Introduction to Semantic Search Engine

Abstract

Search engine is the most important tool to discover the any information in World Wide Web. In a row with the terrific growth numbers of the Web, traditional search engine nowadays is not appropriate anymore to be used. Searched by keyword and do not understand polysemy and synonymy are some reasons the traditional search engine is not suitable anymore. Semantic search engine is born of traditional search engine overcome the problem below. This paper will discuss about semantic search engine and the traditional search engine.

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2009 International Conference on Electrical Engineering and Informatics
5-7 August 2009, Selangor, Malaysia
978-1-4244-4913-2/09/$25.00 ©2009 IEEE
IS-18
Introduction to Semantic Search Engine

Junaidah Mohamed Kassim and Mahathir Rahmany
Faculty of Technology and Information Science
University Kebangsaan Malaysia
junaidah@ftsm.ukm.my and Mahathir.rahmany@yahoo.com

Abstract — Search engine is the most important tool to discover
the any information in World Wide Web. In a row with the
terrific growth numbers of the web, traditional search engine
nowadays is not appropriate anymore to be used. Searched by
keyword and do not understand polysemy and synonymy are
some reasons the traditional search engine is not suitable
anymore. Semantic Search Engine is born of traditional search
engine overcome the problem below. This paper will discuss
about semantic search engine and the traditional search engine.
Keywords: Semantic Search Engine, Information Retrieval.

I. INTRODUCTION

Search engine has become a primary need to explore
the internet. Without Search Engine, there are no uses of
information in website, blog, etc; because without search
engine, it is almost impossible to look for one by one
website just for search information in internet.
In a row with the extraordinary growth of web, there
are many search engines come out to help the users on
finding their need, but search engines find it increasingly
difficult to provide useful results. To manage this
explosively large number of web documents, automatic
clustering of documents and organising them into domain
dependent directories became very popular. [1]
The terrific increment of the web has made the
evolution of the web itself. From web 1.0 (first generation
of internet – 1990 – 2000), web 2.0 and now has become to
web 3.0.
Web 1.0 refers to Internet at its emerging stage, with
corporate and institutional websites occupying 90% of the
cyber space, with an one-way mode. Thus Internet access
serves functional purpose, and people could also read and
extract information from the websites. Internet access was
achieved typically through telephone dial-up at that time
[2]. “Web 2.0” is transforming the Web into a space that
allows anyone to create and share information online—a
space for collaboration, conversation, and interaction; a
space that is highly dynamic, flexible, and adaptable [3].
Web 3.0 is one of the terms used to describe the
evolutionary stage of the Web that follows Web 2.0. Given
that technical and social possibilities identified in this latter
term are yet to be fully realized the nature of defining Web
3.0 is highly speculative. In general it refers to aspects of
the Internet which, though potentially possible, are not
technically or practically feasible at this time. [4]. We can
see a differences between web 2.0 and 3.0 in figure 2.
From the figure 1, it shown that web 1.0 is a one-way
platform, web 2.0 is a two-way platform where


Figure 1: Evolution in web 1.0, web 2.0 and web 3.0 [5]

participation is a key-word. While the Web 3.0 shows more
intelligences: the "web machine" learns, suggests and
anticipates what people like and would like to get [5].




II. LITERATURE REVIEW
A. Information Retrieval
Information retrieval (IR) is finding material (usually
documents) of an unstructured nature (usually text) that

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satisfies an information need from within large collections
(usually stored on computers). [7]

Figure 2: Differences between web 2.0 and web 3.0 [6]

Web 2.0
Main task Focus the power of
community to create
dynamic contents and
interaction technology
Linking Walled gardens
inhibit interoperability
Web 3.0
Linked data,
devices and
people across the
web
Data and devices
linked more
easily and in new
ways
Individual,
organization,
machine create
content which can
be reused
Resource
Description
framework (RDF)
Dbpedia, sioc-
project.org
Content Individual and
organization create
content
Technology AJAX
Website Google, facebook,
wikipedia, ebay,
youtube

The aim of an information retrieval system is to find
relevant documents thus relevance is a (if not ‘the’) central
concept of information retrieval. [8]
From the figure 1 show how an information retrieval
system work in generally. It starts with general information
or query from the user. Then the query will be extracted to
be metadata. This process usually is called creating
representation.
In the other hand, corpus (corpus is a collection of one
or more documents, typically related, and available to an
information retrieval system) is also extracted from
formatted document to full text index. This process is done
by several steps, such as Remove properties and
formatting, parser, stopword removal, stemming and
Synonym matching. That full text index will be saved in
index objects database.
Next step is performed comparison between user query
and index object to retrieve objects. Then last, Evaluate the
result or refine search if the result is not hesitated.

B. Search Engine
Search engine is one of the most famous tools to
discover the information in internet. General purpose
search engines have typically used exhaustive crawlers to
build and update large collections of documents. [10]
All search engines consist of three parts: (1) a
database of web documents, (2) a search engine operating
on that database, and (3) a series of programs that
determine how search results are displayed [11-6]. Part of
the search engines’ success might be due to their
simplicity: you enter some words and the results are then
output in form of a ranked list, in which the search engine
estimates the relevance of each indexed website to your
query. [12]




























Fig. 3 An information retrieval system [9]

The utility of any search engine depends on two
parts: the quality of the system, and content, which in our
case is provided by a large number of contributors
(personal and corporate web sites, for example).
Importantly, content suppliers have to agree on a social
contract (as anywhere on the Internet) on how to provide
and publish data [13].
Now days, as one of essential tool in internet,
search engine has many types, such as Crawler-Based
Search Engines, Directories, Hybrid Search Engines, Meta
Search Engines and Specialty Search Engines.
Crawler-based search engines use automated
software programs to survey and categorise web pages.
The programs used by the search engines to access your
web pages are called ‘spiders’, ‘crawlers’, ‘robots’ or
‘bots’. A ‘directory’ uses human editors who decide what
category the site belongs to; they place websites within
specific categories in the ‘directories’ database. Meta
search engines take the results from all the other search
engines results, and combine them into one large listing;
and Specialty search engines is the search engine for

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searching in niche areas, such as shopping, local search,
etc. [14]
There are several components in search engine,
such as crawler, indexer, sorter, analyser, and searcher.
Crawler is a program that is created to visit each
webpage periodically and
information and store them into database. The Web
Crawler Application is divided into three main modules.

a. Controller Module - This module focuses on the
Graphical User Interface (GUI) designed for the web
crawler and is responsible for controlling the
operations of the crawler. The GUI enables the user
to enter the start URL, enter the maximum number of
URL’s to crawl, view the URL’s that are being
fetched. It controls the Fetcher and Parser.
b. Fetcher Module - This module starts by fetching the
page according to the start URL specified by the
user. The fetcher module also retrieves all the links
in a particular page and continues doing that until the
maximum number of URL’s is reached.
c. Parser Module - This module parses the URL’s
fetched by the Fetcher module and saves the contents
of those pages to the disk. [15]
After that indexer create index in the database to
organize the data by categorize them. The indexer extracts
all the information from each and every document and
stores it in a database. All high-quality search engines
index each and every word in the documents and give a
unique word Id. Then the word occurrences, which some
search engines call “hits,” are checked, recording all the
words, including their placement in the document, their
font size and capitalization. [16]

C. Directory
A directory is “human-powered”. Humans select
sites that will be listed. Directories list sites under
categories and subcategories, becoming more specific at
each level. Directories include a fraction of the sites
available via a search engine. Many search engines now
include a directory feature. Portals are directories for very
specific topics and audiences [17].
Web Directories can be real time savers. Why go
through the trouble of searching and then scrolling through
several pages of links when someone else has already
selected the best links on a subject for you? [18].
Every Open Directory Project (ODP) directory has
an associated URL, which contains a description of the
directory and a number of Web sites that have been
manually listed as pertaining to the directory topic,
accompanied by brief descriptions of each site. This
information is completed with a list of subdirectories, each
containing more Web sites and subdirectories. Finally,
some directories also have pointers to the same category in
other languages [19].
collect the important
One of the famous directory is DMOZ (Directory
Mozilla). In 1998, Rich Skrenta began the volunteer-run
GnuHoo (name inspired by GNU freeware group), which
would later become the ODP. On June 5, 1998 GnuHoo
went live and within two weeks “there were 200 editors,
27,000 sites, 2,000 categories”. Within five months the site
garnered the attention of Netscape and was acquired for
their Netcenter site. However, the site’s name had now
been changed from GnuHoo to NewHoo due to naming
conflicts with the freeware group called GNU who was in
the process of developing a free UNIX inspired operating
system. Soon the name changed one final time, becoming
the Open Directory Project [20]. Now DMOZ has
4,598,551 sites - 82,103 editors - over 590,000 categories.

D. Semantic Search Engine
Differently from traditional search engines, a
semantic search engine stores semantic information about
Web resources and is able to solve complex queries,
considering as well the context where the Web resource is
targeted. [21]
Semantic search integrates the technologies of
Semantic Web and search engine to improve the search
results gained by current search engines and evolves to
next generation of search engines built on Semantic Web.
[22]
In general, processes of semantic search engine are:
(1) The user question is interpreted, extracting the relevant
concepts from the sentence, (2) that set of concepts is used
to build a query that is launched against the ontology, and
(3) The results are presented to the user. [23]

E. Ontology
According to T. R. Gruber, an ontology can be
defined as an explicit specification of a conceptualization
[24]
The term of ontology is come from philosophy,
that is “the science of what is, of the kinds and structures of
objects, properties, events, processes and relations in every
area of reality”. [25]
In the internet, we can see the example of the
ontology, such as Yahoo! Categories, DMOZ Directory,
Amozon.com product catalogue, WordNet, GO (Gene
Ontology) (www.geneontology.org), Unified Medical
Language System (UMLS) and UNSPSC - terminology for
products and services.
Kinds of ontology that are various commonly used
are (a) Terminological ontologies where concepts are word
senses and instances are words, (b) Topic ontologies where
concepts are topics and instances are documents, and (c)
Data-model ontologies where concepts are tables in a data
base and instances are data records (such as in a database
schema). (26)
There are several reasons why someone wants to
develop ontology, such as to share common understanding
of the structure of information among people or software

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agents, to enable reuse of domain knowledge, to make
domain assumption explicit, to separate domain knowledge
from the operational knowledge and to analyze domain
knowledge. [27]
Ontology describes knowledge about the domain in
terms of concepts or vocabularies within the domain and
relationships between them.
Ontology is needed to develop semantic search
engine. By using semantically richer ontologies the
following benefits can be obtained. Firstly, ontologies can
be used to describe the domain knowledge and the
terminology of the application in more detail. For example,
relations between categories in different views can be
defined. Secondly, ontologies can be used for creating
semantically more accurate annotations in terms of the
domain knowledge. Thirdly, with the help of ontologies,
the user can express the queries more precisely and
unambiguously, which leads to better precision and recall
rates. Fourthly, through ontological class definitions and
inference mechanisms, such as property inheritance,
instance-level metadata can be enriched semantically [28].

III. SEMANTIC SEARCH ENGINE

A. The Architecture of Semantic Search Engine
A typical semantic search engine should consist of
the following components: (1) Ontology development, (2)
Ontology Crawler, (3) Ontology Annotator, (4) Web
crawler, (5) Performing semantic search, (6) Query builder,
and (7) Query pre-processor. [29]
Some of the reason why someone want to develop an
ontology are (1) To share common understanding of the
structure of information among people or software agents,
(2) To enable reuse of domain knowledge, (3) To make
domain assumptions explicit, (4) To separate domain
knowledge from the operational knowledge, and (5) To
analyze domain knowledge. [30]
An ontology crawler can crawl through the Web
to find new ontologies and populate the database with
them. Note that the ontologies crawled by the ontology
crawler will not be directly dumped into the database.
Ontology translator, with the help of ontology mapper, will
translate them into the database tables. [31]
Ontological annotations
entities alongside properties and relations that characterize
the entities’ attributes and role in their textual context, with
respect to a reference ontology. [32]
Web crawler is an automatic program (sometimes
called a "robot") which explores the World Wide Web,
following the links and searching for information or
building a database - such programs are often used to build
automated indexes for the Web, allowing users to do
keyword searches for Web documents - it is also the name
used by one of the most popular publically available
keyword searching engines. [33]

identify real-world
(A) Ontologies are created in plain text format
(.OWL or .DAML). Ontology translator translates them
into relational database tables. Ontology Crawler finds new
ontologies on the Web and adds them to the ontology
library. (B) Users use Ontology Annotator to annotate their
Web pages with these ontologies and publish them on
Web. (C) Web Crawler crawls the Web to find Web pages
annotated with these ontologies and builds knowledge base
from the instances of these ontologies in these Web pages.
(D) Users construct search queries with the help of Query
Builder and these queries are sent to the Inference Engine
after pre-processing by Query Pre-processor. (E) Inference
Engine carries out reasoning on these search queries by
using ontology database and the knowledge base. The
results are finally sent to the user to be viewed on the Web.


Figure 4: Proposed layout of semantic search
engine

B. The Use of Semantic Search Engine
Semantic search attempts to improve the results of
research searches in two ways, a) traditional search results
take form of a list of a document/web pages and b) the
search phrase in research searches typically denotes one (or
occasionally two) real-world concepts. [34].
There are several advantages of semantic search,
such as
•
A semantic search makes it easier to locate
relevant information to the user's subject of
interest, saving the user a lot of time reading
unrelated Web pages. [35]
•
A semantic search engine can handle of long-tail
queries. Without relying on statistics, long-tail
queries can be analyzed by semantic algorithms

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on the fly, and bring search results with the
accurate context. [36]

C. Why Semantic Search Engine is Important
Finding what we need is often a hard job. Current
search engine technology is very good in finding complete
Web pages published all over the world, but it lacks the
desired precision and recall when searching for multimedia
resources. [37].
Semantic searches can overcome the aforementioned
limitations of keyword searches because they use an
ontology to infer information about objects. This enables a
semantic search system to correctly identify objects even
when the object's associated metadata does not explicitly
match the user's search criteria. The ability to infer
information based upon relationships encoded in the
ontology enabled users to identify objects which are
logically related. For example a database may contain
objects from multiple domains whereby researchers in each
discipline use there own terminology. To locate all desired
objects using a keyword search would require the user to
have knowledge of the relevant terminology used by
workers in each discipline. This type of information can be
encoded in an ontology and the semantic search system
could automatically retrieve the semantically related items.
[38]

IV. DISCUSSION

A. Traditional Search Engine vs. Semantic Search Engine
There are some differences between traditional search
engine and semantic search engine. The differences are:

Traditional Search Engine:
•
In engine prompt, you are entering keyword.
•
Do not understand polysemy and synonymy.
•
Unknowing meaning of the terms.
•
Do not take into account stop words such as a,
and, is, on, of, or, the, was, with, by, after, the.
•
When looking at a web page, a conventional
search engine looks for the distribution of words
within the web page to try and find how relevant it
is to the user's search query. Basically, this means
that a web page with similar words to those the
user types into a search engine will be thought to
be more relevant, and will appear at a higher
position in the search results page. [39]
•
Unable handle long-tail queries.

Semantic Search Engine:
•
In engine prompt, you are entering question.
•
Understand polysemy and synonymy.
•
Knowing meaning of the terms.
•
Take into account stop words such as a, and, is,
on, of, or, the, was, with, by, after, the.
•
Is designed to try and understand the context that
the words are used within the web page to try and
match it more accurately to the user's search
query. [40]
Able handle long-tail queries.
•

B. Comparison between Traditional Search Engines vs.
Semantic Search Engine.
For the comparison between Traditional Search Engines vs.
Semantic Search Engine, please see these two search
engines. Hakia (www.hakia.com) as a semantic search
engine and Dogpile (www.dogpile.com) as a traditional
search engine.
The keyword phrase that is used for this comparison
is “what is the weather in Kuala Lumpur”. See the result
below.

Figure 4: Hakia



Figure 5: Dogpile


knows what their user want. It is clear from the result that
showed by Hakia. They show the information of Kuala
Lumpur weather not the website that contain keyword of
Kuala Lumpur weather like Dogpile.
V. SUMMARY

From the figures below is showed that Hakia seem