This document provides an overview of information retrieval models. It begins with definitions of information retrieval and how it differs from data retrieval. It then discusses the retrieval process and logical representations of documents. A taxonomy of IR models is presented including classic, structured, and browsing models. Boolean, vector, and probabilistic models are explained as examples of classic models. The document concludes with descriptions of ad-hoc retrieval and filtering tasks and formal characteristics of IR models.
This document provides a full syllabus with questions and answers related to the course "Information Retrieval" including definitions of key concepts, the historical development of the field, comparisons between information retrieval and web search, applications of IR, components of an IR system, and issues in IR systems. It also lists examples of open source search frameworks and performance measures for search engines.
An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
The document discusses information retrieval, which involves obtaining information resources relevant to an information need from a collection. The information retrieval process begins when a user submits a query. The system matches queries to database information, ranks objects based on relevance, and returns top results to the user. The process involves document acquisition and representation, user problem representation as queries, and searching/retrieval through matching and result retrieval.
The document discusses various information retrieval models, including:
1) Classic models like Boolean and vector space models that use index terms to represent documents and queries.
2) Probabilistic models that view IR as estimating the probability of relevance between documents and queries.
3) Structured models that incorporate document structure, including models based on non-overlapping text regions and hierarchical document structure.
4) Browsing models like flat, structure-guided, and hypertext models for navigating document collections.
This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
The document discusses information retrieval models. It describes the Boolean retrieval model, which represents documents and queries as sets of terms combined with Boolean operators. Documents are retrieved if they satisfy the Boolean query, but there is no ranking of results. The Boolean model has limitations including difficulty expressing complex queries, controlling result size, and ranking results. It works best for simple, precise queries when users know exactly what they are searching for.
Ppt evaluation of information retrieval systemsilambu111
The document discusses the evaluation of information retrieval systems. Evaluation is defined as systematically determining a subject's merit using a set of standards. The main purposes of evaluation are to compare the performance of different systems, assess how well systems meet their goals, and identify ways to improve effectiveness. Evaluation can consider managerial or user viewpoints. Common criteria include recall, precision, fallout, generality, effectiveness, efficiency, usability, satisfaction, and cost. Recall measures the proportion of relevant documents retrieved while precision measures the proportion of retrieved documents that are relevant. Evaluation helps identify ways to improve information retrieval system performance.
Probabilistic information retrieval models & systemsSelman Bozkır
The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
This document provides a full syllabus with questions and answers related to the course "Information Retrieval" including definitions of key concepts, the historical development of the field, comparisons between information retrieval and web search, applications of IR, components of an IR system, and issues in IR systems. It also lists examples of open source search frameworks and performance measures for search engines.
An inverted file indexes a text collection to speed up searching. It contains a vocabulary of distinct words and occurrences lists with information on where each word appears. For each term in the vocabulary, it stores a list of pointers to occurrences called an inverted list. Coarser granularity indexes use less storage but require more processing, while word-level indexes enable proximity searches but use more space. The document describes how inverted files are structured and constructed from text and discusses techniques like block addressing that reduce their space requirements.
The document discusses information retrieval, which involves obtaining information resources relevant to an information need from a collection. The information retrieval process begins when a user submits a query. The system matches queries to database information, ranks objects based on relevance, and returns top results to the user. The process involves document acquisition and representation, user problem representation as queries, and searching/retrieval through matching and result retrieval.
The document discusses various information retrieval models, including:
1) Classic models like Boolean and vector space models that use index terms to represent documents and queries.
2) Probabilistic models that view IR as estimating the probability of relevance between documents and queries.
3) Structured models that incorporate document structure, including models based on non-overlapping text regions and hierarchical document structure.
4) Browsing models like flat, structure-guided, and hypertext models for navigating document collections.
This document provides an overview of an information retrieval system. It defines an information retrieval system as a system capable of storing, retrieving, and maintaining information such as text, images, audio, and video. The objectives of an information retrieval system are to minimize the overhead for a user to locate needed information. The document discusses functions like search, browse, indexing, cataloging, and various capabilities to facilitate querying and retrieving relevant information from the system.
The document discusses information retrieval models. It describes the Boolean retrieval model, which represents documents and queries as sets of terms combined with Boolean operators. Documents are retrieved if they satisfy the Boolean query, but there is no ranking of results. The Boolean model has limitations including difficulty expressing complex queries, controlling result size, and ranking results. It works best for simple, precise queries when users know exactly what they are searching for.
Ppt evaluation of information retrieval systemsilambu111
The document discusses the evaluation of information retrieval systems. Evaluation is defined as systematically determining a subject's merit using a set of standards. The main purposes of evaluation are to compare the performance of different systems, assess how well systems meet their goals, and identify ways to improve effectiveness. Evaluation can consider managerial or user viewpoints. Common criteria include recall, precision, fallout, generality, effectiveness, efficiency, usability, satisfaction, and cost. Recall measures the proportion of relevant documents retrieved while precision measures the proportion of retrieved documents that are relevant. Evaluation helps identify ways to improve information retrieval system performance.
Probabilistic information retrieval models & systemsSelman Bozkır
The document discusses probabilistic information retrieval and Bayesian approaches. It introduces concepts like conditional probability, Bayes' theorem, and the probability ranking principle. It explains how probabilistic models estimate the probability of relevance between a document and query by representing them as term sets and making probabilistic assumptions. The goal is to rank documents by the probability of relevance to present the most likely relevant documents first.
The document discusses signature files, which are used for document retrieval. A signature file creates a compressed representation or "signature" for each document in a database. These signatures are stored in hash tables to allow easy retrieval of matching documents for user queries. Signatures can represent words using triplets of characters and a hash function, or entire documents through concatenation of word signatures or superimposed coding. Signature files provide a quick link between queries and documents but have lower accuracy than inverted files, which are generally better for information retrieval applications.
This document compares web search and information retrieval (IR) across 10 differentiators:
1. Languages - Web search indexes documents in many languages using full text, while IR databases usually cover one language.
2. File types - Web search indexes several file types including some without text, while IR indexes consistent formats like PDF.
3. Document length - Web documents vary widely in length from short to long, while IR documents vary less.
4. Document structure - Web documents are semi-structured HTML, while IR allows searching structured document fields.
The Boolean model is a classical information retrieval model based on set theory and Boolean logic. Queries are specified as Boolean expressions to retrieve documents that either contain or do not contain the query terms. All term frequencies are binary and documents are retrieved based on an exact match to the query terms. However, this model has limitations as it does not rank documents and queries are difficult for users to translate into Boolean expressions, often returning too few or too many results.
The document discusses different theories used in information retrieval systems. It describes cognitive or user-centered theories that model human information behavior and structural or system-centered theories like the vector space model. The vector space model represents documents and queries as vectors of term weights and compares similarities between queries and documents. It was first used in the SMART information retrieval system and involves assigning term vectors and weights to documents based on relevance.
The document discusses probabilistic retrieval models in information retrieval. It introduces three influential probabilistic models: (1) Maron and Kuhns' 1960 model which calculates the probability of relevance based on historical user data; (2) Salton's model which estimates the probability of term occurrence in relevant documents; (3) A model that ranks documents by the probability of relevance and considers retrieval as a decision between costs of retrieving non-relevant vs. not retrieving relevant documents. The document provides background on the development of probabilistic IR models and challenges of estimating probabilities for evaluation.
Functions of information retrival system(1)silambu111
The document discusses information retrieval systems. It defines information retrieval as the process of searching collections of documents to identify those dealing with a particular subject. Information retrieval systems aim to facilitate literature searching. They involve representing, storing, organizing, and providing access to information items so that users can easily find information of interest. Information retrieval draws from multiple disciplines and involves subsystems for documents, users, and searching/matching.
Parallel and Distributed Information Retrieval Systemvimalsura
This document discusses parallel and distributed information retrieval. It describes how parallel architectures like MIMD can be used to accelerate search over very large document collections by distributing the work across multiple processors. Two main approaches to parallelism are covered: building new parallel algorithms or adapting existing techniques. Common ways to partition data for parallel indexing and search are discussed, including document partitioning and term partitioning. Specific data structures like inverted files, suffix arrays, and signature files are examined in terms of how they can be adapted for parallel and distributed retrieval architectures.
Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
1. The document defines key terms related to information retrieval systems such as information, retrieval, system, and discusses the basic components and functions of IRS.
2. It explains that the role of users is to formulate queries, and the role of librarians is to assist users in meeting their information needs.
3. The document contrasts older IRS that retrieved entire documents with modern IRS that allow storage, organization, and access to text and multimedia information through techniques like keyword searching and hyperlinks.
The document discusses the World Wide Web and information retrieval on the web. It provides background on how the web was developed by Tim Berners-Lee in 1990 using HTML, HTTP, and URLs. It then discusses some key differences in information retrieval on the web compared to traditional library systems, including the presence of hyperlinks, heterogeneous content, duplication of content, exponential growth in the number of documents, and lack of stability. It also summarizes some challenges in web search including the expanding nature of the web, dynamically generated content, influence of monetary contributions on search results, and search engine spamming.
The document discusses key concepts related to information retrieval including data, information, knowledge, and wisdom. It defines information retrieval as the tracing and recovery of specific information from stored data through searching. The main aspects of the information retrieval process are described as querying a collection to retrieve relevant objects that may partially match the query. Precision and recall are discussed as important measures for information retrieval systems.
Boolean,vector space retrieval Models Primya Tamil
The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
Information retrieval 13 alternative set theoretic modelsVaibhav Khanna
Alternative Set Theoretic Models
Fuzzy Set Model :a set theoretic model of document retrieval based on fuzzy theory.
Extended Boolean Model:a set theoretic model of document retrieval based on an extension of the classic Boolean model. The idea is to interpret partial matches as Euclidean distances represented in a vectorial space of index terms.
The document discusses the vector space model for representing text documents and queries in information retrieval systems. It describes how documents and queries are represented as vectors of term weights, with each term being assigned a weight based on its frequency in the document or query. The vector space model allows documents and queries to be compared by calculating the similarity between their vector representations. Terms that are more frequent in a document and less frequent overall are given higher weights through techniques like TF-IDF weighting. This vector representation enables efficient retrieval of documents ranked by similarity to the query.
The document discusses various aspects of term weighting which is important for text retrieval systems, including term frequency, document frequency, inverse document frequency, and how they are used to calculate TF-IDF weights for terms. It also covers stoplists, stemming, and the bag-of-words model which represents text as vectors of word occurrences without considering word order. Term weighting schemes play a major role in the similarity measures used by information retrieval systems to determine document relevance.
Information retrieval 14 fuzzy set models of irVaibhav Khanna
Fuzzy Model is a set theoretic model of document retrieval based on fuzzy theory. An opposite to this is the Exact match mechanism by which only the objects satisfying some well specified criteria, against object attributes, are returned to the user as a query answer.
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
There are three main points about data streams and stream processing:
1) A data stream is a continuous, ordered sequence of data items that arrives too rapidly to be stored fully. Common sources include sensors, web traffic, and social media.
2) Data stream management systems process continuous queries over streams in real-time using bounded memory. They provide summaries of historical data rather than storing entire streams.
3) Challenges of stream processing include limited memory, complex continuous queries, and unpredictable data rates and characteristics. Approximate query processing techniques like windows, sampling, and load shedding help address these challenges.
Information retrival system and PageRank algorithmRupali Bhatnagar
We discuss the various models for Information retrieval system present in literature and discuss them mathematically. We also study the PageRank Algorithm which is used for relevant search.
Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
The document discusses the history and components of information retrieval systems. It defines information retrieval as searching a collection of documents to identify those related to a topic. Modern systems can retrieve multimedia content like text, audio, images and video. The key components are the document subsystem that analyzes and stores content, the user subsystem that analyzes queries, and the retrieval subsystem that matches queries to documents. The purpose is to connect those generating information with those needing it.
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
The document discusses signature files, which are used for document retrieval. A signature file creates a compressed representation or "signature" for each document in a database. These signatures are stored in hash tables to allow easy retrieval of matching documents for user queries. Signatures can represent words using triplets of characters and a hash function, or entire documents through concatenation of word signatures or superimposed coding. Signature files provide a quick link between queries and documents but have lower accuracy than inverted files, which are generally better for information retrieval applications.
This document compares web search and information retrieval (IR) across 10 differentiators:
1. Languages - Web search indexes documents in many languages using full text, while IR databases usually cover one language.
2. File types - Web search indexes several file types including some without text, while IR indexes consistent formats like PDF.
3. Document length - Web documents vary widely in length from short to long, while IR documents vary less.
4. Document structure - Web documents are semi-structured HTML, while IR allows searching structured document fields.
The Boolean model is a classical information retrieval model based on set theory and Boolean logic. Queries are specified as Boolean expressions to retrieve documents that either contain or do not contain the query terms. All term frequencies are binary and documents are retrieved based on an exact match to the query terms. However, this model has limitations as it does not rank documents and queries are difficult for users to translate into Boolean expressions, often returning too few or too many results.
The document discusses different theories used in information retrieval systems. It describes cognitive or user-centered theories that model human information behavior and structural or system-centered theories like the vector space model. The vector space model represents documents and queries as vectors of term weights and compares similarities between queries and documents. It was first used in the SMART information retrieval system and involves assigning term vectors and weights to documents based on relevance.
The document discusses probabilistic retrieval models in information retrieval. It introduces three influential probabilistic models: (1) Maron and Kuhns' 1960 model which calculates the probability of relevance based on historical user data; (2) Salton's model which estimates the probability of term occurrence in relevant documents; (3) A model that ranks documents by the probability of relevance and considers retrieval as a decision between costs of retrieving non-relevant vs. not retrieving relevant documents. The document provides background on the development of probabilistic IR models and challenges of estimating probabilities for evaluation.
Functions of information retrival system(1)silambu111
The document discusses information retrieval systems. It defines information retrieval as the process of searching collections of documents to identify those dealing with a particular subject. Information retrieval systems aim to facilitate literature searching. They involve representing, storing, organizing, and providing access to information items so that users can easily find information of interest. Information retrieval draws from multiple disciplines and involves subsystems for documents, users, and searching/matching.
Parallel and Distributed Information Retrieval Systemvimalsura
This document discusses parallel and distributed information retrieval. It describes how parallel architectures like MIMD can be used to accelerate search over very large document collections by distributing the work across multiple processors. Two main approaches to parallelism are covered: building new parallel algorithms or adapting existing techniques. Common ways to partition data for parallel indexing and search are discussed, including document partitioning and term partitioning. Specific data structures like inverted files, suffix arrays, and signature files are examined in terms of how they can be adapted for parallel and distributed retrieval architectures.
Automatic indexing is the process of analyzing documents to extract information to be included in an index. This can be done through statistical, natural language, concept-based, or hypertext linkage techniques. Statistical techniques are the most common, identifying words and phrases to index documents. Natural language techniques perform additional parsing of text. Concept indexing correlates words to concepts, while hypertext linkages create connections between documents. The goal of automatic indexing is to preprocess documents to allow for relevant search results by representing concepts in the index.
1. The document defines key terms related to information retrieval systems such as information, retrieval, system, and discusses the basic components and functions of IRS.
2. It explains that the role of users is to formulate queries, and the role of librarians is to assist users in meeting their information needs.
3. The document contrasts older IRS that retrieved entire documents with modern IRS that allow storage, organization, and access to text and multimedia information through techniques like keyword searching and hyperlinks.
The document discusses the World Wide Web and information retrieval on the web. It provides background on how the web was developed by Tim Berners-Lee in 1990 using HTML, HTTP, and URLs. It then discusses some key differences in information retrieval on the web compared to traditional library systems, including the presence of hyperlinks, heterogeneous content, duplication of content, exponential growth in the number of documents, and lack of stability. It also summarizes some challenges in web search including the expanding nature of the web, dynamically generated content, influence of monetary contributions on search results, and search engine spamming.
The document discusses key concepts related to information retrieval including data, information, knowledge, and wisdom. It defines information retrieval as the tracing and recovery of specific information from stored data through searching. The main aspects of the information retrieval process are described as querying a collection to retrieve relevant objects that may partially match the query. Precision and recall are discussed as important measures for information retrieval systems.
Boolean,vector space retrieval Models Primya Tamil
The document discusses various information retrieval models including Boolean, vector space, and probabilistic models. It provides details on how documents and queries are represented and compared in the vector space model. Specifically, it explains that in this model, documents and queries are represented as vectors of term weights in a multi-dimensional space. The similarity between a document and query vector is calculated using measures like the inner product or cosine similarity to retrieve and rank documents.
Information retrieval 13 alternative set theoretic modelsVaibhav Khanna
Alternative Set Theoretic Models
Fuzzy Set Model :a set theoretic model of document retrieval based on fuzzy theory.
Extended Boolean Model:a set theoretic model of document retrieval based on an extension of the classic Boolean model. The idea is to interpret partial matches as Euclidean distances represented in a vectorial space of index terms.
The document discusses the vector space model for representing text documents and queries in information retrieval systems. It describes how documents and queries are represented as vectors of term weights, with each term being assigned a weight based on its frequency in the document or query. The vector space model allows documents and queries to be compared by calculating the similarity between their vector representations. Terms that are more frequent in a document and less frequent overall are given higher weights through techniques like TF-IDF weighting. This vector representation enables efficient retrieval of documents ranked by similarity to the query.
The document discusses various aspects of term weighting which is important for text retrieval systems, including term frequency, document frequency, inverse document frequency, and how they are used to calculate TF-IDF weights for terms. It also covers stoplists, stemming, and the bag-of-words model which represents text as vectors of word occurrences without considering word order. Term weighting schemes play a major role in the similarity measures used by information retrieval systems to determine document relevance.
Information retrieval 14 fuzzy set models of irVaibhav Khanna
Fuzzy Model is a set theoretic model of document retrieval based on fuzzy theory. An opposite to this is the Exact match mechanism by which only the objects satisfying some well specified criteria, against object attributes, are returned to the user as a query answer.
This document provides an overview of information retrieval models, including vector space models, TF-IDF, Doc2Vec, and latent semantic analysis. It begins with basic concepts in information retrieval like document indexing and relevance scoring. Then it discusses vector space models and how documents and queries are represented as vectors. TF-IDF weighting is explained as assigning higher weight to rare terms. Doc2Vec is introduced as an extension of word2vec to learn document embeddings. Latent semantic analysis uses singular value decomposition to project documents to a latent semantic space. Implementation details and examples are provided for several models.
There are three main points about data streams and stream processing:
1) A data stream is a continuous, ordered sequence of data items that arrives too rapidly to be stored fully. Common sources include sensors, web traffic, and social media.
2) Data stream management systems process continuous queries over streams in real-time using bounded memory. They provide summaries of historical data rather than storing entire streams.
3) Challenges of stream processing include limited memory, complex continuous queries, and unpredictable data rates and characteristics. Approximate query processing techniques like windows, sampling, and load shedding help address these challenges.
Information retrival system and PageRank algorithmRupali Bhatnagar
We discuss the various models for Information retrieval system present in literature and discuss them mathematically. We also study the PageRank Algorithm which is used for relevant search.
Association analysis is a technique used to uncover relationships between items in transactional data. It involves finding frequent itemsets whose occurrence exceeds a minimum support threshold, and then generating association rules from these itemsets that satisfy minimum confidence. The Apriori algorithm is commonly used for this task, as it leverages the Apriori property to prune the search space - if an itemset is infrequent, its supersets cannot be frequent. It performs multiple database scans to iteratively grow frequent itemsets and extract high confidence rules.
The document discusses the history and components of information retrieval systems. It defines information retrieval as searching a collection of documents to identify those related to a topic. Modern systems can retrieve multimedia content like text, audio, images and video. The key components are the document subsystem that analyzes and stores content, the user subsystem that analyzes queries, and the retrieval subsystem that matches queries to documents. The purpose is to connect those generating information with those needing it.
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
This document provides an overview of an information retrieval course. The course will cover topics related to information retrieval models, techniques, and systems. Students will complete exams, assignments, and a major project to build a search engine using both text-based and semantic retrieval techniques. The document defines key concepts in information retrieval and discusses different types of information retrieval systems and techniques.
This document provides an overview of an information retrieval system (IRS). It defines IRS as obtaining relevant information from a collection to meet a user's need. The IRS has three main components: a document subsystem for acquiring, representing and organizing data; a user subsystem for representing queries; and a search/retrieval subsystem for matching queries to documents. It describes the basic concepts like how a user enters a query that is scored and ranked to return relevant results, which can be iterated. The objectives are to highlight probabilistic models and establish relationships between popular techniques. The functions are to analyze information sources and queries to match and retrieve relevant items. [/SUMMARY]
An Improved Annotation Based Summary Generation For Unstructured DataMelinda Watson
This document discusses annotation-based summarization of unstructured data. It begins with an introduction to annotation and information retrieval. Current annotation processes cannot maintain modifications due to frequent document updates. The document then reviews literature on automatic text classification, applying annotations to linked open data sets, and using domain ontologies for automatic document annotation. Keywords, sentences and contexts are extracted from documents for annotation. Different annotation models are discussed. The goal is to develop an improved annotation approach for summarizing unstructured data that can handle frequent document changes.
This document provides an overview of information retrieval systems, including their definition, objectives, and key functional processes. An information retrieval system aims to minimize the time and effort users spend locating needed information by supporting search generation, presenting relevant results, and allowing iterative refinement of searches. The major functional processes involve normalizing input items, selectively disseminating new items to users, searching archived documents and user-created indexes. Information retrieval systems differ from database management systems in their handling of unstructured text-based information rather than strictly structured data.
This document provides an overview of information retrieval systems, including their definition, objectives, and key functional processes. An information retrieval system aims to minimize the time and effort users spend locating needed information by supporting search generation, presenting relevant results, and allowing iterative refinement of searches. The major functional processes involve normalizing input items, selectively disseminating new items to users, searching archived documents and user-created indexes. Information retrieval systems differ from database management systems in their handling of unstructured text-based information rather than strictly structured data.
The document provides an introduction to information retrieval, including its history, key concepts, and challenges. It discusses how information retrieval aims to retrieve relevant documents from a collection to satisfy a user's information need. The main challenge in information retrieval is determining relevance, as relevance depends on personal assessment, task, context, time, location, and device. Three main issues in information retrieval are determining relevance, representing documents and queries, and developing effective retrieval models and algorithms.
The document provides an introduction to information retrieval, including its history, key concepts, and challenges. It discusses how information retrieval aims to retrieve relevant documents from a collection to satisfy a user's information need. The main challenge in information retrieval is determining relevance, as relevance depends on personal assessment and can change based on context, time, location, and device. The document outlines the major issues and developments in the field over time from the 1950s to present day.
INTELLIGENT INFORMATION RETRIEVAL WITHIN DIGITAL LIBRARY USING DOMAIN ONTOLOGYcscpconf
A digital library is a type of information retrieval (IR) system. The existing information retrieval
methodologies generally have problems on keyword-searching. We proposed a model to solve
the problem by using concept-based approach (ontology) and metadata case base. This model
consists of identifying domain concepts in user’s query and applying expansion to them. The
system aims at contributing to an improved relevance of results retrieved from digital libraries
by proposing a conceptual query expansion for intelligent concept-based retrieval. We need to
import the concept of ontology, making use of its advantage of abundant semantics and
standard concept. Domain specific ontology can be used to improve information retrieval from
traditional level based on keyword to the lay based on knowledge (or concept) and change the
process of retrieval from traditional keyword matching to semantics matching. One approach is
query expansion techniques using domain ontology and the other would be introducing a case
based similarity measure for metadata information retrieval using Case Based Reasoning
(CBR) approach. Results show improvements over classic method, query expansion using
general purpose ontology and a number of other approaches.
1. The document proposes techniques to improve search performance by matching schemas between structured and unstructured data sources.
2. It involves constructing schema mappings using named entities and schema structures. It also uses strategies to narrow the search space to relevant documents.
3. The techniques were shown to improve search accuracy and reduce time/space complexity compared to existing methods.
Performance Evaluation of Query Processing Techniques in Information Retrievalidescitation
The first element of the search process is the query.
The user query being on an average restricted to two or three
keywords makes the query ambiguous to the search engine.
Given the user query, the goal of an Information Retrieval
[IR] system is to retrieve information which might be useful
or relevant to the information need of the user. Hence, the
query processing plays an important role in IR system.
The query processing can be divided into four categories
i.e. query expansion, query optimization, query classification and
query parsing. In this paper an attempt is made to evaluate the
performance of query processing algorithms in each of the
category. The evaluation was based on dataset as specified by
Forum for Information Retrieval [FIRE15]. The criteria used
for evaluation are precision and relative recall. The analysis is
based on the importance of each step in query processing. The
experimental results show that the significance of each step
in query processing and also the relevance of web semantics
and spelling correction in the user query.
Algorithm for calculating relevance of documents in information retrieval sys...IRJET Journal
The document proposes an algorithm to calculate the relevance of documents returned in response to user queries in information retrieval systems. It is based on classical similarity formulas like cosine, Jaccard, and dice that calculate similarity between document and query vectors. The algorithm aims to integrate user search preferences as a variable in determining document relevance, as classic models do not account for this. It uses text and web mining techniques to process user query and document metadata.
This document introduces inverted files, which are a core data structure for text search engines. It describes inverted files and how they allow for efficient indexing, construction, and querying. The document then outlines some common extensions to inverted file indexes, such as compression, phrase querying, and distribution. It concludes by providing context on text search and information retrieval.
Chapter 1 Introduction to Information Storage and Retrieval.pdfHabtamu100
This course outline provides information about an Information Storage and Retrieval course for third year Information Technology students. The course will cover introductory concepts of information storage and retrieval over 5 ECTS credits across one semester. Topics will include automatic text operations, indexing structures, retrieval models, evaluation, query languages, and current issues. Assessment will include assignments, tests, a project, midterm, and final exam.
Design and Implementation of Meetings Document Management and Retrieval SystemCSCJournals
The document describes the design and implementation of a meetings document management and retrieval system. Key features of the system include:
1. Capturing, storing, indexing, and retrieving meeting documents such as agendas, minutes, and registration forms from a database.
2. Implementing a search facility to allow users to quickly locate topics of interest within documents.
3. Incorporating hyperlinks to enable navigation between related documents and sections.
4. Developing the system as a web application using ASP.NET to allow remote access by authorized users.
The system was designed using object-oriented principles and includes security features to protect unauthorized access to documents. It aims to improve organization and access of meeting
The document provides an overview of the key components and objectives of an information retrieval system. It discusses how an IR system aims to minimize the time a user spends locating needed information by facilitating search generation, presenting search results in a relevant order, and processing incoming documents through normalization, indexing, and selective dissemination to users. The major measures of an IR system's effectiveness are precision and recall.
Information filtering is the process of monitoring large amounts of dynamically generated information and identifying the subset of information likely to be of interest to a user based on their information needs. It represents the user's interests and identifies only pieces of information they would find interesting. There are three main categories of information filtering: collaborative filtering which uses recommendations from other users; content-based filtering which uses a comparison between item content and user profiles; and hybrid filtering which combines aspects of collaborative and content-based filtering. Feedback techniques can also be used to continually update and improve filtering.
This document provides an overview of information retrieval and extraction systems. It discusses how information retrieval systems work by generating representations of documents and queries/profiles, comparing the representations, and returning relevant results. It also outlines the generic modules that comprise information extraction systems, including their inputs, outputs, functions, and rule-based operations.
IJCER (www.ijceronline.com) International Journal of computational Engineerin...ijceronline
This document summarizes text mining techniques for information retrieval, extraction, and indexing. It discusses common information retrieval techniques like inverted indices and signature files. It also covers stemming, domain dictionaries, exclusion lists, and research directions in text mining like finding better representations for extracted information, enabling multilingual analysis, and integrating domain knowledge. The key techniques discussed are text indexing, query processing, and information extraction from text.
Similar to Information retrieval introduction (20)
This is an overview of my current metallic design and engineering knowledge base built up over my professional career and two MSc degrees : - MSc in Advanced Manufacturing Technology University of Portsmouth graduated 1st May 1998, and MSc in Aircraft Engineering Cranfield University graduated 8th June 2007.
Online train ticket booking system project.pdfKamal Acharya
Rail transport is one of the important modes of transport in India. Now a days we
see that there are railways that are present for the long as well as short distance
travelling which makes the life of the people easier. When compared to other
means of transport, a railway is the cheapest means of transport. The maintenance
of the railway database also plays a major role in the smooth running of this
system. The Online Train Ticket Management System will help in reserving the
tickets of the railways to travel from a particular source to the destination.
We have designed & manufacture the Lubi Valves LBF series type of Butterfly Valves for General Utility Water applications as well as for HVAC applications.
2. Topics
Introduction
Information versus Data Retrieval
IR: Past, present, and future
Basic concepts: The retrieval process
Logical view of documents
Modeling: A Taxonomy of IR models
Ad-hoc retrieval and filtering
Classic IR models
Set theoretic
Algebraic
Probabilistic IR models
Models for browsing
3. Introduction
Information retrieval (IR) deals with the
representation, storage, organization and access
to information items.
The representation and organization of the
information items should provide the user with
easy access to the information in which he is
interested.
Unfortunately, characterization of the user
information need is not a simple problem.
Web search engines are the most visible IR
applications.
The user must first translate the request
information into a query which can be processed
by the search engine.
4. An information retrieval process begins when a
user enters a query into the system.
Queries are formal statements of information
needs, for example search strings in web search
engines.
Given the user query, the key goal of an IR
system is to retrieve information which might be
useful or relevant to the user.
The emphasis is on the retrieval of information as
opposed to the retrieval of data.
Data is a row fact and information is the
processed data.
5. Information versus Data Retrieval
Information Retrieval Data Retrieval
Retrieves information about a subject Determines the keywords in the user
query and retrieves data
Small errors are likely to go unnoticed. Single erroneous object means total
failure
Deals with natural language text Deals with relational database
Not always well structured and is
semantically ambiguous.
Has a well defined structure and
semantics
Does not provides solution to the user
of a database system
Provides solution to the user of a
database system
Extracts syntactic and semantic
information from the document text and
use this information to match the user
information need
Does not solve the problem of retrieving
information about a subject
The primary goal is to retrieve all the
documents which are relevant to a user
query while retrieving as few non-
The primary goal is to retrieve the data
according to the user query
6. IR: Past, present, and future
A typical example of information retrieval is the table of
contents of a book
Since the volume of information eventually grew
beyond a few books, it became necessary to build
specialized data structures to ensure faster access to
the stored information.
An old and popular data structure for faster
information retrieval is a collection of selected words
or concepts with which are associated pointers to the
related information is the index.
In one form or another, indexes are at the core of
every modern information retrieval system.
They provide faster access to the data and allow the
query processing task to be speeded up.
7. Two different views of the IR problem: a
computer-centered one and a human-centered
one.
In the computer-centered view, the IR problem
consists mainly of building up efficient indexes,
processing user queries with high performance,
and developing ranking algorithms which improve
the `quality' of the answer set.
In the human-centered view, the IR problem
consists mainly of studying the behavior of the
user, of understanding his main needs, and of
determining how such understanding affects the
organization and operation of the retrieval system
8. Information Retrieval in the Library
Libraries were among the first institutions to adopt
IR systems for retrieving information.
In the first generation, the systems allowed
searches based on author name and title.
In the second generation, increased search
functionality was added which allowed searching
by subject headings, by keywords.
In the third generation, which is currently being
deployed, the focus is on improved graphical
interfaces, electronic forms, hypertext features,
and open system architectures.
9. The Web and Digital Libraries
Three dramatic and fundamental changes have
occurred due to the advances in modern computer
technology and the boom of the Web.
First, it became a lot cheaper to have access to various
sources of information.
Second, the advances in all kinds of digital
communication provided greater access to networks.
Third, the freedom to post whatever information
someone judges useful has greatly contributed to the
popularity of the Web.
Fundamentally, low cost, greater access, and
publishing freedom have allowed people to use the
Web as a highly interactive medium.
Such interactivity allows people to exchange
messages, photos, documents, software, videos, and
11. Basic concepts: The retrieval process
To describe the retrieval process, we use a simple
and generic software architecture .
First of all, before the retrieval process can even be
initiated, it is necessary to define the text database.
This is usually done by the manager of the
database, which specifies the following:
(a) The documents to be used
(b) The operations to be performed on the text
(c) The text model (i.e., the text structure and what
elements can be retrieved). The text operations
transform the original documents and generate a
logical view of them.
Once the logical view of the documents is defined,
the database manager (using the DB Manager
Module) builds an index of the text.
12. An index is a critical data structure because it allows
fast searching over large volumes of data.
Given that the document database is indexed, the
retrieval process can be initiated.
The user first specifies a user need which is then
parsed and transformed by the same text operations
applied to the text.
Then, query operations might be applied before the
actual query, which provides a system representation
for the user need, is generated.
The query is then processed to obtain the retrieved
documents.
Fast query processing is made possible by the index
structure previously built.
Before been sent to the user, the retrieved
documents are ranked according to a likelihood of
relevance.
13. At this point, he might pinpoint a subset of the
documents seen as definitely of interest and initiate
a user feedback cycle.
In such a cycle, the system uses the documents
selected by the user to change the query
formulation.
14. Logical view of documents
Due to historical reasons, documents in a
collection are frequently represented through a
set of index terms or keywords.
Such keywords might be extracted directly from
the text of the document or might be specified by
a human subject.
No matter whether these representative keywords
are derived automatically or generated by a
specialist, they provide a logical view of the
document.
Modern computers are making it possible to
represent a document by its full set of words. In
this case, we say that the retrieval system adopts
a full text logical view of the documents.
15. With very large collections, however, even modern
computers might have to reduce the set of
representative keywords.
This can be accomplished through the elimination
of stopwords (such as articles and connectives), the
use of stemming (which reduces distinct words to
their common grammatical root), and the
identification of noun groups (which eliminates
adjectives, adverbs, and verbs).
These operations are called text operations (or
transformations).
Text operations reduce the complexity of the
document representation and allow moving the
logical view from that of a full text to that of a set
of index terms.
16.
17. Modeling: A Taxonomy of IR models
Three models:
Classic models
Structured models
Browsing models
Three classic models in IR are :
Boolean: documents and queries are represented as
sets of index terms. Also known as set theoretic.
Vector: documents and queries are represented as
vectors in a t-dimensional space. Also known as
algebraic.
Probabilistic: the framework for modeling document and
query representations is based on probability theory.
Set theoretic
Fuzzy
Extended boolean
18. Algebraic
Generalized vector
Latent semantic indexing
Neural networks
Probabilistic
Inference network
Belief network
Two structured models in IR are
Non-overlapping lists model
Proximal nodes
Three models for browsing are:
Flat
Structure guided
Hypertext
19.
20. Ad-hoc retrieval and filtering
Ad hoc retrieval
Standard retrieval task in which the user specifies his
information need through a query which initiates a
search (executed by the information system) for
documents which are likely to be relevant to the user.
The documents in the collection remain relatively static
while new queries are submitted to the system
The most common form of user task
Filtering
The queries remain relatively static while new
documents come into the system (and leave)
User profile
Describing the user’s preferences
Routing (variation of filtering, rank the filtered document)
21.
22.
23. User profile is compared to the incoming documents
to determine the user’s interest.
Eg: selecting a news article among thousands of
articles which are broadcast each day
The task of determining which ones are really relevant
is fully reserved to the user.
This is accomplished by ranking the filtered
documents and thereby user should examine smaller
number of documents. This variation of filtering is
called routing.
To rank the documents vector model is preferred.
In filtering, crucial task is not ranking, but the
construction of the user profile.
An approach for constructing a user profile is to
describe the profile through a set of keywords and to
require the user to provide the necessary keywords.
24. The process is to collect information from the user
about his preferences and use this information to
build the user profile.
In the beginning user provides a set of keywords
which describes an initial profile of his preferences.
As new documents arrive the system uses this
profile to select documents that are relevant and not
relevant.
The system uses this information to adjust the user
profile description such that it reflects new
preferences.
25. Formal characterization of IR models
An IR model is a quadruple [D, Q, F, R(qi, dj)] where
1. D is a set of logical views for the documents in the
collection
2. Q is a set of logical views for the user queries
3. F is a framework for modeling documents and
queries
4. R(qi, dj) is a ranking function
26. Classic IR models
Basic concepts: Each document is described by a set of
representative keywords called index terms
An index term is a word that represents a documents
main theme.
It is used to index and summarize the document contents.
Assign a numerical weights to distinct relevance between
index terms
Three classic models: Boolean, vector, probabilistic
ki : A generic index term
K : The set of all index terms {k1,…,kt}
wi,j : A weight associated with index term
ki of a document dj
For an index term does not appear in the document , wi,j=0
gi: A function returns the weight associated
27. Boolean model
Simple retrieval model based on set theory and
Boolean algebra
Binary decision criterion
Either relevant or not relevant (no partial match)
Data retrieval model
Advantage
Simplicity
Disadvantage
It is not simple to translate an information need into a
Boolean expression
Exact matching may lead to retrieval of too few or too
many documents
28. Formal definition
For the Boolean model, the index term weight are
all binary, i.e. wij {0,1}
A query q is composed of index terms linked by
three connectives: not, and , or.
A query is a conventional Boolean expression,
which can be represented as a disjunction of
conjunctive vectors (in DNF)
q= ka(kbkc)= (1,1,1) (1,1,0) (1,0,0)
where each of the component is a binary weighted
vector associated with the tuple (ka,kb,kc)
The similarity of the document dj to the query q is
defined as
if (qcc )(ki, wi,j=gi(qcc))
0
1
),( qdsim j
(1,1,1)
(1,0,0) (1,1,0)Ka
Kb
Kc
dnfq
dnfq
29. If 1 then the boolean model predicts
that the document dj is relevant to the query q
Otherwise the prediction is that the document is not
relevant.
),( qdsim j
30. Vector model
Assign non-binary weights to index terms in queries and in
documents
Compute the similarity between documents and query =
Sim(dj,q)
More precise than Boolean model
The weight associated with the pair
(ki,dj) positive and non-binary.
The index terms in the query are also weighted
The vector for a document dj is represented by
is the query vector where wi,q be the weight
associated with the pair (ki,q) where wi,q >=0
Term weights are used to compute the degree of
similarity between documents and the user query. It
varies from 0 to +1.
),,,(
),,,(
,,2,1
,,2,1
qtqq
jtjjj
wwwq
wwwd
q
31. A document might be retrieved even if it matches the
query only partially.
Degree of similarity
dj
q
Figure 2.4 The cosine of is adopted
as sim(dj,q)
t
i qi
t
i ji
t
i qiji
j
j
j
ww
ww
qd
qd
qdsim
1
2
,1
2
,
1 ,,
||||
),(
32.
33.
34. Advantages
Its term-weighting scheme improves retrieval
performance
Its partial matching strategy allows retrieval of
documents that approximate the query conditions
Its cosine ranking formula sorts the documents
according to their degree of similarity to the query
Disadvantage
The assumption of mutual independence between index
terms
35. Probabilistic model
Introduced by Roberston and Sparck Jones, 1976
Binary independence retrieval (BIR) model
Idea: Given a user query q, and the ideal answer set R of
the relevant documents, the problem is to specify the
properties for this set
Assumption (probabilistic principle): the probability of
relevance depends on the query and document
representations only; ideal answer set R should
maximize the overall probability of relevance
The probabilistic model tries to estimate the probability
that the user will find the document dj relevant with ratio
P(dj relevant to q)/P(dj nonrelevant to q)
36. Definition
All index term weights are all binary i.e., wi,j {0,1}
Let R be the set of documents known to be relevant to
query q
Let be the complement of R
Let be the probability that the document dj is
relevant to the query q
Let be the probability that the document dj is
nonelevant to query q
)|( jdRP
)|( jdRP
R
37.
38.
39. Pr(ki |R) stands for the probability that the index
term ki is present in a document randomly selected
from the set R
stands for the probability that the index
term ki is not present in a document randomly
selected from the set R
)|Pr( Rki
雅虎竞拍
煤炉代买
paypay商城
乐天二手
日本亚马逊
乐天新品
ZOZOTOWN
