SEARCH ENGINE INDEXING collects, parses, and stores data to
facilitate fast and accurate information retrieval . Index design
incorporates interdisciplinary concepts from linguistics, cognitive
psychology, mathematics, informatics , and computer science. An
alternate name for the process in the context of search engines
designed to find web pages on the
Popular engines focus on the full-text indexing of online, natural language documents. Media types such as video and audio and graphics are also searchable.
Meta search engines reuse the indices of other services and do not store a local index, whereas cache-based search engines permanently store the index along with the corpus . Unlike full-text indices, partial-text services restrict the depth indexed to reduce index size. Larger services typically perform indexing at a predetermined time interval due to the required time and processing costs, while agent -based search engines index in real time .
* 1 Indexing
* 1.1 Index design factors * 1.2 Index data structures * 1.3 Challenges in parallelism * 1.4 Inverted indices * 1.5 Index merging * 1.6 The forward index * 1.7 Compression
* 2 Document parsing
* 2.1 Challenges in natural language processing
* 2.2 Tokenization
* 3 See also * 4 References * 5 Further reading
The purpose of storing an index is to optimize speed and performance in finding relevant documents for a search query. Without an index, the search engine would scan every document in the corpus, which would require considerable time and computing power. For example, while an index of 10,000 documents can be queried within milliseconds, a sequential scan of every word in 10,000 large documents could take hours. The additional computer storage required to store the index, as well as the considerable increase in the time required for an update to take place, are traded off for the time saved during information retrieval.
INDEX DESIGN FACTORS
Major factors in designing a search engine's architecture include: Merge factors How data enters the index, or how words or subject features are added to the index during text corpus traversal, and whether multiple indexers can work asynchronously. The indexer must first check whether it is updating old content or adding new content. Traversal typically correlates to the data collection policy. Search engine index merging is similar in concept to the SQL Merge command and other merge algorithms. Storage techniques How to store the index data , that is, whether information should be data compressed or filtered. Index size How much computer storage is required to support the index. Lookup speed How quickly a word can be found in the Inverted index . The speed of finding an entry in a data structure, compared with how quickly it can be updated or removed, is a central focus of computer science. Maintenance How the index is maintained over time. Fault tolerance How important it is for the service to be reliable. Issues include dealing with index corruption, determining whether bad data can be treated in isolation, dealing with bad hardware, partitioning , and schemes such as hash-based or composite partitioning, as well as replication .
INDEX DATA STRUCTURES
CHALLENGES IN PARALLELISM
A major challenge in the design of search engines is the management of serial computing processes. There are many opportunities for race conditions and coherent faults. For example, a new document is added to the corpus and the index must be updated, but the index simultaneously needs to continue responding to search queries. This is a collision between two competing tasks. Consider that authors are producers of information, and a web crawler is the consumer of this information, grabbing the text and storing it in a cache (or corpus ). The forward index is the consumer of the information produced by the corpus, and the inverted index is the consumer of information produced by the forward index. This is commonly referred to as a PRODUCER-CONSUMER MODEL. The indexer is the producer of searchable information and users are the consumers that need to search. The challenge is magnified when working with distributed storage and distributed processing. In an effort to scale with larger amounts of indexed information, the search engine's architecture may involve distributed computing , where the search engine consists of several machines operating in unison. This increases the possibilities for incoherency and makes it more difficult to maintain a fully synchronized, distributed, parallel architecture.
Many search engines incorporate an inverted index when evaluating a search query to quickly locate documents containing the words in a query and then rank these documents by relevance. Because the inverted index stores a list of the documents containing each word, the search engine can use direct access to find the documents associated with each word in the query in order to retrieve the matching documents quickly. The following is a simplified illustration of an inverted index:
Inverted Index WORD DOCUMENTS
the Document 1, Document 3, Document 4, Document 5, Document 7
cow Document 2, Document 3, Document 4
says Document 5
moo Document 7
This index can only determine whether a word exists within a particular document, since it stores no information regarding the frequency and position of the word; it is therefore considered to be a boolean index. Such an index determines which documents match a query but does not rank matched documents. In some designs the index includes additional information such as the frequency of each word in each document or the positions of a word in each document. Position information enables the search algorithm to identify word proximity to support searching for phrases; frequency can be used to help in ranking the relevance of documents to the query. Such topics are the central research focus of information retrieval .
The inverted index is a sparse matrix , since not all words are present in each document. To reduce computer storage memory requirements, it is stored differently from a two dimensional array . The index is similar to the term document matrices employed by latent semantic analysis . The inverted index can be considered a form of a hash table. In some cases the index is a form of a binary tree , which requires additional storage but may reduce the lookup time. In larger indices the architecture is typically a distributed hash table .
The inverted index is filled via a merge or rebuild. A rebuild is similar to a merge but first deletes the contents of the inverted index. The architecture may be designed to support incremental indexing, where a merge identifies the document or documents to be added or updated and then parses each document into words. For technical accuracy, a merge conflates newly indexed documents, typically residing in virtual memory, with the index cache residing on one or more computer hard drives.
After parsing, the indexer adds the referenced document to the document list for the appropriate words. In a larger search engine, the process of finding each word in the inverted index (in order to report that it occurred within a document) may be too time consuming, and so this process is commonly split up into two parts, the development of a forward index and a process which sorts the contents of the forward index into the inverted index. The inverted index is so named because it is an inversion of the forward index.
THE FORWARD INDEX
The forward index stores a list of words for each document. The following is a simplified form of the forward index:
Forward Index DOCUMENT WORDS
Document 1 the,cow,says,moo
Document 2 the,cat,and,the,hat
Document 3 the,dish,ran,away,with,the,spoon
The rationale behind developing a forward index is that as documents are parsed, it is better to immediately store the words per document. The delineation enables Asynchronous system processing, which partially circumvents the inverted index update bottleneck. The forward index is sorted to transform it to an inverted index. The forward index is essentially a list of pairs consisting of a document and a word, collated by the document. Converting the forward index to an inverted index is only a matter of sorting the pairs by the words. In this regard, the inverted index is a word-sorted forward index.
Generating or maintaining a large-scale search engine index
represents a significant storage and processing challenge. Many search
engines utilize a form of compression to reduce the size of the
indices on disk . Consider the following scenario for a full text,
* It takes 8 bits (or 1 byte ) to store a single character. Some encodings use 2 bytes per character * The average number of characters in any given word on a page may be estimated at 5 (Wikipedia:Size comparisons )
Given this scenario, an uncompressed index (assuming a non-conflated , simple, index) for 2 billion web pages would need to store 500 billion word entries. At 1 byte per character, or 5 bytes per word, this would require 2500 gigabytes of storage space alone. This space requirement may be even larger for a fault-tolerant distributed storage architecture. Depending on the compression technique chosen, the index can be reduced to a fraction of this size. The tradeoff is the time and processing power required to perform compression and decompression.
Notably, large scale search engine designs incorporate the cost of storage as well as the costs of electricity to power the storage. Thus compression is a measure of cost.
Document parsing breaks apart the components (words) of a document or other form of media for insertion into the forward and inverted indices. The words found are called _tokens_, and so, in the context of search engine indexing and natural language processing , parsing is more commonly referred to as tokenization . It is also sometimes called word boundary disambiguation , tagging , text segmentation , content analysis , text analysis, text mining , concordance generation, speech segmentation , lexing , or lexical analysis . The terms 'indexing', 'parsing', and 'tokenization' are used interchangeably in corporate slang.
Natural language processing is the subject of continuous research and technological improvement. Tokenization presents many challenges in extracting the necessary information from documents for indexing to support quality searching. Tokenization for indexing involves multiple technologies, the implementation of which are commonly kept as corporate secrets.
CHALLENGES IN NATURAL LANGUAGE PROCESSING
Word Boundary Ambiguity Native English speakers may at first
consider tokenization to be a straightforward task, but this is not
the case with designing a multilingual indexer. In digital form, the
texts of other languages such as Chinese , Japanese or Arabic
represent a greater challenge, as words are not clearly delineated by
whitespace . The goal during tokenization is to identify words for
which users will search. Language-specific logic is employed to
properly identify the boundaries of words, which is often the
rationale for designing a parser for each language supported (or for
groups of languages with similar boundary markers and syntax).
Unlike literate humans, computers do not understand the structure of a natural language document and cannot automatically recognize words and sentences. To a computer, a document is only a sequence of bytes. Computers do not 'know' that a space character separates words in a document. Instead, humans must program the computer to identify what constitutes an individual or distinct word referred to as a token. Such a program is commonly called a tokenizer or parser or lexer . Many search engines, as well as other natural language processing software, incorporate specialized programs for parsing, such as YACC or Lex .
During tokenization, the parser identifies sequences of characters which represent words and other elements, such as punctuation, which are represented by numeric codes, some of which are non-printing control characters. The parser can also identify entities such as email addresses, phone numbers, and URLs . When identifying each token, several characteristics may be stored, such as the token's case (upper, lower, mixed, proper), language or encoding, lexical category (part of speech, like 'noun' or 'verb'), position, sentence number, sentence position, length, and line number.
If the search engine supports multiple languages, a common initial
step during tokenization is to identify each document's language; many
of the subsequent steps are language dependent (such as stemming and
part of speech tagging).
If the search engine supports multiple document formats , documents
must be prepared for tokenization. The challenge is that many document
formats contain formatting information in addition to textual content.
Options for dealing with various formats include using a publicly available commercial parsing tool that is offered by the organization which developed, maintains, or owns the format, and writing a custom parser .
Some search engines support inspection of files that are stored in a compressed or encrypted file format. When working with a compressed format, the indexer first decompresses the document; this step may result in one or more files, each of which must be indexed separately. Commonly supported compressed file formats include:
* ZIP - Zip archive file
* RAR - Roshal ARchive file
* CAB -
Microsoft Windows Cabinet File
Gzip - File compressed with gzip
* BZIP - File compressed using bzip2
* Tape ARchive (TAR) ,
Format analysis can involve quality improvement methods to avoid including 'bad information' in the index. Content can manipulate the formatting information to include additional content. Examples of abusing document formatting for spamdexing :
* Including hundreds or thousands of words in a section which is
hidden from view on the computer screen, but visible to the indexer,
by use of formatting (e.g. hidden "div" tag in
Some search engines incorporate section recognition, the
identification of major parts of a document, prior to tokenization.
Not all the documents in a corpus read like a well-written book,
divided into organized chapters and pages. Many documents on the web ,
such as newsletters and corporate reports, contain erroneous content
and side-sections which do not contain primary material (that which
the document is about). For example, this article displays a side menu
with links to other web pages. Some file formats, like
* Content in different sections is treated as related in the index, when in reality it is not * Organizational 'side bar' content is included in the index, but the side bar content does not contribute to the meaning of the document, and the index is filled with a poor representation of its documents.
Section analysis may require the search engine to implement the
rendering logic of each document, essentially an abstract
representation of the actual document, and then index the
representation instead. For example, some content on the
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Indexing often has to recognize the
META TAG INDEXING
Specific documents often contain embedded meta information such as
author, keywords, description, and language. For
In Desktop search , many solutions incorporate meta tags to provide a way for authors to further customize how the search engine will index content from various files that is not evident from the file content. Desktop search is more under the control of the user, while Internet search engines must focus more on the full text index.
Compound term processing
* Document Retrieval
Full text search
* Keyword In Context Indexing
Latent semantic indexing
List of search engines
Natural language processing
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* v * t * e
* Local search