context (e.g.,

location, language), and his/her search history.

Clearly, both RS and IR will eventually converge to

one intelligent user assistant agent.

2.1.2 Complementary Role of Taxonomies

and Ontologies

Taxonomy is a hierarchical grouping of entities.

Ontologies are a machine readable set of definitions

that create a taxonomy of classes and subclasses and

relationships between them (Deng and Peng, 2006).

Both taxonomies and ontologies are used to en-

hance the quality of results suggested by RS. The RS

can use the taxonomy structures and ontology to

refine the filtering and adjust the ranking of the re-

sults sent by the IR internal component. Since most

of our knowledge is not hierarchical, it is intuitive to

assume that an ontology-based approach would lead

to better results. Yet, there is an overhead in defining

ontologies by the user and creating a match for the

nodes of different ontologies in case of peer-based

KEOD2012-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

autonomous systems, such as multi-agent environ-

ments.

Clearly, a trade-off would be using implicit on-

tology relations which can easily be defined by the

user and then matched by the system. The simplicity

of the definition of the ontology is critical factor in

convincing the autonomous peer to define it.

2.2 Example of Artifact

Recommendation System

KARe (Knowledgeable Agent for Recommenda-

tions) is a typical example of artifact recommender

systems (Gomez Ludermir et al., 2005), (Guizzardi-

Silva Souza et al,. 2007). KARe is a multi-agent

recommender system that supports nomadic users

sharing knowledge in a peer-to-peer environment.

Supporting social interaction, KARe allows users to

share knowledge through questions and answers.

Furthermore, it is assumed that nearby users are

more suitable for answering user questions in some

scenarios and it uses this information for choosing

the answering partners during the recommendation

request process.

The first goal of KARe is to develop a distribut-

ed system for artifact recommendation. It aims at

increasing the precision of current recommendation

algorithms. KARe mainly consists of the following

components, illustrated in Figure 1 (Gomez Lu-

dermir, 2005).

The information retrieval component is divided

into two parts. The first part is the process where the

user creates an index of the knowledge artifacts and

concepts. The second is the recommendation mech-

anism which consists of a searching process for the

knowledge artifacts. KARe includes the user context

in the searching mechanism, providing semantics to

the artifacts (i.e., relating it to the concept it is asso-

ciated with). Similar documents are grouped by the

user under the same concept in the context tree. Be-

fore submitting the query, the user assigns it to a

specific concept. By doing this, the user gives the

system extra information on the query content lead-

ing to more accurate results.

The recommendation agent component simulates

the natural social process involved in knowledge

sharing by exchanging requests (questions) and rec-

ommendations (answers). Furthermore, the agents

have to control the user knowledge base, i.e., when-

ever a recommendation arrives, the agent stores it in

his knowledge base. Social interaction involved in

the recommendation process is modeled as agent

interaction.

The peer discovery component finds potential

peers based on proximity information. The system

scans the neighborhood for other devices. When new

bluetooth-enabled devices are found this information

is forwarded to the KARe scanner to check whether

the device participates in the KARe platform or not.

The KARe scanner prepares a message and sends it

to the peer assistant agent. If the peer assistant finds

the agent representing the device in the KARe plat-

form, then it sends a message back to the KARe

scanner.

Figure 1: KARe Architecture.

We choose KARe as a base for our work due to

the following reasons:

 Its multi-agent nature suits the environment of

autonomous virtual organizations.

 Documents fit perfectly in the artifact concept

of KARe.

 It comprises a standard search engine: Lucene

(Hatcher and Gospodnetic, 2004).

 It originally uses taxonomies in structuring its

recommendations.

 It is extendible due to its origins in research

labs.

 It belongs to the most general class of RS;

which is the hybrid family. It combines

knowledge-based, location (similar to demo-

graphic), community-based, and content-based

approaches.

In our work, we concentrate on the first (lowest

component): the information retrieval component.

3 PROPOSED SOLUTION

KARe inherently supports a distributed knowledge

management approach. One challenge, however, is

gaining user acceptance to spend more time in feed-

ing the system with documents and classifying them.

Since the way each user classifies his/her own

knowledge is particular, we cannot impose a com-

mon classification for their artifacts.

EnhancingtheResultsofRecommenderSystemsusingImplicitOntologyRelations

Each user is allowed to define and use his/her

own taxonomy represented in OWL format (OWL,

2009) to classify artifacts. Figure 2 illustrates a user-

defined ontology that holds the artifacts; computer

science research papers in this case. This ontology

expresses the peer’s point of view and does not typi-

cally match with the standard classification system

of the Association of Computing Machinery (ACM,

1998) used as a reference base in KARe and illus-

trated in Figure 3.

Figure 2: User-defined ontology.

Figure 3: ACM ontology.

We integrate the Protégé Ontology Editor (Tu-

dorache et al., 2008) in our proposed system to ena-

ble the peer editing his/her OWL ontology file.

Through the editor, the user can extend the basic

taxonomy trees with other implicit relations such as

sibling, parent and related-to between the different

tree nodes. The more relations are added, the more

support is given for the recommender system in de-

tecting the best matched categories. Figure 4 illus-

trates a sample ontology relation added to the ACM

taxonomy.

Figure 4: Sample ontology relation added to ACM.

3.1 Component Architecture

The ontology files defined in the previous section

are fed to the adapted information retrieval compo-

nent. Figure 5 illustrates the integration of new com-

ponents of our proposed system within the infor-

mation retrieval layer.

Figure 5: System components of the information retrieval

layer.

The question contains the name of the document;

the questioner is searching for and expecting rec-

ommendations around it. It is important to note that

the answer will contain this document- if found - as

a special case, since the answer of the responder

involves recommendations related to this document

and not only the document in question. The

knowledge artifacts represent the document libraries

exposed by each peer and constitute the search pool

for the recommendation seekers. The index terms are

the words contained in each document after perform-

ing text preprocessing steps. The term frequency

vector defines the frequency of occurrence of each

specific term in a document. The document vector

represents the terms in the documents together with

their frequencies. It is used to formulate the docu-

ment matrix where the documents are represented as

rows and the terms as columns and the term fre-

quencies as cell values. The concept vectors repre-

sent concepts or nodes in the ontology. The dimen-

sion size is that of the vocabulary (i.e., number of

indexed terms). In order to create the vectors it is

KEOD2012-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

necessary to read all indexed terms and count their

occurrences in the documents during the indexing

process. The questioner and responder indices rep-

resent the store for the concept and document vec-

tors representing the indexed terms and term fre-

quencies.

3.2 The Indexing Process

Figure 6 shows the indexing sequence diagram in

UML notation. The indexing process is triggered by

the user usually after adding documents to the

knowledge artifact store. The Indexer is the class

that receives the method call

createIndex from

the user and is responsible for handling the process.

The Indexer receives two parameters: a list of docu-

ments to be indexed and the ontology that classifies

them. The first step towards the creation of the index

is to parse each concept of the ontology and the re-

lated relations associated with each concept. During

the concept parsing, the indexer parses each

knowledge artifact to create the vocabulary and in-

dex terms. Once indexing is completed, the Indexer

creates the vectors for the concepts and knowledge

artifacts. During this step, the weight for each term

in each document and concept is calculated and

stored within the index.

Figure 6: Indexing sequence diagram.

3.3 The Searching Process

The searching sequence diagram is shown in Fig-

ure 7. The major contribution is shown in the last

part of the sequence diagram after returning the best

matching concepts. Here, we use the implicit ontol-

ogy relations saved during the indexing process and

retrieve the corresponding concept vectors and their

artifacts to include them in the calculation process.

The similarity is calculated and the query vector and

the list of the retrieved documents are ranked ac-

cording to the similarity. The following is a descrip-

tion of the methods in Figure 7.



Query: is an indication from the user that a

question is posted. The parameters are the

question itself, the vocabulary spoken by the

questioning peer and the concept vector asso-

ciated with the question.



ParseQuery: is a method for pre-processing

the question. It performs stemming and re-

moves the stop words from the query.



CreateQueryVector: compares the question

with the knowledge artifacts. For this method

to work, we must create a vector representa-

tion of the question itself.



GetVector, StoreSimilarity and Get-

BestConcepts

: the questioning concept vec-

tor is compared to each concept vector on the

destination taxonomy. For that, we retrieve

each vector and check its similarity with the

questioning concept vector. At the end of the

process, we are able to retrieve the best match-

ing concepts with the questioning concept.



NormalizeConceptVector: aligns the ques-

tioning concept vector with the targeted vec-

tors and vocabulary.



GetListOfRelatedConcepts: for each of

the best matched concepts, we retrieve the re-

lated concepts to include in the next compari-

son.



RecalculateSimilarity: in this step, we

check the similarity between the newly related

concepts and the best matched concepts.



ReplaceBestMatchedconcepts: if the sim-

ilarity calculation shows better concepts we

reorder and replace the selected three concepts

with better ones. The number three is arbitrary

chosen and can be changed in the configura-

tion files.



GetConcept and GetAtrifact: once we

have a good concept, we retrieve its artifacts.



StoreArtifactSimilarity: calculates the

similarity among the documents from the re-

lated concepts and the question vector. The

method returns the resulting documents with

associated similarities.

EnhancingtheResultsofRecommenderSystemsusingImplicitOntologyRelations

Figure 7: Searching sequence diagram.

The pseudo-code of the searching steps is shown in

Figure 8.

Figure 8: Recommendation algorithm.

3.4 Example

The following is a simplified example of the rec-

ommendation process performed by our system.

Figure 9 shows the questioner categorization of its

library while Figure 10 illustrates that of the re-

sponder after adding few implicit ontological rela-

tionships.

During the indexing process, concepts and doc-

ument are parsed to build the document matrix. As-

suming the following six books: “Design Patterns

Java Workbook”, “Effective Java Programming

Language Guide”, “Micro JAVA Game Develop-

ment”, “Java Collections”, “Client-Side Java Script

Reference”, and “Java 2 Network Security” under

the concept “Java”, the resulting document matrix is

illustrated in Table 1 and the concept vector for “Ja-

va” is illustrated in Table 2.

Figure 9: Questioner library categorization.

Figure 10: Responder-specific implicit ontology relations.

Table 1: Sample document matrix.

Table 2: Sample concept vector for term “Java”.

Searching for the document “Swing Basic Com-

ponents” under the concept “java”, the system to-

kenizes the question into terms, i.e., “Swing”,

“Basic”, and “Components” and assigns a frequency

for those terms (in this case all have the frequency 1

due to the absence of duplication). The system com-

pares the associated concept vector with each con-

cept vector of the responder. This process is done

KEOD2012-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

with an intersection between the questioner vector

and the responder vector. The questioner selected

concept vector (calculated above as the average of

the document vectors belonging to that concept) is

shown in Table 2. Using the cosine similarity meas-

ure, the projected vector is compared to each of the

responder concept vectors. The best three matched

concept vectors are shown in Figure 11.

Figure 11: Best three matched concepts.

“Java”, “J2EE”, and “J2ME” are the three most re-

lated concepts considering the cosine similarity of

the concept vectors. This list is updated taking into

consideration the related concept list from the im-

plicit ontological relations generated during the in-

dexing phase. Assuming that “Programming books”

is related to “Java”, “other” to “J2EE”, and “J2EE”

to “J2ME”, the concept called “other” represents one

of the user classifications and indicates that match-

ing concepts does not necessarily depend on the

name of the classification and that all of them are

included in the cosine similarity calculation. In this

particular example, searching for a book about

Swing, the system searches in all siblings of java,

J2EE and J2ME concepts and replace related con-

cepts. According to the cosine similarly, the system

replaces “J2ME” with the concept “other” since it

has a relative term frequency of 150 for the term

Figure 12: Best three matched concepts after replacement.

“swing” as compared to 40 for the same term in the

concept “J2ME” as illustrated in Figure 12.

4 ASSESSMENT

4.1 Datasets

We use two different datasets. The first dataset is the

same one used in evaluating the original KARe sys-

tem. We insist on using the same dataset to quantify

the increase in quality versus the increase in compu-

tation cost during indexing and searching. A sum-

mary of the dataset is found in Table 3 (Dataset I).

Taxonomy A collects papers and classifies them

according to user specific point of view. Taxonomy

B is taken from the ACM Classification System. In

our assessment, we simulate the questions and an-

swers using the title and the body of the scientific

papers. We test whether the algorithm is able to re-

trieve a paper giving its title or keywords from its

abstract.

We also use a second dataset to ensure the gen-

erality of our solution. The second dataset is shown

in Table 3 (Dataset II). The second dataset is a real

library of programming books found at a medium

sized software company. It is classified from two

points of views and is used as questioner and re-

sponder exposed libraries. This dataset consists of

125 programming books as a questioner source and

206 as a responder target; thus slightly smaller than

dataset I but has the advantage of having much high-

er terms frequencies, since every book contains hun-

dreds of pages unlike the papers in dataset I with

maximum of 20 pages per paper.

Table 3: Summary of datasets.

Dataset I II

Taxonomy A B A B

Number of documents 250 315 125 206

Number of concepts 28 15 32 24

Average doc/concept 9 21 4 9

4.2 Input and Output Settings

To start the experiment execution, we give the sys-

tem the following as input:

 the title of a document, and

 the concept associated with the paper.

The outputs are:

 the list of matched concepts,

 the list of documents classified under the re-

sulting concepts, and

EnhancingtheResultsofRecommenderSystemsusingImplicitOntologyRelations

 the cosine similarity measure attached with

each concept.

To distinguish between the false/true posi-

tive/negative alarms, we search for a specific paper

or book where we previously know that it already

exists in the target peer shared data pool.

4.3 Performance Measures

In our assessment, we use the following standard

performance indices:

 Number of document hits: the average number

of hits returned by the responder.

 Recall: is the fraction of the documents that

are relevant to the query that are successfully

retrieved.

 Precision: the fraction of retrieved documents

relevant to the search.

 F1-Measure: is a measure of test accuracy. It

considers both the precision and the recall.

The F1-Measure can be interpreted as a

weighted average of the precision and recall,

where an F1-Measure reaches its best value at

1 and worst score at 0.

In addition to scalability measures such as:

 Indexing time vs. the number of documents,

and

 Searching time vs. the number of documents.

4.4 Results of Dataset I

In this set of experiments, we perform 75 queries. A

summary of the results is shown in Table 4. Under

the original implementation, the number of docu-

ments found is 42. Using our ontology-based solu-

tion, the number increases to 59. The average recall

is also increased from 0.573 to 0.786. Figure 13

shows the detailed plotting of the recall versus the

number of queries. The precision is also enhanced

from 0.153 to 0.238. Figure 14 shows the detailed

plotting of the concept precision versus the number

of queries. Consequently, the derived F1-Measure is

enhanced from 0.24 to 0.365.

Table 4: Summary of the result of dataset I.

3 concepts

(taxonomy)

3 concepts

(ontology)

Document recall 0.573 0.786

Concept precision 0.153 0.238

F1-Measure 0.24 0.365

Considering the scalability measures, our proposed

solution incurs a higher cost of computation - as

expected - due to the increase in the result quality.

Figure 13: Recall vs. number of queries for dataset I.

Figure 14: Precision vs. number of queries for dataset I.

The good news is that both indexing time, illustrated

in Figure 15, and searching time, illustrated in Fig-

ure 16, increase with the same rate as the original

KARe implementation. The relative increase in pro-

cessing compared to KARe does not increase above

10% which is a fair price to pay for the improvement

in quality especially that the absolute values for both

indexing and searching times are very acceptable.

Figure 15: Indexing time for dataset I.

KEOD2012-InternationalConferenceonKnowledgeEngineeringandOntologyDevelopment

Figure 16: Searching time for dataset I.

4.5 Results of Dataset II

In this second set of experiments, we perform 20

queries. The summary of the results is shown in Ta-

ble 5. The number of documents found using our

proposed solution doubles from 6 to 12 when com-

pared to the KARe implementation. The recall

measure also doubles from 0.3 to 0.6. The precision

increases from 0.12 to 0.2; and boosting the F1-

Measure from 0.17 to 0.3. It is worth mentioned that

the relative improvement for this dataset is very sim-

ilar to the results of the first set of experiments.

Table 5: Summary of the result of dataset II.

3 concepts

(taxonomy)

3 concepts

(ontology)

Document recall 0.3 0. 6

Concept precision 0.12 0.2

F1-Measure 0.17 0.3

In Figure 17 and Figure 18, the recall and preci-

sion are respectively plotted versus the number of

queries.

Figure 17: Recall vs. number of queries for dataset II.

Figure 18: Precision vs. number of queries for dataset II.

The scalability measures reveal a slight increase in

the computation time here too. Again, the relative

increase in both the indexing time, illustrated in Fig-

ure 19, and the searching time, illustrated in Figure

20, are around 10% for all values of the document

counts. An interesting observation, however, is made

when comparing the absolute indexing times of ex-

periment I, illustrated in Figure 15, with that of exper-

iment II, illustrated in Figure 17. The large increase in

indexing time is attributed to the large document size

of dataset II (books) as compared to the document

size of dataset I (papers). This difference is not pre-

sent in the searching time due to the scalable nature of

the B+-Trees of Lucene regarding retrieval.

Figure 19: Indexing time for dataset II.

Figure 20: Searching time for dataset II.

EnhancingtheResultsofRecommenderSystemsusingImplicitOntologyRelations

5 CONCLUSIONS

Our main contribution in this work is integrating

ontological concepts into the recommendation pro-

cess. We extend the information retrieval part of

multi-agent recommender system KARe by allowing

the definition of simple ontological relations.

The simple and implicit ontological relations, such

as sibling, parent/child and related-to relations,are

presented as data properties in the OWL file. Saving

those concepts during the indexing process and us-

ing them in the searching process gives additional

information to support the search and retrieval of

better concepts. Instead of increasing the results with

more concepts, we focus on keeping the same num-

ber of concepts constant while improving their rele-

vance which prevents the precision value from de-

creasing.

We assess the performance of our proposed sys-

tem on two datasets to measure the recall, precision

and F1-Measure. The results show good improve-

ment in recall and precision. We also measure the

indexing and searching time to see the effect of add-

ing related concepts. The results show that adding

ontology relations have a slight increase of 10%

indexing and searching times.

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