2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore,
INDIA
A Comparative Study of Semantic Search Systems
Dr. Chetana Gavankar
chetana.gavankar@pilani.bits-pilani.ac.in
Taniya Bhosale
taniya.bhosale0@gmail.com
Anindita Chavan
dhanashreegunda98@gmail.com
Shafa Hassan
aninditachavan@gmail.com
shafa.hassan112@gmail.com
individuals. Semantic web, instead of word matching, will be able to
show related items thus showing new relationships. Todays search
involves fetching documents that match the given words and phrases.
But with semantic web, instead of performing word matching, it will be
able to show how different things relate to each other. We have come
across two types of semantic search systems so far, semantic search
engine using semantic resources to improve search results and search
systems that search across semantic resources [2], [3], [4], [5], [6]. Of
these, the semantic search engines are the ones that seek to understand
the query given by the user and gives relevant results. Example- Hakia,
Duckduckgo, Lexxe. They go beyond keyword matching and try to find
meaning in the queries. Whereas searching across semantic web
resources is the kind of technique where the query is searched across
ontologies in OWL and RDF formats. Example- Swoogle, Watson,
Bioportal, Falcons. In this paper, Section II explains today’s web and
it’s drawbacks. Section IIIgives a brief understanding of existing
Semantic search engines and search engines based on Semantic Data. It
explains the working methodologies, features and few of the
drawbacks. Section IV concludes the review on the various search
engines.
Abstract— Today’s internet consists of mostly unstructured
data, most of it being unusable for average users. With increase in
the number of smart devices that are getting access to the web, we
have a large set of unlinked data that is not able to communicate.
Indirectly, it can be said that the Web is broken. Semantic Web
focuses on making the meaning explicit instead of fetching results
with the help of word matching. Semantic Web is an extension to
the current Web that provides an easier way to find, share, reuse
and combine information. In this paper, we are presenting an
analysis of the different approaches taken by various semantic web
search engines and the comparison between them, thus identifying
the advantages and limitation of each search engine.
I. INTRODUCTION
The Semantic Web is an enhancement to the existing web which
focuses on giving a well defined meaning to the information, helping
computers and people to work together in liaison. One of the major
challenges of presenting information on the web was that web
applications were not able to provide context to the data and therefore
could not differentiate between relevant and irrelevant information.
According to Tim Berners Lee, [1] the Semantic Web is not a separate
Web but an extension of the current one, in which information is given
well-defined meaning, better enabling computers and people to work in
cooperation. The Semantic Web will bring structure to the meaningful
content of Web pages, creating an environment where software agents
roaming from page to page can readily carry out sophisticated tasks for
users. Semantic Web involves two terms: Semantic Markup and Web
Services. A web service is a software system which is designed to
support interaction between computers over the internet. Semantic
markup refers to the communication gap between web users and
computer-ized applications. Semantic web will involve a collaboration
of both semantic markup as well as web services thus giving various
applications a prospective to communicate with other applications and
perform broader searches for information through simpler interfaces.
The present web can be characterized as the syntactic web where data
fetching and presentation is done by the machines, and filtering and
identifying important information is designated to the
978-1-7281-4514-3/20/$31.00 c 2020 IEEE
Dhanashree Gunda
II. TODAY’S WEB AND IT’S DRAWBACKS
Today, the Web provides us a platform through which information
can be shared easily and everyone has the ability to write websites.
HTML is used for programming the information or structuring of a
Web page and connecting to other Web pages or resources with the
help of hyper-links. A combined result of all of this is that the Web is
expanding at a very fast pace. However, majority of the web pages are
designed in such a way that they can be interpreted by the humans
alone and cannot be processed by the machines. While fetching data,
computers perform the job of site scraping which is decoding the colour
schema and links that are encoded within the Web pages. The search
engines lack the ability to actually interpret the result and then present
it to the user. With increase of data on the Web,this situation is getting
worse day by day. Most users only go through the top results provided
by the search engines thus, neglecting the later ones. As the size of
search results goes on increasing progressively, it has become hard for
humans to interpret such huge amounts of information making the task
of finding relevant information on the Web more difficult than desired.
1
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020,
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The conclusion is that the Web has developed as a platform for
information exchange between the people instead of the machines. The
meaning, i.e. the semantic content of the Web page is encoded in such a
way that it is useful by human intervention and interpretation only.
used in search engine optimization as it uses a tool to sift through all
the HTML code and various scripts of a page in order to determine all
the links present and whether they are active or dead. This information
allows the analyst to determine whether the search engine has the
ability to find and index a particular website. In OntoRank, two
concepts are considered as reference relationship if and only if a
relationship exists between the two classes in a relation set. The
reference relations are directional and transitive. With more transitive
steps, the reference relationship is weakened gradually. The link
strength between two ontologies depends on the number of interreference concepts among ontology sets and reference strength. The
main challenge behind using link analyze method to compute ontology
importance is the lack of explicit links among ontologies, which results
in the difference in ontology and web page, and demands the searching
of implicit relationships between ontologies and hence has interoperability issues. Thus, the drawback of OntoRank algorithm is that it
evaluates the rank of an ontology statistically and does not take into
consideration the user query as an effective factor for ranking the
results.[10] An example of Swoogle search is shown below in Figure 1.
A. Limitations of Today’s Web
1) The huge amount of results that the search engines list out have
a very low recall accuracy. For example, if the user has to search
for the web pages where ”nuclear” and ”science” occur, the
resulting information would be of very little use and the user
will be overwhelmed with huge amount of results. Also this may
not be even relevant to the search request.
2) The results that are fetched by the search engines are vocabulary
sensitive. For example, a particular user wants to search for
”TCP/IP protocol”; and there are some useful web pages that
have the word ’standard’ instead of protocol. Therefore, the
Web pages that have been listed as a result of the search
performed will not be the best match for a search that has been
made with the use of keyword protocol.
3) The search results yields us a list of Web pages containing
relevant information. It often happens that multiple entries are
present for the same Web Page. Thus, if relevant information is
getting distributed in more that one result, it would be very
difficult to determine the set of all relevant entries.
These shortcomings of todays Web poses the need of a Web where
more focus is given on the semantics of the data. This would help
establish new relationships among entities and yield precise results for
queries that are even more complex that the ones we use today.
III. SEARCH ENGINES
A. Search across Semantic Resources
1) SWOOGLE:1Swoogle[7] is a semantic web search en-gine
that searches for Web documents in RDF and OWL.[8] Swoogle
performs indexing using a web crawler with which the search engine
crawls through one page at a time, until all the pages have been
indexed. This helps in collecting information about a web page like it’s
metadata using which the search engine computes relationships
between documents. Web Crawler also gives Swoogle the ability to
keep track of URLs which have already been downloaded to avoid
downloading the same page again. Swoogle not only identifies
ontologies according to user specific search queries but also uses
OntoRank algorithm to rank these ontologies on the basis of their
popularity.[9] Identical to the PageRank algorithm, OntoRank also
makes use of link analysis for evaluation of ontologies. Link analysis is
a popular tech-nique used in data analysis to evaluate the relationships
or connections between various types of objects. It is a kind of
knowledge discovery which helps in better analysis especially in
context of links. Link analysis is commonly
1
Fig. 1.
SWOOGLE
2) BIOPORTAL:Bioportal2 is an ontology search engine
[2] which has been developed by the National Center for Bio-medical
Ontology. It serves as a repository for Biomedical Ontologies.
Bioportal defines relationships among different domains of existing
ontologies and also between the on-tologies. The Open Biomedical
Resources(OBR) component automatically indexes the online
biomedical data sets. The data sets are indexed on the basis of metadata
annotations. It links the data sets to the terms in the ontologies. This
helps in establishing semantic relationships among the entities and
mapping of ontologies. Ontologies in Bioportal are repre-sented in
different semantic web languages like Web Ontol-ogy
Language(OWL), Open Biological and Biomedical On-tologies(OBO)
and Resourse Description Framework(RDF). Mayo Clinics Lex Grid
system is used to store ontologies in OBO Format and to access
standard biomedical termi-nologies. Protege frame language is the back
end for OWL
http://swoogle.umbc.edu/2006/
2
https://bioportal.bioontology.org/
2
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020,
Coimbatore, INDIA
and RDF ontologies. One of the key features of Bioportal is that the
users can browse, search, download and update the existing
ontologies.[2] They can also upload their own ontologies, add notes
and edit ontologies according to the comments and also make
suggestions to the ontology devel-opers. Users can also browse, create
and upload mappings between the ontologies. In this way they can
actively con-tribute to bioportal which increases its value and this
feature distinguishes bioportal from other ontology repositories. At
present, Bioportal has around 773 ontologies and 9,118,651 classes.
Bioportal displays the ontology class hierarchy in a tree structure and
also has different visualization methods for showing links between
different classes. A snapshot of heart failure ontology is shown below
in Figure 2.
the functionality of searching in between and among the ontologies and
semantic documents, retrieving metadata and metrics on ontologies,
entities and reuse of ontologies. The architecture of Watson is shown in
Figure 3.
Fig. 3.
WATSON ARCHITECTURE
4) FALCONS: Falcons [11] is used for semantic search over the
web. It has been developed mainly for concept sharing and ontology
reusing. It is an ontology search based on keywords that returns the
concepts/ontologies whose textual description matches with the terms
given in the keyword query. It ranks the results based on the relevance
of the query and popularity of the concepts. The popularity of a concept
is measured depending on a large set of data which is collected from
the Semantic Web. In Falcons, each concept
Fig. 2.
BIOPORTAL
3) WATSON:Watson[3] is a search engine which collects,
analyses and gives access to ontologies. It is a search engine which
works on specific type of documents. It provides numerous
functionalities to applications with a set of API’s. It finds, explores and
locates semantic documents. Watson performs three functionalities:[3]
1) Collects the semantic data on the Web.
2) Implements different query methods to access the data.
3) Analyses the data to extract useful metadata and in-dexes.
The semantic documents are located through a tracking and crawling
component called Heritrix. The validation and analysis component
indexes the documents using the Apache Lucene Indexing technology.
The crawler also explores new repositories to locate documents written
in ontology lan-guages. The collected semantic documents are recrawled in order to find evolutions of known semantic data or new
elements. They are then filtered and only those documents whose
content characterizes the semantic web are kept. The documents which
cannot be parsed by the crawler are eliminated by Jena. All the
documents in the form of RDF are extracted except the documents in
RSS. Watson also ex-tracts metadata from the collected ontologies.
Two ontologies which are different in nature can have the same URI.
To re-solve this issue, Watson uses internal identifiers which differ
from the URI’s of collected semantic documents. Watson can be used
for querying complex keywords using SPARQL. It summarizes the
entire description of the ontologies so that it is easily understandable by
the users. Watson provides
Fig. 4.
Falcons Search Engine
returned as a result is associated with a query relevant snippet giving an
idea of how the concept is matched with the given question and also
briefly gives its meaning. Also, the system recommends to the user,
numerous other query related popular ontologies that can be used by
the user to restrict the results to a specific ontology. Thus, Falcons is a
search engine parsing through the semantic data and giving results in
the form of concepts along with their context as structured snippets.
Along with that, Falcons also provides a detailed RDF description of
each concept and a summary of each ontology on demand. As shown in
the figure 4, the query ’student university’ will return results in the
form of ontologies. The lower area of the page gives the different
concepts returned. For every concept the first line gives its name and
type. It also gives an option for clicking on the name to browse its RDF
description in detail. After that we can see a structured snippet that
shows which part of the
3
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RDF description of the concept matches with the terms in the keyword
query. After the snippet, the URI is given followed by a number that
signifies in how many RDF documents this concept is mentioned and
with links to further browse these documents if needed. The upper part
of the result page gives ontologies related to the query and the user can
choose to further restrict the search to these ontologies by selecting
them.
, the engine understands how the words are formed as phrases so that
the relevant information is extracted. The technologies used include
Part-of-speech Tagging, Parsing and word sense disambiguation. Each
semantic key acts as a category for a set of keywords. For example, the
semantic key ”Ferrari colour” refers to any number of colours. The
ability to use semantic keys is of very little use when the user does not
know the exact terms he is looking for. In the Ferrari example, Lexxe
provides statistics of the most popular colours of Ferrari along with
their percentages. By replacing the semantic key ”colour” with ”price”
provides user with the information about the cost estimation. Lexxe is
currently not in use. The snapshot of Lexxe search engine is shown
below in Figure 6.
B. Semantic Search Engines
1) HAKIA: Hakia 3 is a search engine [12] that provides results
based on meaning matching. It uses Natural Language processing for
returning the search results. Hakia searches within structured text. It is
a private search engine that is designed to provide search results based
on the meaning of the content rather than on the basis of page
popularity. Hakia uses QDEX for query detection and extracting
inverted text index. Ontosem is Hakia’s linguistic database repository
in which words are categorized based on different meanings. It makes
use of natural language processing to access full meaning of the text it
handles. It is composed of a set of language independent ontologies
consisting of thousands of interrelated concepts. A snapshot of Hakia
search is shown in Figure 5.
Fig. 6. LEXXE
3) SENSEBOT:5Sensebot[14] is a new kind of search engine in
which a search query gives out results in the form of a text summary
instead of a collection of web links. This summary works as a
condensation on the topic of your search query, merging together the
most important and pertinent aspects of the search results. The parsing
of Web pages is done using text mining that identifies the key concepts
inside the web page. This is followed by a multi-document
summarizing on the content to produce a coherent summary. For
instance, take the example of the term dot-com-bubble. As you can see
in the figure 7, We type ”dot-com bubble” into Google and receive
plenty of links with some context around them. But looking at all those
links does not give a clear idea as to what exactly dot-com-bubble is.
And so we start by clicking on each link one by one. However, some
pages assume that we know the basics of the topic while others are too
shallow, biased or just links to other resources instead of giving an
accurate answer to our question. By the time we comprehend what
’dot-com bubble’ is, we have tapped on an excessive number of links
and filtered through an excessive amount of repetitive data. SenseBot
generates a short abstract of the top pages returned by the engine.
Reading this summary will give us a good idea as to what ’dot-com
bubble’ is and understand all the basics of it, and then we can decide if
we want to further go into the details of it. SenseBot will skip the
sources that are not extremely helpful on the topic applicable to our
subject, regardless of
Fig. 5. HAKIA
2) LEXXE:4On the basis of the requirement space, Semantic
Search Engines are classified into four categories
: Search Environment, Intrinsic Problems and Iterative and
Exploratory, Search requirement and Query Type. Lexxe[13] comes
under the Query Type category. It is a search engine that uses natural
language processing on its semantic search technology. It was designed
to answer short queries by gathering content from the unstructured data
on the internet. The user can specify different parameters in the form of
questions and then Lexxe automatically extracts expected results from
the web pages. Hence, Lexxe can answer wide range of questions.
Lexxe does not work like a traditional search engine which presents us
the answers from manually prepared databases. Instead, it categorizes
the results into three parts: answers, clusters and web page snippets.
The fetching part uses computational linguistics to eliminate ir-relevant
content. With the help of phrase recognition method
3
http://www.hakia.com/
4
http://www.lexxe.com/
5
http://www.sensebot.com/
4
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how they were ranked by the search engines. Usually, any kind of
search where the client is trying to comprehend a concept or a specific
area of learning; or get a digest on an individual, an occasion, or an
activity; or rapidly recognize the best source of data on the topic, will
profit greatly from SenseBot. Greater the diversity of the views on the
subject
, higher would be the advantage of seeing their summary instead of
concentrating on each view independently.
3)
4)
5)
6)
Calculation detection.
Does not save IP addresses of the users.
It fetches the same result irrespective of the user.
Keeps user’s search keywords private from the web-sites that
are visited as a result of the search.
7) It operates on the data compiled from more than 400 sources
such as Bing, Yahoo and also it’s own Web crawler known as
DuckDuckBot.
8) Avails the use of cookies when required.
9) Makes use of a variety of open source technologies to provide
personalized search results.
5) EXALEAD: 7Exalead offers functionalities such as searching
within the results, proximity search, regular ex-pressions and phonetic
search. The search engine provides access to over 8 billion Web pages
and 1 billion online images. Exalead also offers the functionality of
image search-ing which is useful for comparing and classifying those
images. It helps users to limit the image search results by providing a
combination of LTU software and Exalead. On each keyword input,
Exalead fetches a set of images corresponding to the input. The face
refinement functionality offered by LTU technologies restricts the
search results to only those images that represent faces or portraits.
Exalead image search is comprised of two modules: Image DNA
Generator and Semantic Description Generator. The image DNA
generator module creates a numerical vector called as DNA that
encodes information related to the image such as colour, shape, texture,
scale, object translation, image quality. The semantic description
generator classifies the DNA image on the basis of pattern recognition
as against the knowledge base that uses state of art techniques modeled
on behavior of human subjects. The analyzer and the describer have the
capability of learning thus providing an enhanced search experience.
Exalead also helps the user to narrow down the image search by
allowing the user to search on the basis of image size. The developers
have now added a new feature that is video search. An example of
Exalead search for Elvis Presley is shown below in Figure 9.
Fig. 7. SENSEBOT
4) DUCKDUCKGO:6DuckDuckGo[15] is a new search engine
focused on relevant results and respecting user pri-vacy. DuckDuckGo
is a Semantic Web search search engine that is characterized by its
feature rich semantic search. If a search is made for a word having
multiple meanings, Duck-DuckGo gives it’s users the functionality of
choosing from various options with its disambiguation results.
Example: If we search for the term Apple then the search engine will
list down all the possible contexts of the word Apple thus giving the
user an option to search according to his preference. Duckduckgo
search for the word ’bank’ is shown in Figure 8.
Fig. 8.
DUCKDUCKGO
Fig. 9.
EXALEAD
Following are the advantages of DuckDuckGo:
1) Keyboard shortcuts.
2) Customization.
6
7
https://duckduckgo.com/
http://www.exalead.com/search/
5
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2020 International Conference on Computer Communication and Informatics (ICCCI -2020), Jan. 22-24, 2020, Coimbatore,
INDIA
[15]
TABLE I
COMPARATIVE STUDY OF SEARCH ENGINES
Name of Engine
Hakia
Search
Methodology
Pure
analysis of
contents
Features
Working logic
Advantages
Drawbacks
Excellent resumes, CMR,
Semantic rank algorithm,
Related searches.
Swoogle
Indexes
documents
using RDF.
Uses REST Interface to
provide different services.
Searches structured
text
with the help of QDEX,
Ontosem and
semantic
rank algorithm.
Gives Semantic
web
search results in
RDF,
OWL using web ontology.
Gathers relevant information for the given query
from various credible sites
easily.
Finds
appropriate ontologies, instance data structure of the semantic web.
Lexxe
Uses
Semantic
key
technology.
Users can query
with a conceptual
keyword.
Uses text mining,
summarizes multiple records.
A meta
search
engine
that
collects
information
from
different
search engines.
Classification
and
Categorization.
Parsing, Word
sense
disambiguation,Part-ofSpeech tagging.
Selects
link from Subset
Cluster, Natural Language
Processing
Blends together the significant and relevant aspects
of search results.
Focuses
on
privacy,
doesn’t
track
user’s
personal
information,
uses its own web crawler
as well as other search
engines.
Image recognition
using
DNA generator and Semantic description
generator.
Parses through the links
to give a summary of the
relevant data.
It gives results in the form
of summary and uses local
search.
Uses computational linguistics to preclude irrelevant content. Clustering of
results yields options
on
various contexts.
Gives summary of results
instead of providing links
related to the query.
Results are gathered from
different sources like Yahoo, Wikipedia etc. Hence
more reliable.
Cannot operate on unstructured data, still ambiguous with some of the
search queries.
Not multilingual. Extending Swoogle to index
and effectively query large
amounts of instance data
is still a challenge.
Does not work well with
long queries.
Sensebot
Duckduckgo
Exalead
Easily narrows down image search results with integration of software between LTU and Exalead.
IV. CONCLUSION
This paper provides a brief overview of the existing literature
regarding intelligent semantic search technologies. We have reviewed
their characteristics respectively and the conclusion derived is that the
existing techniques have a few drawbacks particularly in terms of time
response, accuracy of results, importance of results and relevancy of
results. An efficient semantic web search engines should meet these
challenges efficiently and be compatible with global stan-dards of web
technology.
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