Intelligent Information Retrieval and History of Web Search Engines

1. Intelligent Information Retrieval and History
of Web Search Engines
Ibrahim Ramadan, Monier Shokry
#
Faculty of computer and information-Computer Science, Cairo University
Cairo, Egypt
Abstract: Web intelligence combines the interaction of the
human mind and artificial intelligence with networks and technology.
This paper attempts to define and summarise the concept of web
intelligence, highlight the key elements of web intelligence, and
explore the topic of web information retrieval with particular focus on
multimedia/information retrieval and intelligent agents. [1]
Keywords: Web intelligence, multimedia information
retrieval, intelligent agent, artificial intelligence, information
technology, human computer interaction, WWW .
I. INTRODUCTION
The concept of 'intelligent' information retrieval was first
mooted in the late 1970s, but had lost currency within the
information retrieval Community by at least the early 1990s.
With the popularity of the concept of 'intelligent agents', it
appears that the idea of intelligent information retrieval is
again in general vogue. In this paper, I attempt to show that
the naive concept of intelligent information retrieval, based on
the idea of agency, misses the essence of intelligence in the
information retrieval System, and will inevitably lead to
dysfunctional information retrieval. As a counter-proposal, I
suggest that true intelligence in information retrieval resides in
appropriate allocation of responsibility amongst all the actors
in the information retrieval System, and that intelligent
information retrieval will be achieved through effective
support of people in their various interactions with information
[2]
II. OVERVIEW OF BACKGROUND TOPICS
A) Information Hierarchy
Fig. 1 Data, Information, Knowledge, and Wisdom
B) Information Overload
The greatest problem of today is how to teach people to
ignore the irrelevant, how to refuse to know things, before
they are suffocated. For too many facts are as bad as none
at all. (W.H. Auden)
C) Information Retrieval
Information Retrieval (IR) is finding material (usually
documents) of an unstructured nature (usually text) that
satisfies an information need from within large collections
(usually stored on computers). Most prominent example:
Web Search Engines
D) Search Engine Early History
The search industry, and SEO as a whole, has come a long
way from the days of link bait tactics and keyword stuffing,
developing at an unparalleled rate. While old school SEO
focused solely on keywords and targeted search engines, not
users, modern search engine optimization has become much
more sophisticated and integrated.
New school SEO recognizes the importance of quality
over quantity, especially in term of links. It works in
conjunction with other channels, focuses on long tail
keywords and conversation phrases, and focuses on the user
experience. Content is targeted towards a specific audience,
with the goal of user engagement.[1]
Take a look at Media Vision’s “Evolution of SEO”
infographic, charting the significant industry
developments over the past decade. From Google's
algorithm updates to increasingly sophisticated search
engine capabilities, the industry continues to evolve
providing users with the most accurate search results
possible while also ensuring information visibility in the
ever-growing clutter of online content.[7]
 Data
The raw material of
information
 Information
Data organized and
presented by someone
 Knowledge
Information read, heard
or seen and understood
 Wisdom
Distilled and integrated
knowledge and
understanding

2. Fig. 3 the History of Search Engine Optimization 1994 – 2014[8]
III.INFORMATION RETRIEVAL AS A PROCESS
1.Text Representation (Indexing)
Given a text document, identify the concepts that
describe the content and how well they describe it
2.Representing Information Need (Query Formulation)
Describe and refine info. Needs as explicit queries
3.Comparing Representations (Retrieval)
Compare text and query representations to determine
which documents are potentially relevant
4.Evaluating Retrieved Text (Feedback)
Present documents to user and modify query based on
feedback, as shown in the fig. 4.

3. A) Keyword Search
Simplest notion of relevance is that the query string
appears verbatim in the document.
Slightly less strict notion is that the words in the query
appear frequently in the document, in any order (bag of
words).[13]
B) Problems with Keywords
May not retrieve relevant documents that include
synonymous terms.
– “restaurant” vs. “café”
– “PRC” vs. “China”
May retrieve irrelevant documents that include
ambiguous terms.
– “bat” (baseball vs. mammal)
– “Apple” (company vs. fruit)
– “bit” (unit of data vs. act of eating)
Fig. 4 Information Retrieval as a Process
IV.INFORMATION SYSTEM EVALUATION
IR systems are often components of larger systems
Might evaluate several aspects:
– assistance in formulating queries
– speed of retrieval
– resources required
– presentation of documents
– ability to find relevant documents
Evaluation is generally comparative
– system A vs. system B, etc.
Most common evaluation: retrieval effectiveness.
A) Evaluating Effectiveness
Effectiveness of retrieval depends on the “relevance” of
the documents retrieved.
Effectiveness is often measured in terms of “recall” and
“precision”.
Recall
Proportion of relevant material actually retrieved
Precision
Proportion of retrieved material actually relevant
B) Relevance
Relevance is difficult to define precisely,A relevant
document is “judged” useful in context of a query[11]
– Who judges?
– What is useful?
– Humans not very consistent
Judgements depend on more than the document and the
query with real collections, never know full set of relevant
documents, any retrieval model includes and implicit
definition of relevance, e.g.
– distance metrics
– P(relevance | query, document)
C) Retrieved vs. Relevant Documents
Fig. 5 Retrieved vs. Relevant Documents
D) Precision/Recall Curves
There is a trade-off between Precision and Recall, So
measure Precision at different levels of Recall[12]
Fig. 5 Precision/Recall Curve





 

|Rel|
|RelRet|
Recall

4. V. CONCLUSIONS
In the World Wide Web (WWW) there are information
resources available and for these resources it provides an IIR
systems and tools which are used to find more relevant
information according to user’s interests. We began our
discussion of IIR system and its need, searching mechanism,
drawback of keyword search. In the semantic web and ontology
systems that provides a solution in the World Wide Web
discovery and organizing information, so our paper present a
survey on the semantic web oriented system and models. These
systems are representing and storing the information Resource
Description Framework, Resource Description Framework
Schema, Ontology Web Language. [6],[1]
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