VNSISPL_DBMS_Concepts_ch19

Chapter 19: Information Retrieval

Database System Concepts, 1 st Ed.
©VNS InfoSolutions Private Limited, Varanasi(UP), India 221002
See www.vnsispl.com for conditions on re-use 1

Chapter 19: Information Retrieval

s Relevance Ranking Using Terms
s Relevance Using Hyperlinks
s Synonyms., Homonyms, and Ontologies
s Indexing of Documents
s Measuring Retrieval Effectiveness
s Web Search Engines
s Information Retrieval and Structured Data
s Directories

Database System Concepts – 1 st Ed. 19.2 ©VNS InfoSolutions Private Limited, Varanasi(UP), India 22100

Information Retrieval Systems
s Information retrieval (IR) systems use a simpler data model than
database systems
q Information organized as a collection of documents
q Documents are unstructured, no schema
s Information retrieval locates relevant documents, on the basis of user
input such as keywords or example documents
q e.g., find documents containing the words “database systems”
s Can be used even on textual descriptions provided with non-textual
data such as images
s Web search engines are the most familiar example of IR systems


Information Retrieval Systems
(Cont.)
s Differences from database systems
q IR systems don’t deal with transactional updates (including
concurrency control and recovery)
q Database systems deal with structured data, with schemas that
define the data organization
q IR systems deal with some querying issues not generally
addressed by database systems
 Approximate searching by keywords
 Ranking of retrieved answers by estimated degree of relevance


Keyword Search
s In full text retrieval, all the words in each document are considered to be
keywords.
q We use the word term to refer to the words in a document
s Information-retrieval systems typically allow query expressions formed using
keywords and the logical connectives and, or, and not
q Ands are implicit, even if not explicitly specified
s Ranking of documents on the basis of estimated relevance to a query is critical
q Relevance ranking is based on factors such as
 Term frequency
– Frequency of occurrence of query keyword in document
 Inverse document frequency
– How many documents the query keyword occurs in
» Fewer  give more importance to keyword
 Hyperlinks to documents
– More links to a document  document is more important


Relevance Ranking Using Terms
s TF-IDF (Term frequency/Inverse Document frequency) ranking:
q Let n(d) = number of terms in the document d
q n(d, t) = number of occurrences of term t in the document d.
q Relevance of a document d to a term t

n(d, t)
TF (d, t) = log 1+
n(d)
 The log factor is to avoid excessive weight to frequent terms
q Relevance of document to query Q

r (d, Q) = ∑ TF (d, t)
t∈Q n(t)


Relevance Ranking Using Terms
(Cont.)
s Most systems add to the above model
q Words that occur in title, author list, section headings, etc. are
given greater importance
q Words whose first occurrence is late in the document are given
lower importance
q Very common words such as “a”, “an”, “the”, “it” etc are eliminated
 Called stop words
q Proximity: if keywords in query occur close together in the
document, the document has higher importance than if they occur
far apart
s Documents are returned in decreasing order of relevance score
q Usually only top few documents are returned, not all


Similarity Based Retrieval
s Similarity based retrieval - retrieve documents similar to a given
document
q Similarity may be defined on the basis of common words
 E.g. find k terms in A with highest TF (d, t ) / n (t ) and use
these terms to find relevance of other documents.
s Relevance feedback: Similarity can be used to refine answer set to
keyword query
q User selects a few relevant documents from those retrieved by
keyword query, and system finds other documents similar to these
s Vector space model: define an n-dimensional space, where n is the
number of words in the document set.
q Vector for document d goes from origin to a point whose i th
coordinate is TF (d,t ) / n (t )
q The cosine of the angle between the vectors of two documents is
used as a measure of their similarity.


Relevance Using Hyperlinks
s Number of documents relevant to a query can be enormous if only term
frequencies are taken into account
s Using term frequencies makes “spamming” easy
 E.g. a travel agency can add many occurrences of the words
“travel” to its page to make its rank very high
s Most of the time people are looking for pages from popular sites
s Idea: use popularity of Web site (e.g. how many people visit it) to rank
site pages that match given keywords
s Problem: hard to find actual popularity of site
q Solution: next slide


Relevance Using Hyperlinks (Cont.)
s Solution: use number of hyperlinks to a site as a measure of the popularity or
prestige of the site
q Count only one hyperlink from each site (why? - see previous slide)
q Popularity measure is for site, not for individual page
 But, most hyperlinks are to root of site
 Also, concept of “site” difficult to define since a URL prefix like
cs.yale.edu contains many unrelated pages of varying popularity
s Refinements
q When computing prestige based on links to a site, give more weight to
links from sites that themselves have higher prestige
 Definition is circular
 Set up and solve system of simultaneous linear equations
q Above idea is basis of the Google PageRank ranking mechanism


Relevance Using Hyperlinks (Cont.)
s Connections to social networking theories that ranked prestige of people
q E.g. the president of the U.S.A has a high prestige since many people
know him
q Someone known by multiple prestigious people has high prestige
s Hub and authority based ranking
q A hub is a page that stores links to many pages (on a topic)
q An authority is a page that contains actual information on a topic
q Each page gets a hub prestige based on prestige of authorities that
it points to
q Each page gets an authority prestige based on prestige of hubs
that point to it
q Again, prestige definitions are cyclic, and can be got by
solving linear equations
q Use authority prestige when ranking answers to a query


Synonyms and Homonyms
s Synonyms
q E.g. document: “motorcycle repair”, query: “motorcycle maintenance”
 need to realize that “maintenance” and “repair” are synonyms
q System can extend query as “motorcycle and (repair or maintenance)”
s Homonyms
q E.g. “object” has different meanings as noun/verb
q Can disambiguate meanings (to some extent) from the context
s Extending queries automatically using synonyms can be problematic
q Need to understand intended meaning in order to infer synonyms
 Or verify synonyms with user
q Synonyms may have other meanings as well


Concept-Based Querying
s Approach
q For each word, determine the concept it represents from context
q Use one or more ontologies:
 Hierarchical structure showing relationship between concepts
 E.g.: the ISA relationship that we saw in the E-R model
s This approach can be used to standardize terminology in a specific
field
s Ontologies can link multiple languages
s Foundation of the Semantic Web (not covered here)


Indexing of Documents
s An inverted index maps each keyword K to a set of documents S that
i i
contain the keyword
q Documents identified by identifiers
s Inverted index may record
q Keyword locations within document to allow proximity based ranking
q
Counts of number of occurrences of keyword to compute TF
s and operation: Finds documents that contain all of K1, K2, ..., Kn.
q Intersection S1∩ S2 ∩..... ∩ Sn
s or operation: documents that contain at least one of K1, K2, …, Kn
q union, S1∩ S2 ∩..... ∩ Sn,.
s Each Si is kept sorted to allow efficient intersection/union by merging
q “not” can also be efficiently implemented by merging of sorted lists


Measuring Retrieval Effectiveness
s Information-retrieval systems save space by using index structures
that support only approximate retrieval. May result in:
q false negative (false drop) - some relevant documents may
not be retrieved.
q false positive - some irrelevant documents may be retrieved.
q For many applications a good index should not permit any false
drops, but may permit a few false positives.
s Relevant performance metrics:
q precision - what percentage of the retrieved documents are
relevant to the query.
q recall - what percentage of the documents relevant to the query
were retrieved.


Measuring Retrieval Effectiveness (Cont.)
s Recall vs. precision tradeoff:
 Can increase recall by retrieving many documents (down to a low
level of relevance ranking), but many irrelevant documents would be
fetched, reducing precision
s Measures of retrieval effectiveness:
q Recall as a function of number of documents fetched, or
q Precision as a function of recall
 Equivalently, as a function of number of documents fetched
q E.g. “precision of 75% at recall of 50%, and 60% at a recall of 75%”
s Problem: which documents are actually relevant, and which are not


Web Search Engines
s Web crawlers are programs that locate and gather information on
the Web
q Recursively follow hyperlinks present in known documents, to find
other documents
 Starting from a seed set of documents
q Fetched documents
 Handed over to an indexing system
 Can be discarded after indexing, or store as a cached copy
s Crawling the entire Web would take a very large amount of time
q Search engines typically cover only a part of the Web, not all of it
q Take months to perform a single crawl


Web Crawling (Cont.)
s Crawling is done by multiple processes on multiple machines, running
in parallel
q Set of links to be crawled stored in a database
q New links found in crawled pages added to this set, to be crawled
later
s Indexing process also runs on multiple machines
q Creates a new copy of index instead of modifying old index
q Old index is used to answer queries
q After a crawl is “completed” new index becomes “old” index
s Multiple machines used to answer queries
q Indices may be kept in memory
q Queries may be routed to different machines for load balancing


Information Retrieval and Structured
Data
s Information retrieval systems originally treated documents as a
collection of words
s Information extraction systems infer structure from documents, e.g.:
q Extraction of house attributes (size, address, number of
bedrooms, etc.) from a text advertisement
q Extraction of topic and people named from a new article
s Relations or XML structures used to store extracted data
q System seeks connections among data to answer queries
q Question answering systems


Directories

s Storing related documents together in a library facilitates browsing
q users can see not only requested document but also related ones.
s Browsing is facilitated by classification system that organizes logically
related documents together.
s Organization is hierarchical: classification hierarchy


A Classification Hierarchy For A Library
System


Classification DAG

s Documents can reside in multiple places in a hierarchy in an
information retrieval system, since physical location is not important.
s Classification hierarchy is thus Directed Acyclic Graph (DAG)


A Classification DAG For A Library
Information Retrieval System


Web Directories
s A Web directory is just a classification directory on Web pages
q E.g. Yahoo! Directory, Open Directory project
q Issues:
 What should the directory hierarchy be?
 Given a document, which nodes of the directory are categories
relevant to the document
q Often done manually
 Classification of documents into a hierarchy may be done
based on term similarity


End of Chapter

Database System Concepts, 1 st Ed.
©VNS InfoSolutions Private Limited, Varanasi(UP), India 221002
See www.vnsispl.com for conditions on re-use 25

VNSISPL_DBMS_Concepts_ch19

More Related Content

Similar to VNSISPL_DBMS_Concepts_ch19 (20)

More from sriprasoon (13)

Recently uploaded (20)

VNSISPL_DBMS_Concepts_ch19