a century of progress on information integration: a mid-term report
DESCRIPTION
A Century Of Progress On Information Integration: A Mid-Term Report. William W. Cohen Center for Automated Learning and Discovery (CALD), Carnegie Mellon University. Linkage. Queries. Querying integrated information sources (e.g. queries to views, execution of web-based queries, …) - PowerPoint PPT PresentationTRANSCRIPT
A Century Of Progress On Information Integration:
A Mid-Term Report
William W. CohenCenter for Automated Learning and Discovery
(CALD),Carnegie Mellon University
Information Integration
• Discovering information sources (e.g. deep web modeling, schema learning, …)
• Gathering data (e.g., wrapper learning & information extraction, federated search, …)
Queries
• Querying integrated information sources (e.g. queries to views, execution of web-based queries, …)
• Data mining & analyzing integrated information (e.g., collaborative filtering/classification learning using extracted data, …)
Linkage
• Cleaning data (e.g., de-duping and linking records) to form a single [virtual] database
[Science 1959]
Record linkage: bringing together of two or more separately recorded pieces of information concerning a particular individual or family (Dunn, 1946; Marshall, 1947).
…
…
…Very much like inverse document frequency (IDF) rule used in information retrieval.
Motivations for Record Linkage c. 1959
Record linkage is motivated by certain problems faced by a small number of scientists doing data analysis for obscure reasons.
In 1954, Popular Mechanics showed its readers what a home computer might look like in 2004 …
Information integration in 1959• Many of the basic principles of modern integration work
are recognizable.– Fellegi and Sunter, "A theory for record linkage", Journal of the American
Statistical Society, 1969
• Manual engineering of distance features (e.g., last names as Soundex codes) that are then matched probabilistically.– DB1 + DB2 DB12 + Pr(matches) + elbowGrease DB12
• Applied to records from pairs of datasets– “Smallest possible scale” for integration (one one dimension)
• Computationally expensive– Relative to ordinary database operations
• Narrowly used– Only for scientists in certain narrow areas (e.g., public health)
• Where have we come to now, in 2005?– [Hector’s heckling “how to we know when we’re finished?”]
Ted Kennedy's “Airport Adventure” [2004]
Washington -- Sen. Edward "Ted" Kennedy said Thursday that he was stopped and questioned at airports on the East Coast five times in March because his name appeared on the government's secret "no-fly" list…Kennedy was stopped because the name "T. Kennedy" has been used as an alias by someone on the list of terrorist suspects.
“…privately they [FAA officials] acknowledged being embarrassed that it took the senator and his staff more than three weeks to get his name removed.”
Florida Felon List [2000,2004]
The purge of felons from voter rolls has been a thorny issue since the 2000 presidential election. A private company hired to identify ineligible voters before the election produced a list with scores of errors, and elections supervisors used it to remove voters without verifying its accuracy…
The new list … contained few people identified as Hispanic; of the nearly 48,000 people on the list created by the Florida Department of Law Enforcement, only 61 were classified as Hispanics.
Gov. Bush said the mistake occurred because two databases that were merged to form the disputed list were incompatible. … when voters register in Florida, they can identify themselves as Hispanic. But the potential felons database has no Hispanic category…
The glitch in a state that President Bush won by just 537 votes could have been significant — because of the state's sizable Cuban population, Hispanics in Florida have tended to vote Republican… The list had about 28,000 Democrats and around 9,500 Republicans…
Information dealing with such matters as violent crime, organized crime, fraud and other white-collar crime may take days to be shared throughout the law enforcement community, according to an FBI official.
The new software program was supposed to allow agents to pass along intelligence and criminal information in real time….
In a response contained in the inspector general's report, the FBI pointed to its Investigative Data Warehouse…that provides … access to 47 sources of counterterrorism data, including information from FBI files, other government agencies and open-source news feeds.
..counter asymmetric threats by achieving total
information awareness…
Fishing in a sea of information
• Suppose you discover a pattern of events between a group of three people such that Pr(group is terroristCell | pattern) = 0.99999
• If you apply it to all three-person groups in the US, how many false positives will there be?
(250,000,000 * 50 * 49) * (1 – 0.99999) = _______ 612,500
And how many true positives?
Chinese Embassy Bombing [1999]• May 7, 1999: NATO bombs the Chinese Embassy in Belgrade with five
precision-guided bombs—sent to the wrong address—killing three.
“The Chinese embassy was mistaken for the intended target…located just 200 yards from the embassy. Reliance on an outdated map, aerial photos, and the extrapolation of the address of the federal directorate from number patterns on surrounding streets were cited … as causing the tragic error…despite the elaborate system of checks built-into the targeting protocol, the coordinates did not trigger an alarm because the three databases used in the process all had the old address.” [US-China Policy Foundation summary of the investigation]
“BEIJING, June 17 –– China today publicly rejected the U.S. explanation … [and] said the U.S. report ‘does not hold water.’” [Washington Post]
“The Chinese embassy was clearly marked on tourist maps that are on sale internationally, including in the English language. … Its address is listed in the Belgrade telephone directory…. For the CIA to have made such an elementary blunder is simply not plausible.” [World Socialist Web Site]
“Many observers believe that the bombing was deliberate…it if you believe that the bombing was an accident, you already believe in the far-fetched” [disinfo.com, July 2002].
Information integration in 2005
• Apparently, we still have work to do.• We fail to integrate information correctly
– “Ted Kennedy (D-MA)” ≠ “T. Kennedy, (T-IRA)”
• Crucial decisions are affected by these errors– Who can/can’t vote (felon list)– Where bombs are sent (Chinese embassy)
• Storing, linking, and analyzing information is a double-edged sword:– Loss of privacy and “fishing expeditions”
Information integration, 2005-2059
• Understanding sources of uncertainty, and propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
• Information integration for the great unwashed masses– Personal information, small stores of scientific
information, …• Using more information in linkage:
– Text, images, multiple interacting “hard” sources• Anonymous secure linkage; non-technical
limitations of how information can be combined, as well as distributed.
Information integration, 2005-2059
• Understanding sources of uncertainty, and propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
– When does a particular user believe that “X is the same thing as Y”? Does “the same thing” always mean the same thing?
– Is “X is the same entity as Y” always transitive?
When are two entities the same?
• Bell Labs• Bell Telephone Labs• AT&T Bell Labs• A&T Labs• AT&T Labs—Research• AT&T Labs Research,
Shannon Laboratory• Shannon Labs• Bell Labs Innovations• Lucent Technologies/Bell
Labs Innovations
History of Innovation: From 1925 to today, AT&T has attracted some of the world's greatest scientists, engineers and developers…. [www.research.att.com]
Bell Labs Facts: Bell Laboratories, the research and development arm of Lucent Technologies, has been operating continuously since 1925… [bell-labs.com]
[1925]
=
=
≠
Bell Telephone Labs
“Buddhism rejects the key element in folk psychology: the idea of a self (a unified personal identity that is continuous through time)…
King Milinda and Nagasena (the Buddhist sage) discuss … personal identity… Milinda gradually realizes that "Nagasena" (the word) does not stand for anything he can point to: … not … the hairs on Nagasena's head, nor the hairs of the body, nor the "nails, teeth, skin, muscles, sinews, bones, marrow, kidneys, ..." etc… Milinda concludes that "Nagasena" doesn't stand for anything… If we can't say what a person is, then how do we know a person is the same person through time? …
There's really no you, and if there's no you, there are no beliefs or desires for you to have… The folk psychology picture is profoundly misleading and believing it will make you miserable.” -S. LaFave
When are two entities are the same?
Passing linkage decisions along to the user
Usual Goal: link records and create a single highly accurate database for users query.
• Equality is often uncertain, given available information about an entity
– “name: T. Kennedy occupation: terrorist”
• The interpretation of “equality” may change from user to user and application to application
– Does “Boston Market” = “McDonalds” ?
– Alternate goal: wait for a query, then answer it, propogating uncertainty about linkage decisions on that query to the end user
LinkageQueries
Traditional approach:
Uncertainty about what to linkmust be decided by the integration
system, not the end user
Link items asneeded by Q
WHIRL vision:
Query Q
SELECT R.a,S.a,S.b,T.b FROM R,S,T
WHERE R.a=S.a and S.b=T.b
R.a S.a S.b T.b
Anhai Anhai Doan Doan
Dan Dan Weld Weld
Strongest links: those agreeable to most users
William Will Cohen Cohn
Steve Steven Minton Mitton
Weaker links: those agreeable to some users
William David Cohen Cohneven weaker links…
Link items asneeded by Q
WHIRL vision:
Query Q
SELECT R.a,S.a,S.b,T.b FROM R,S,T
WHERE R.a~S.a and S.b~T.b (~ TFIDF-similar)
R.a S.a S.b T.b
Anhai Anhai Doan Doan
Dan Dan Weld Weld
Incrementally produce a ranked list of possible links,
with “best matches” first. User (or downstream process)
decides how much of the list to generate and examine.
William Will Cohen Cohn
Steve Steven Minton Mitton
William David Cohen Cohn
DB1 + DB2 ≠ DB
WHIRL queries• Assume two relations:
review(movieTitle,reviewText): archive of reviews
listing(theatre, movieTitle, showTimes, …): now showing
The Hitchhiker’s Guide to the Galaxy, 2005
This is a faithful re-creation of the original radio series – not surprisingly, as Adams wrote the screenplay ….
Men in Black, 1997
Will Smith does an excellent job in this …
Space Balls, 1987
Only a die-hard Mel Brooks fan could claim to enjoy …
… …
Star Wars Episode III
The Senator Theater
1:00, 4:15, & 7:30pm.
Cinderella Man
The Rotunda Cinema
1:00, 4:30, & 7:30pm.
… … …
WHIRL queries
• “Find reviews of sci-fi comedies [movie domain]
FROM review SELECT * WHERE r.text~’sci fi comedy’
(like standard ranked retrieval of “sci-fi comedy”)
• “ “Where is [that sci-fi comedy] playing?”FROM review as r, LISTING as s, SELECT *
WHERE r.title~s.title and r.text~’sci fi comedy’
(best answers: titles are similar to each other – e.g., “Hitchhiker’s Guide to the Galaxy” and “The Hitchhiker’s Guide to the Galaxy, 2005” and the review text is similar to “sci-fi comedy”)
WHIRL queries• Similarity is based on TFIDF rare words are most important.• Search for high-ranking answers uses inverted indices….
The Hitchhiker’s Guide to the Galaxy, 2005
Men in Black, 1997
Space Balls, 1987
…
Star Wars Episode III
Hitchhiker’s Guide to the Galaxy
Cinderella Man
…
WHIRL queries• Similarity is based on TFIDF rare words are most important.• Search for high-ranking answers uses inverted indices….
The Hitchhiker’s Guide to the Galaxy, 2005
Men in Black, 1997
Space Balls, 1987
…
Star Wars Episode III
Hitchhiker’s Guide to the Galaxy
Cinderella Man
…
Years are common in the review archive, so have low weight
hitchhiker movie00137
the movie001,movie003,movie007,movie008, movie013,movie018,movie023,movie0031,
…..
- It is easy to find the (few) items that match on “important” terms
- Search for strong matches can prune “unimportant terms”
WHIRL results
• This sort of worked:– Interactive speeds
(<0.3s/q) with a few hundred thousand tuples.
– For 2-way joins, average precision (sort of like area
under precision-recall curve) from 85% to 100% on 13 problems in 6 domains.
– Average precision better than 90% on 5-way joins
WHIRL and soft integration
• WHIRL worked for a number of
web-based demo applications.– e.g., integrating data from 30-50
smallish web DBs with <1 FTE labor
• WHIRL could link many data types reasonably well, without engineering
• WHIRL generated numerous papers (Sigmod98, KDD98, Agents99, AAAI99, TOIS2000, AIJ2000, ICML2000, JAIR2001)
• WHIRL was relational– But see ELIXIR (SIGIR2001)
• WHIRL users need to know schema of source DBs
• WHIRL’s query-time linkage worked only for TFIDF, token-based distance metrics Text fields with few
misspellimgs
• WHIRL was memory-based– all data must be centrally stored
—no federated data. small datasets only
Link items asneeded by Q
WHIRL vision: very radical, everything was inter-dependent
Query Q
SELECT R.a,S.a,S.b,T.b FROM R,S,T
WHERE R.a~S.a and S.b~T.b (~ TFIDF-similar)
R.a S.a S.b T.b
Anhai Anhai Doan Doan
Dan Dan Weld Weld
Incrementally produce a ranked list of possible links,
with “best matches” first. User (or downstream process)
decides how much of the list to generate and examine.
William Will Cohen Cohn
Steve Steven Minton Mitton
William David Cohen Cohn
Information integration, 2005-2059
• Understanding sources of uncertainty, and propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
• Information integration for the great unwashed masses– Personal information, small stores of scientific
information, …• Using more information in linkage:
– Text, images, multiple interacting “hard” sources• Anonymous secure linkage; non-technical
limitations of how information can be combined, as well as distributed.
0
50
100
150
200
250
PublishBook
PublishWebsite
PublishBlog
Series1
0
50
100
150
200
250
PublishCorporate
Data
PublishScientific
Data
PublishPersonal
Data
Series1
Information integration, 2005-2059• Understanding sources of uncertainty, and
propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
• Information integration for the great unwashed masses– Personal information, small stores of scientific
information, …
• Needed:– Robust distance metrics that work “out of the box”– Methods to tune and combine these metrics
Robust distance metrics for strings
• Kinds of distances between s and t:– Edit-distance based (Levenshtein, Smith-
Waterman, …): distance is cost of cheapest sequence of edits that transform s to t.
– Term-based (TFIDF, Jaccard, DICE, …): distance based on set of words in s and t, usually weighting “important” words
– Which methods work best when?
Robust distance metrics for strings
• Java toolkit of string-matching methods from AI, Statistics, IR and DB communities
• Tools for evaluating performance on test data• Used to experimentally compare a number of metrics
SecondString (Cohen, Ravikumar, Fienberg, IIWeb 2003):
Results: Edit-distance variants Monge-Elkan (a carefully-tuned Smith-Waterman variant) is the best on average
across the benchmark datasets…
11-pt interpolated recall/precision curves averaged across 11 benchmark problems
Results: Edit-distance variants But Monge-Elkan is sometimes outperformed on specific datasets
Precision-recall for Monge-Elkan and one other method (Levenshtein) on a specific benchmark
SoftTFDF: A robust distance metric
We also compared edit-distance based and term-based methods, and evaluated a new “hybrid” method:
SoftTFIDF, for token sets S and T:• Extends TFIDF by including pairs of words in S and T that “almost” match—i.e., that are highly similar according to a second distance metric (the Jaro-Winkler metric, an edit-distance like metric).
Comparing token-based, edit-distance, and hybrid distance metrics
SFS is a vanilla IDF weight on each token (circa 1959!)
SoftTFIDF is a Robust Distance Metric
Information integration, 2005-2059
• Understanding sources of uncertainty, and propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
• Information integration for the great unwashed masses– Personal information, small stores of scientific
information, …• Needed:
– Robust distance metrics that work “out of the box”– Methods to tune and combine these metrics
Tuning and combining distance metrics using learning
• Why use a single distance metric? – Can you learn how to combine several distance
metrics—either distances across several fields (name, address) or several ways of measuring distance on the same field (edit distance, TFIDF?)
• [Bilenko & Mooney, KDD2003; Ravikumar, Cohen & Fienberg, UAI 2004]
– Can you learn the (many) parameters of an edit distance metric? (e.g., what is the cost of replacing “M” with “N” vs “M” with “V”?)
• [Ristad and Yianolis, PAMI’98; Bilenko & Mooney, KDD 2003]: learning edit distances using pair HMMs
Tuning and combining distance metrics using learning
• Pair HMM: a probabilistic automata that randomly emits a pair of letters at each clock tick.
• A sequence of letter pairs corresponds to a pair of strings:COHEN, COH_M = COHEN, COHM
• The parameters of the HMM can be tuned with extensions of the standard learning methods for HMMS– Training data is pairs of “true
matches”, e.g. Cohen/Cohm
(A,A) 0.002
(A,B) 0.000001
… …
(M,M) 0.0012
(M,N) 0.0003
… …
(M,X) 0.0000001
(M,_) 0.007
… …
A 1-state pair HMM
(C,C),(O,O),(H,H),(E,_),(N,M),…
Tuning and combining distance metrics using learning
(A,A) 0.002
(A,B) 0.000001
… …
(M,M) 0.0012
(M,N) 0.0003
… …
Ristad & Yianolis, 98
Bilenko & Mooney, 2003
Levenshtein-like edit distance Smith-Waterman-like “affine gap”edit distance
Tuning and combining distance metrics using learning
• Traditionally, multiple similarity measurements are combined using learning methods:– E.g., by clustering using a latent
binary “Match?” variable
– Independence assumptions are usually inappropriate: (e.g., same-address same-last-name)
• How to model dependencies?– Structural EM on a limited model
(Ravikumar, Cohen, Fienberg, UAI2004)
M
F1 F2 F3 F4
F1: JaroWinkler(name1,name2)
F2: Levenshtein(addr1,addr2)
…
Fk: SoftTFIDF(name1,name2)
Tuning and combining distance metrics using learning
• Traditionally, multiple similarity measurements are combined using learning methods:– E.g., by clustering using a latent
binary “Match?” variable
– Independence assumptions are usually inappropriate: (e.g., same-address same-last-name)
• How to model dependencies?– Structural EM on a limited model
(Ravikumar, Cohen, Fienberg, UAI2004)
F1: JaroWinkler(name1,name2)
F2: Levenshtein(addr1,addr2)
…
Fk: SoftTFIDF(name1,name2)
X1 X2 X3
F1 F2 F3
X4
F4
Mlatent
binaryvariables
dependencies allowed
monotone
fixedrelation
Tuning and combining distance metrics using unsupervised structural EM
Robust distance metrics, learnable using generative models (semi-/unsupervised learning)
• Summary:– None of these methods are
evaluated on as many integration problems as one would like
– Ravikumar et al structural EM method works well, but is computationally expensive
– Pair-HMM methods of Ristad & Yianolis, Bilenko & Mooney work well, but require “true matched pairs”
– Claim: could combine these by using pair-HMMs in inner loop of structural EM
• Practical well before 2059
X1 X2 X3
F1 F2 F3
X4
F4
Mlatent
binaryvariables
dependencies allowed
fixedrelation
Information integration, 2005-2059• Understanding sources of uncertainty, and
propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
• Information integration for the great unwashed masses– Personal information, small stores of scientific
information, …• Needed:
– Robust distance metrics that work “out of the box”– Methods to tune and combine these metrics– Ways to rapidly integrate new information sources with
unknown schemata
BANKS: Basic Data Model
• Database is modeled as a graph– Nodes = tuples– Edges = references between tuples
• foreign key, inclusion dependencies, ..
• Edges are directed.
MultiQuery Optimization
S. Sudarshan
Prasan Roy
writes
author
paper
Charuta
BANKS: Keyword search…
User need not know organization of database to formulate queries.
BANKS: Answer to Query
Query: “sudarshan roy” Answer: subtree from graph
MultiQuery Optimization
S. Sudarshan
Prasan Roy
writes writes
author author
paper
Other work on “structured search without schemata”
• BANKS (Browsing aNd Keyword Search): [Chakrabarti & others, VLDB 02, VLDB 05] – discussed above
• DataSpot (DTL)/Mercado Intuifind: [VLDB 98]• Proximity Search: [VLDB98]• Information units (linked Web pages): [WWW10]• Microsoft DBExplorer, Microsoft English query
QueriesLinkage
Discovery, Gathering, UnderstandingCombining BANKS-like search and WHIRL-like linkage combining DBs without schema understanding or data cleaning
Learning from user queries
Results expected
before 2059:
• Given a group of strings that refer to some external entities, cluster them so that strings in the same cluster refer to the same entity.
Bart Selmann
B. Selman
Bart Selman
Cornell Univ.
Critical behavior in satisfiability
Critical behavior for satisfiability
BLACKBOX theorem proving
Using multiple relations: “database hardening”
Using multiple relations: “database hardening”
Database S1 (extracted from paper 1’s title page):
Database S2 (extracted from paper 2’s bibliography):
Assumption: identical strings from the same source are co-referent (one sense per discourse?)
So this gives some known matches, which might interact with proposed matches: e.g. here we deduce...
Using multiple relations: “database hardening”
“Soft database” from IE:
“Hard database” suitable for Oracle, MySQL, etc
• (McAllister et al, KDD2000) Defined “hardening”:– Find “interpretation” (maps variant->name) that produces a
compact version of “soft” database S.– Probabilistic interpretation of hardening:
• Original “soft” data S is version of latent “hard” data H. • Hardening finds max likelihood H.
– Hardening is hard!• Optimal hardening is NP-hard.
– Greedy algorithm:• naive implementation is quadratic in |S|• clever data structures make it P(n log n), where n=|S|d
• Other related work:– Pasula et al, NIPS2002: more explicit generative Bayesian
formulation and MCMC method, experimental support– Wellner & McCallum 2004, Parag & Domingos 2004, Culotta &
McCallum 2005: discriminative models for multiple-relation linkage
Using multiple relations: “database hardening”
Information integration, 2005-2059
• Understanding sources of uncertainty, and propagating uncertainty to the end user.– “Soft” information integration– Driven by user’s goal and user’s queries
• Information integration for the great unwashed masses– Personal information, small stores of scientific
information, …
• Using more information in linkage:– Text, images, multiple interacting “hard” sources
Using free text:Information integration meets information extraction
QueriesLinkage
Traditionally, information extraction is viewed as “downstream” of integration—i.e., a separate process
When CraigKnoblock was here,he said that ISI wasworking on a wayto automatically…
Craig Knoblock
ISI
Alon Halevey University of Washington
… …
Information extraction
Using free text:Information integration meets information extraction
QueriesLinkage
Question: Can information from other databases (to which the extracted data will be linked) be used to help IE?
This is especially important if we care a lot about what the joins will be…
When CraigKnowblock was here, he said that ISI was working on a Way to automatically…
Craig Knowblock ISI
Alon Halevey University of Washington
… …
Information extraction
Craig A. Knoblock FETCH
Alon Y. Halevey NIMBLE
Thomas A. Edison Edison Power & Lighting
Using free text:Information integration meets information extraction
When CraigKnowblock was here, he said that ISI was working on a Way to automatically…
Craig Knowblock
Alon Halevey
…
Craig A. Knoblock
Alon Y. Halevey
Thomas A. Edison
Simplified question: how much can NER (named entity recognition) be improved using a list of known names?
What if I just care about the JOIN of the two lists of names? I.e., the goal is to “join” the blue table with the orange text?
Information Extraction with HMM-like methods
hall 0.11
center 0.06
bldg 0.02
... ...
House number Building Road City Zip
4089 Whispering Pines Nobel Drive San Diego CA 92122
State
State-of-the art in named entity recognition: HMM-like sequential word classifiers
I say Prof. F. Douglas at the San Diego zoo.
I saw
Person name: Craig Knoblock
Given a sequence of observations:
…and a trained HMMor CRF:
…find the most likely state sequence (Viterbi)
Any words determined to be generated by a “person name”state are extracted as a person name:
person name
location name
background
Prof. F. Douglas at the San Diego zoo
Features for information extraction
I met Prof. F. Douglas at the zoo
1 2 3 4 5 6 7 8
I met Prof F. Douglas at the zoo.
Other Other Person Person Person other other Location
t
x
y
Question: how can we guide this using a
dictionary D?
Simple answer: make membership in D a feature fd
Existing Markov models for IE• Feature vector for each position
• Examples
• Parameters: weight W for each feature (vector)
i-th labelWord i & neighbors
previous label
Problem
• Exact match to entry in D cannot handle abbreviations, misspellings, etc
Exploit state-of-the-art similarity metrics
edit distance, TFIDF match
Use similarity measures as feature values
Single-word classification prevents effective use of multi-word entities in dictionaries
Classify multi-word segments
Use Semi-Markov models
Proposed solution
Semi-markov models for information extraction
1 2 3 4 5 6 7 8
I met Prof. F. Douglas
at the zoo.
Other Other Person Person Person other other Location
l1=u1=1 l2=u2=2 l3=3, u3=5 l4=6,u4=6 l5=u5=7
l6=u6=8
I met Prof. F. Douglas at the zoo.
Other Other Person other other Location
t
x
y
l,u
x
y
COST: Requires additional search in ViterbiLearning and inference slower by O(maxNameLength)
Features for Semi-Markov models
j-th label Start of Sj previous
labelend of Sj
Internal dictionary: formed from training examples
External dictionary: from some column of an external DB
Using free text:Information integration meets information extraction
When CraigKnowblock was here, he said that ISI was working on a Way to automatically…
Craig Knowblock
Alon Halevey
…
Craig A. Knoblock
Alon Y. Halevey
Thomas A. Edison
Simplified question: how much can NER (named entity recognition) be improved using a list of known names? How well can we do at “joining to free text”?
Recent work:
• Kou et al, ISMB2005
• Cohen & Sarawagi, KDD2004
• Sarawagi & Cohen, NIPS2004
• Wellner et al, UAI2004
• Bunescu & Mooney, ACL2004
• Pasula et al, NIPS2003
• ….
Correspondence LDA
• Generative process
–Image first
–Caption second
• Caption topics are a subset of the image topics
• Enforces a correspondence between image segments and words that are associated with them.
Information Integration: today and tomorrow
• Discovering information sources: based on standards and free-text metadata.
• Data providers will be even more numerous.
• Gathering data: will get cheaper and cheaper
Queries
• Querying integrated information sources may be done in radically different query models
• Data mining & analyzing integrated information will be the norm, not the exception
Linkage
• Cleaning data to form a single virtual database will be guided by a user or group of users, and by characteristics of all the data