MLIS Course Code 5501-Information Retrieval and Dissemination- Workshop AIOU 2013, Information Management, Information Retrieval and Dissemination, Information Retrieval, Information Dissemination, Workshop, AIOU, Computer Science, Information science, Information technology, Hardware, Software, Computer basics,
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
This 2-hour lecture was held at Amsterdam University of Applied Sciences (HvA) on October 16th, 2013. It represents a basic overview over core technologies used by ICT companies such as Google, Twitter or Facebook. The lecture does not require a strong technical background and stays at conceptual level.
An Introduction to Information Retrieval and Applicationssathish sak
An Introduction to Information Retrieval and Applications The score you get depends on the functions, difficulty and quality of your project
For system development:
System functions and correctness
For academic paper presentation
Quality and your presentation of the paper
Major methods/experimental results *must* be presented
Papers from top conferences are strongly suggested
E.g. SIGIR, WWW, CIKM, WSDM, JCDL, ICMR, …
Proposals are *required* for each team, and will be counted in the score
INTRODUCTION TO INFORMATION RETRIEVAL
This lecture will introduce the information retrieval problem, introduce the terminology related to IR, and provide a history of IR. In particular, the history of the web and its impact on IR will be discussed. Special attention and emphasis will be given to the concept of relevance in IR and the critical role it has played in the development of the subject. The lecture will end with a conceptual explanation of the IR process, and its relationships with other domains as well as current research developments.
INFORMATION RETRIEVAL MODELS
This lecture will present the models that have been used to rank documents according to their estimated relevance to user given queries, where the most relevant documents are shown ahead to those less relevant. Many of these models form the basis for many of the ranking algorithms used in many of past and today’s search applications. The lecture will describe models of IR such as Boolean retrieval, vector space, probabilistic retrieval, language models, and logical models. Relevance feedback, a technique that either implicitly or explicitly modifies user queries in light of their interaction with retrieval results, will also be discussed, as this is particularly relevant to web search and personalization.
MLIS Course Code 5501-Information Retrieval and Dissemination- Workshop AIOU 2013, Information Management, Information Retrieval and Dissemination, Information Retrieval, Information Dissemination, Workshop, AIOU, Computer Science, Information science, Information technology, Hardware, Software, Computer basics,
Broad introduction to information retrieval and web search, used to teaching at the Yahoo Bangalore Summer School 2013. Slides are a mash-up from my own and other people's presentations.
The diversity and complexity of contents available on the web have dramatically increased in recent years. Multimedia content such as images, videos, maps, voice recordings has been published more often than before. Document genres have also been diversified, for instance, news, blogs, FAQs, wiki. These diversified information sources are often dealt with in a separated way. For example, in web search, users have to switch between search verticals to access different sources. Recently, there has been a growing interest in finding effective ways to aggregate these information sources so that to hide the complexity of the information spaces to users searching for relevant information. For example, so-called aggregated search investigated by the major search engine companies will provide search results from several sources in a single result page. Aggregation itself is not a new paradigm; for instance, aggregate operators are common in database technology.
This talk presents the challenges faced by the like of web search engines and digital libraries in providing the means to aggregate information from several and complex information spaces in a way that helps users in their information seeking tasks. It also discusses how other disciplines including databases, artificial intelligence, and cognitive science can be brought into building effective and efficient aggregated search systems.
This 2-hour lecture was held at Amsterdam University of Applied Sciences (HvA) on October 16th, 2013. It represents a basic overview over core technologies used by ICT companies such as Google, Twitter or Facebook. The lecture does not require a strong technical background and stays at conceptual level.
An Introduction to Information Retrieval and Applicationssathish sak
An Introduction to Information Retrieval and Applications The score you get depends on the functions, difficulty and quality of your project
For system development:
System functions and correctness
For academic paper presentation
Quality and your presentation of the paper
Major methods/experimental results *must* be presented
Papers from top conferences are strongly suggested
E.g. SIGIR, WWW, CIKM, WSDM, JCDL, ICMR, …
Proposals are *required* for each team, and will be counted in the score
INTRODUCTION TO INFORMATION RETRIEVAL
This lecture will introduce the information retrieval problem, introduce the terminology related to IR, and provide a history of IR. In particular, the history of the web and its impact on IR will be discussed. Special attention and emphasis will be given to the concept of relevance in IR and the critical role it has played in the development of the subject. The lecture will end with a conceptual explanation of the IR process, and its relationships with other domains as well as current research developments.
INFORMATION RETRIEVAL MODELS
This lecture will present the models that have been used to rank documents according to their estimated relevance to user given queries, where the most relevant documents are shown ahead to those less relevant. Many of these models form the basis for many of the ranking algorithms used in many of past and today’s search applications. The lecture will describe models of IR such as Boolean retrieval, vector space, probabilistic retrieval, language models, and logical models. Relevance feedback, a technique that either implicitly or explicitly modifies user queries in light of their interaction with retrieval results, will also be discussed, as this is particularly relevant to web search and personalization.
Keystone Summer School 2015: Mauro Dragoni, Ontologies For Information RetrievalMauro Dragoni
The presentation provides an overview of what an ontology is and how it can be used for representing information and for retrieving data with a particular focus on the linguistic resources available for supporting this kind of task. Overview of semantic-based retrieval approaches by highlighting the pro and cons of using semantic approaches with respect to classic ones. Use cases are presented and discussed
The SlideShare 101 is a quick start guide if you want to walk through the main features that the platform offers. This will keep getting updated as new features are launched.
The SlideShare 101 replaces the earlier "SlideShare Quick Tour".
Chapter 1: Introduction to Information Storage and Retrievalcaptainmactavish1996
Course material for 3rd year Information Technology students. Information Storage and Retrieval Course. Chapter 1: Introduction to Information storage and retrieval
IJRET-V1I1P5 - A User Friendly Mobile Search Engine for fast Accessing the Da...ISAR Publications
Mobile search engine is a meta search engine that imprisonments the user’s favorite in
the form of concepts by mining their click through data. But the search query is limited to small
words unlike those used when interacting with search engines through computers. It has become
popular because of presence of huge number of applications. Smartphone’s carry large amount of
personal information, such as user’s personal details, contacts, messages, emails, credit card
information, etc. User type specific search and finally Ontology based Search. Moreover opinion
mining is conducted to provide feedback and valuable suggestions given by the mobile users. Due
to the different characteristics of the content concepts and location concepts, use different
techniques for their concept extraction and ontology formulation. Moreover the individual users
can use this search engine, which runs on android platform. They can give feedbacks and
suggestions about the search result. Based on the feedback other users can get valuable
information about the services available in their location or nearby location.
This chapter introduces the notion of Information Retrieval (IR). it discusses after a survey of classification of various IR systems and major components of an IR system, the notion of Boolean Retrieval model and Invertex Index and extended Boolean are presented.
International Journal of Engineering Research and Applications (IJERA) is an open access online peer reviewed international journal that publishes research and review articles in the fields of Computer Science, Neural Networks, Electrical Engineering, Software Engineering, Information Technology, Mechanical Engineering, Chemical Engineering, Plastic Engineering, Food Technology, Textile Engineering, Nano Technology & science, Power Electronics, Electronics & Communication Engineering, Computational mathematics, Image processing, Civil Engineering, Structural Engineering, Environmental Engineering, VLSI Testing & Low Power VLSI Design etc.
Instructions for Submissions thorugh G- Classroom.pptxJheel Barad
This presentation provides a briefing on how to upload submissions and documents in Google Classroom. It was prepared as part of an orientation for new Sainik School in-service teacher trainees. As a training officer, my goal is to ensure that you are comfortable and proficient with this essential tool for managing assignments and fostering student engagement.
Unit 8 - Information and Communication Technology (Paper I).pdfThiyagu K
This slides describes the basic concepts of ICT, basics of Email, Emerging Technology and Digital Initiatives in Education. This presentations aligns with the UGC Paper I syllabus.
How to Split Bills in the Odoo 17 POS ModuleCeline George
Bills have a main role in point of sale procedure. It will help to track sales, handling payments and giving receipts to customers. Bill splitting also has an important role in POS. For example, If some friends come together for dinner and if they want to divide the bill then it is possible by POS bill splitting. This slide will show how to split bills in odoo 17 POS.
The Art Pastor's Guide to Sabbath | Steve ThomasonSteve Thomason
What is the purpose of the Sabbath Law in the Torah. It is interesting to compare how the context of the law shifts from Exodus to Deuteronomy. Who gets to rest, and why?
How to Create Map Views in the Odoo 17 ERPCeline George
The map views are useful for providing a geographical representation of data. They allow users to visualize and analyze the data in a more intuitive manner.
The French Revolution, which began in 1789, was a period of radical social and political upheaval in France. It marked the decline of absolute monarchies, the rise of secular and democratic republics, and the eventual rise of Napoleon Bonaparte. This revolutionary period is crucial in understanding the transition from feudalism to modernity in Europe.
For more information, visit-www.vavaclasses.com
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In Odoo, the multi-company feature allows you to manage multiple companies within a single Odoo database instance. Each company can have its own configurations while still sharing common resources such as products, customers, and suppliers.
The Roman Empire A Historical Colossus.pdfkaushalkr1407
The Roman Empire, a vast and enduring power, stands as one of history's most remarkable civilizations, leaving an indelible imprint on the world. It emerged from the Roman Republic, transitioning into an imperial powerhouse under the leadership of Augustus Caesar in 27 BCE. This transformation marked the beginning of an era defined by unprecedented territorial expansion, architectural marvels, and profound cultural influence.
The empire's roots lie in the city of Rome, founded, according to legend, by Romulus in 753 BCE. Over centuries, Rome evolved from a small settlement to a formidable republic, characterized by a complex political system with elected officials and checks on power. However, internal strife, class conflicts, and military ambitions paved the way for the end of the Republic. Julius Caesar’s dictatorship and subsequent assassination in 44 BCE created a power vacuum, leading to a civil war. Octavian, later Augustus, emerged victorious, heralding the Roman Empire’s birth.
Under Augustus, the empire experienced the Pax Romana, a 200-year period of relative peace and stability. Augustus reformed the military, established efficient administrative systems, and initiated grand construction projects. The empire's borders expanded, encompassing territories from Britain to Egypt and from Spain to the Euphrates. Roman legions, renowned for their discipline and engineering prowess, secured and maintained these vast territories, building roads, fortifications, and cities that facilitated control and integration.
The Roman Empire’s society was hierarchical, with a rigid class system. At the top were the patricians, wealthy elites who held significant political power. Below them were the plebeians, free citizens with limited political influence, and the vast numbers of slaves who formed the backbone of the economy. The family unit was central, governed by the paterfamilias, the male head who held absolute authority.
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Roman architecture and engineering achievements were monumental. They perfected the arch, vault, and dome, constructing enduring structures like the Colosseum, Pantheon, and aqueducts. These engineering marvels not only showcased Roman ingenuity but also served practical purposes, from public entertainment to water supply.
2. About the Course
Book:
An Introduction to Information Retrieval, Christopher D.
Manning Prabhakar Raghavan Hinrich Schütze, Cambridge
University Press, 2009.
Other materials may be considered depending on the subject.
Principal objective of this course:
To introduce students to Information Retrieval concepts,
paradigms and techniques, with an emphasis on String and
Semantics based IR techniques.
3. About the Course
Grading & Assessment:
First Exam …………………….. 20%
Second Exam ………………….. 20%
Final Exam …………………….. 35%
Other Activities ………………. 10%
Major Assignment ……………. 15%
“You are to build a prototype for a search engine that employs
both text-based and semantics-based techniques for retrieving the
most relevant results to users’ queries. The search space will be a
collection of documents, in addition to a collection of images
associated with some textual descriptions”.
4. Course Topics
Part 01 – Introduction
What is IR?
Examples of IR Systems.
Other topics related to IR.
Models of IR
Part 02 – Boolean Retrieval
What is Boolean IR?
Term-Document Incidence Matrices
Terminology and Notations
5. Course Topics
Part 03 – Indexing
Building Indexes
Semantic Networks
Part 04 – Retrieval
Scoring, Ranking
Relevance Feedback
Precision/Recall
6. Course Topics
Part 05 – Exploiting Ontologies in IR
Ontologies
Traditional vs. Semantics-based IR techniques
7. Introduction
What is IR
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).”
Unstructured Data:
“refers to data which does not have clear, semantically overt, easy-for-a-
computer structure.”
e.g. Textual information in web pages.
Semistructured Data:
“refers to data which have a partially clear, semantically overt, easy-for-a-
computer structure.”
e.g. finding a document where the title contains Java and the body
contains threading.
8. Introduction
What is IR
Structured Data:
“refers to data which have a clear, semantically overt, easy-
for-a-computer structure.”
e.g. Relational Databases.
9. A look back: 1990s
Studies showed that most people preferred getting
information from other people rather than from information
retrieval systems.
Online booking systems?
Following to this period and after relentless optimization of
IR:
The field of information retrieval has moved from being a
primarily academic discipline to being the basis underlying
most people’s preferred means of information access.
Introduction
What is IR
10. Information retrieval did not begin with the Web.
The field began with scientific publications and library
records, but soon spread to other forms of content, particularly
those of information professionals, such as journalists, lawyers,
and doctors
Introduction
What is IR
11. Introduction
Other Topics Related to IR
Cross-language IR
Multimedia IR
Speech retrieval
User interfaces for IR
Ontology and Semantics-based IR
Natural Language Processing (NLP) techniques
Dynamic IR
Online Advertising !?
12. Introduction
Other Topics Related to IR
The field of information retrieval also covers supporting users in
browsing or filtering document collections or further processing
a set of retrieved documents.
Given a set of documents, clustering is the task of coming up
with a good grouping of the documents based on their contents.
Given a set of topics, standing information needs, or other
categories (such as suitability of texts for different age groups),
classification is the task of deciding which class(es), if any,
each of a set of documents belongs to. It is often approached by
first manually classifying some documents and then hoping to
be able to classify new documents automatically.
13. Introduction
Classification of IR systems
Scale-based Classification of IR systems: Distinguishing
between Information retrieval systems according to the scale at
which they operate.
1. Web search: The search is conducted over billions of
documents stored on millions of computers.
Issues to consider:
1. Needing to gather documents for indexing.
2. Being able to build systems that work efficiently at this
enormous scale.
3. Handling particular aspects of the web, such as the
exploitation of hypertext and page ranking given the
commercial importance of the web.
14. 2. Personal Information Retrieval: Integrating information
retrieval into consumer operating systems.
Issues to consider:
1. Handling the broad range of document types on a typical
personal computer.
2. Making the search system maintenance free and
sufficiently lightweight in terms of startup, processing, and
disk space usage that it can run on one machine without
annoying its owner.
Introduction
Classification of IR systems
15. 3. Enterprise, Institutional, and Domain-specific Search:
A corporation’s documents will typically be stored on
centralized file systems and one or a handful of
dedicated machines will provide search over the
collection.
Issues to consider:
1. Handling the broad range of document types on a
centralized computer.
2. Scale and Efficiency of the IR system.
3. Maintenance of the search system.
Introduction
Classification of IR systems
16. Introduction
Classification of IR systems
Technique-based Classification of IR systems:
Distinguishing between Information retrieval systems
according to the search technique that they employ.
1. Keyword-based search: String matching algorithms are
employed to find documents relevant to the user’s query.
Issues to consider:
1. Precision and Recall of the search algorithm.
2. Gap between the textual information contained in the
document collections and the user’s information need.
17. Introduction
Classification of IR systems
2. Semantics-based search: Semantic aspects of the
user’s query are derived in an attempt to find documents
relevant to the user’s query.
Issues to consider:
1. Precision and Recall of the search algorithm.
2. Lack of Semantic Resources.
3. Incompleteness of Background Knowledge
represented in existing Semantic Resources.
4. Semantic Heterogeneity problem between existing
Semantic Resources.
5. Lack of Multi-lingual Semantic Resources.
18. Introduction
Classification of IR systems
2. Hybrid Approaches: Keyword-based search is enriched with
Semantics-based search to retrieve more relevant results to the
user’s information needs.
Issues to consider:
1. Precision and Recall of the search algorithm.
2. Lack of Semantic Resources.
3. Priority of the employed techniques.
4. Incompleteness of Background Knowledge represented in
existing Semantic Resources.
5. Types of queries that the system can handle (Single-term vs.
Verbose queries).
6. Lack of Multi-lingual Semantic Resources.
Research is very active in this area.
Example: Dbpedia based search engine (June 2015)