Web Information Extraction 3 rd Oct 2007. Information Extraction (Slides based on those by Ray...

Web Information Extraction

3rd Oct 2007

Information Extraction

(Slides based on those by Ray Mooney, Craig Knoblock, Dan Weld, Perry, Subbarao Kambhampati, Bing Liu)

Introduction

The Web is perhaps the single largest data source in the world.

Much of the Web (content) mining is about Data/information extraction from semi-structured objects

and free text, and Integration of the extracted data/information

Due to the heterogeneity and lack of structure, mining and integration are challenging tasks.

This talk gives an overview.

Introduction

Web mining aims to develop new techniques to extract useful knowledge from the Web

Web offers unprecedented opportunity and challenges to NLP Huge amount of information accessible Wide and diverse coverage Information of all types – structured table, text,

multimedia data … Semi-structured (html) Linked Redundant

Noisy. main content, advertisement, navigation panel, copyright notices, …

Surface Web and Deep Web Surface web: pages that can be browsed using q web

browser Deep web: databases accessible through parameterized

query interfaces Services. Dynamic Virtual Society. Interactions among people,

organizations, and systems.

Information Extraction (IE)

Identify specific pieces of information (data) in a unstructured or semi-structured textual document.

Transform unstructured information in a corpus of documents or web pages into a structured database.

Applied to different types of text: Newspaper articles Web pages Scientific articles Newsgroup messages Classified ads Medical notes

Information Extraction vs. NLP?

Information extraction is attempting to find some of the structure and meaning in the hopefully template driven web pages.

As IE becomes more ambitious and text becomes more free form, then ultimately we have IE becoming equal to NLP.

Web does give one particular boost to NLP Massive corpora..

Subject: US-TN-SOFTWARE PROGRAMMERDate: 17 Nov 1996 17:37:29 GMTOrganization: Reference.Com Posting ServiceMessage-ID: <56nigp$mrs@bilbo.reference.com>

SOFTWARE PROGRAMMER

Position available for Software Programmer experienced in generating software for PC-Based Voice Mail systems. Experienced in C Programming. Must be familiar with communicating with and controlling voice cards; preferable Dialogic, however, experience with others such as Rhetorix and Natural Microsystems is okay. Prefer 5 years or more experience with PC Based Voice Mail, but will consider as little as 2 years. Need to find a Senior level person who can come on board and pick up code with very little training. Present Operating System is DOS. May go to OS-2 or UNIX in future.

Please reply to:Kim AndersonAdNET(901) 458-2888 faxkimander@memphisonline.com

Subject: US-TN-SOFTWARE PROGRAMMERDate: 17 Nov 1996 17:37:29 GMTOrganization: Reference.Com Posting ServiceMessage-ID: <56nigp$mrs@bilbo.reference.com>

SOFTWARE PROGRAMMER

Position available for Software Programmer experienced in generating software for PC-Based Voice Mail systems. Experienced in C Programming. Must be familiar with communicating with and controlling voice cards; preferable Dialogic, however, experience with others such as Rhetorix and Natural Microsystems is okay. Prefer 5 years or more experience with PC Based Voice Mail, but will consider as little as 2 years. Need to find a Senior level person who can come on board and pick up code with very little training. Present Operating System is DOS. May go to OS-2 or UNIX in future.

Please reply to:Kim AndersonAdNET(901) 458-2888 faxkimander@memphisonline.com

Sample Job Posting

Extracted Job Template

computer_science_jobid: 56nigp$mrs@bilbo.reference.comtitle: SOFTWARE PROGRAMMERsalary:company:recruiter:state: TNcity:country: USlanguage: Cplatform: PC \ DOS \ OS-2 \ UNIXapplication:area: Voice Mailreq_years_experience: 2desired_years_experience: 5req_degree:desired_degree:post_date: 17 Nov 1996

Amazon Book Description….</td></tr></table>The Age of Spiritual Machines : When Computers Exceed Human Intelligence by <a href="/exec/obidos/search-handle-url/index=books&field-author= Kurzweil%2C%20Ray/002-6235079-4593641">Ray Kurzweil</a> <a href="http://images.amazon.com/images/P/0140282025.01.LZZZZZZZ.jpg"><img src="http://images.amazon.com/images/P/0140282025.01.MZZZZZZZ.gif" width=90 height=140 align=left border=0></a>List Price: $14.95 Our Price: $11.96 You Save: $2.99 (20%)

….</td></tr></table>The Age of Spiritual Machines : When Computers Exceed Human Intelligence by <a href="/exec/obidos/search-handle-url/index=books&field-author= Kurzweil%2C%20Ray/002-6235079-4593641">Ray Kurzweil</a> <a href="http://images.amazon.com/images/P/0140282025.01.LZZZZZZZ.jpg"><img src="http://images.amazon.com/images/P/0140282025.01.MZZZZZZZ.gif" width=90 height=140 align=left border=0></a>List Price: $14.95 Our Price: $11.96 You Save: $2.99 (20%) …

Extracted Book Template

Title: The Age of Spiritual Machines : When Computers Exceed Human IntelligenceAuthor: Ray KurzweilList-Price: $14.95Price: $11.96::

Product information/ Comparison shopping, etc.

Need to learn to extract info from online vendors Can exploit uniformity of layout, and (partial)

knowledge of domain by querying with known products Early e.g., Jango Shopbot (Etzioni and Weld)

Gives convenient aggregation of online content Bug: originally not popular with vendors

Make personal agents rather than web services? This seems to have changed (e.g., Froogle)

Commercial information…

Need thisprice

A book,Not a toy

Information Extraction

Information extraction systems Find and understand the limited relevant parts of texts

Clear, factual information (who did what to whom when?) Produce a structured representation of the relevant

information: relations (in the DB sense) Combine knowledge about language and a domain Automatically extract the desired information

E.g. Gathering earnings, profits, board members, etc. from

company reports Learn drug-gene product interactions from medical

research literature “Smart Tags” (Microsoft) inside documents

Using information extraction to populate knowledge bases

http://protege.stanford.edu/

The European Commission said on Thursday it disagreed with German advice.

Only France and Britain backed Fischler 's proposal .

“What we have to be extremely careful of is how other countries are going to take Germany 's lead”, Welsh National Farmers ' Union ( NFU ) chairman John Lloyd Jones said on BBC radio .

The European Commission said on Thursday it disagreed with German advice.

Only France and Britain backed Fischler 's proposal .

“What we have to be extremely careful of is how other countries are going to take Germany 's lead”, Welsh National Farmers ' Union ( NFU ) chairman John Lloyd Jones said on BBC radio .

Named Entity Extraction

The task: find and classify names in text, for example:

The purpose: … a lot of information is really associations between named entities. … for question answering, answers are usually named entities. … the same techniques apply to other slot-filling classifications.

The European Commission [ORG] said on Thursday it disagreed with German [MISC] advice.

Only France [LOC] and Britain [LOC] backed Fischler [PER] 's proposal .

“What we have to be extremely careful of is how other countries are going to take Germany 's lead”, Welsh National Farmers ' Union [ORG] ( NFU [ORG] ) chairman John Lloyd Jones [PER] said on BBC [ORG] radio .

CoNLL (2003) Named Entity Recognition task

Task: Predict semantic label of each word in text

Foreign NNP I-NP ORG

Ministry NNP I-NP ORG

spokesman NN I-NP O

Shen NNP I-NP PER

Guofang NNP I-NP PER

told VBD I-VP O

Reuters NNP I-NP ORG

: : : :

}Standard evaluationis per entity, not per token

Precision/Recall/F1 for IE

Recall and precision are straightforward for tasks like IR and text categorization, where there is only one grain size (documents)

The measure behaves a bit funnily for IE/NER when there are boundary errors (which are common): First Bank of Chicago announced earnings …

This counts as both a fp and a fn Selecting nothing would have been better Some other systems (e.g., MUC scorer) give partial

credit (according to complex rules)

Template Types

Slots in template typically filled by a substring from the document. Some slots may have a fixed set of pre-specified possible fillers that

may not occur in the text itself. Terrorist act: threatened, attempted, accomplished. Job type: clerical, service, custodial, etc. Company type: SEC code

Some slots may allow multiple fillers. Programming language

Some domains may allow multiple extracted templates per document. Multiple apartment listings in one ad

Task: Wrapper Induction

Learning wrappers is wrapper induction Sometimes, the relations are structural.

Web pages generated by a database. Tables, lists, etc.

Can’t computers automatically learn the patterns a human wrapper-writer would use?

Wrapper induction is usually regular relations which can be expressed by the structure of the document:

the item in bold in the 3rd column of the table is the price Wrapper induction techniques can also learn:

If there is a page about a research project X and there is a link near the word ‘people’ to a page that is about a person Y then Y is a member of the project X.

[e.g, Tom Mitchell’s Web->KB project]

Web Extraction

Many web pages are generated automatically from an underlying database.

Therefore, the HTML structure of pages is fairly specific and regular (semi-structured).

However, output is intended for human consumption, not machine interpretation.

An IE system for such generated pages allows the web site to be viewed as a structured database.

An extractor for a semi-structured web site is sometimes referred to as a wrapper.

Process of extracting from such pages is sometimes referred to as screen scraping.

Web Extraction using DOM Trees

Web extraction may be aided by first parsing web pages into DOM trees.

Extraction patterns can then be specified as paths from the root of the DOM tree to the node containing the text to extract.

May still need regex patterns to identify proper portion of the final CharacterData node.

Sample DOM Tree Extraction

Age of Spiritual Machines

Ray Kurzweil

Element

Character-DataHEADER

Title: HTMLBODYBCharacterDataAuthor: HTML BODYFONTA CharacterData

Wrappers: Simple Extraction Patterns

Specify an item to extract for a slot using a regular expression pattern. Price pattern: “\b\$\d+(\.\d{2})?\b”

May require preceding (pre-filler) pattern to identify proper context. Amazon list price:

Pre-filler pattern: “List Price: ” Filler pattern: “\$\d+(\.\d{2})?\b”

May require succeeding (post-filler) pattern to identify the end of the filler. Amazon list price:

Pre-filler pattern: “List Price: ” Filler pattern: “.+” Post-filler pattern: “”

Simple Template Extraction

Extract slots in order, starting the search for the filler of the n+1 slot where the filler for the nth slot ended. Assumes slots always in a fixed order. Title Author List price …

Make patterns specific enough to identify each filler always starting from the beginning of the document.

Pre-Specified Filler Extraction

If a slot has a fixed set of pre-specified possible fillers, text categorization can be used to fill the slot. Job category Company type

Treat each of the possible values of the slot as a category, and classify the entire document to determine the correct filler.

Wrapper induction

Highly regularsource documents

Relatively simple

extraction patterns

Efficient

learning algorithm

Writing accurate patterns for each slot for each domain (e.g. each web site) requires laborious software engineering.

Alternative is to use machine learning: Build a training set of

documents paired with human-produced filled extraction templates.

Learn extraction patterns for each slot using an appropriate machine learning algorithm.

Learning for IE

Writing accurate patterns for each slot for each domain (e.g. each web site) requires laborious software engineering.

Alternative is to use machine learning: Build a training set of documents paired with human-produced

filled extraction templates. Learn extraction patterns for each slot using an appropriate

machine learning algorithm.

Use , , , for extraction

<HTML><TITLE>Some Country Codes</TITLE>Congo 242 Egypt 20 Belize 501 Spain 34 </BODY></HTML>

Wrapper induction: Delimiter-based extraction

l1, r1, …, lK, rK

Example: Find 4 strings

, , , l1 , r1 , l2 , r2

labeled pages wrapper<HTML><HEAD>Some Country Codes</HEAD>Congo 242 Egypt 20 Belize 501 Spain 34 </BODY></HTML>

<HTML><HEAD>Some Country Codes</HEAD>Congo 242 Egypt 20 Belize 501 Spain 34 </BODY></HTML>

Learning LR wrappers

LR: Finding r1

r1 can be any prefix

eg

LR: Finding l1, l2 and r2

r2 can be any prefix

eg

l2 can be any suffix

eg

l1 can be any suffix

eg

Distracting text in head and tail <HTML><TITLE>Some Country Codes</TITLE>

<BODY>Some Country Codes Congo 242 Egypt 20 Belize 501 Spain 34 <HR>End</BODY></HTML>

A problem with LR wrappers

Ignore page’s head and tail

<HTML><TITLE>Some Country Codes</TITLE><BODY>Some Country CodesCongo 242 Egypt 20 Belize 501 Spain 34 <HR>End</BODY></HTML>

start of tail

end of head

Head-Left-Right-Tail wrappers

One (of many) solutions: HLRT

More sophisticated wrappers

LR and HLRT wrappers are extremely simple Though applicable to many tabular patterns

Recent wrapper induction research has explored more expressive wrapper classes [Muslea et al, Agents-98; Hsu et al, JIS-98; Kushmerick, AAAI-1999; Cohen, AAAI-1999; Minton et al, AAAI-2000] Disjunctive delimiters Multiple attribute orderings Missing attributes Multiple-valued attributes Hierarchically nested data Wrapper verification and maintenance

Boosted wrapper induction

Wrapper induction is only ideal for rigidly-structured machine-generated HTML…

… or is it?! Can we use simple patterns to extract from natural

language documents?

… Name: Dr. Jeffrey D. Hermes … … Who: Professor Manfred Paul …... will be given by Dr. R. J. Pangborn …

… Ms. Scott will be speaking …… Karen Shriver, Dept. of ... … Maria Klawe, University of ...

BWI: The basic idea

Learn “wrapper-like” patterns for texts pattern = exact token sequence

Learn many such “weak” patterns Combine with boosting to build “strong” ensemble

pattern Boosting is a popular recent machine learning method where

many weak learners are combined Demo: http://www.smi.ucd.ie/bwi Not all natural text is sufficiently regular for exact string

matching to work well!!

Natural Language Processing-based Information Extraction

If extracting from automatically generated web pages, simple regex patterns usually work.

If extracting from more natural, unstructured, human-written text, some NLP may help. Part-of-speech (POS) tagging

Mark each word as a noun, verb, preposition, etc. Syntactic parsing

Identify phrases: NP, VP, PP Semantic word categories (e.g. from WordNet)

KILL: kill, murder, assassinate, strangle, suffocate Extraction patterns can use POS or phrase tags.

Crime victim: Prefiller: [POS: V, Hypernym: KILL] Filler: [Phrase: NP]

Stalker: A wrapper induction system (Muslea et al. Agents-99)

E1:513 Pico, Venice, Phone 1-800-555-1515E2:90 Colfax, Palms, Phone (800) 508-1570E3: 523 1st St., LA, Phone 1-800-578-2293E4: 403 La Tijera, Watts, Phone: (310) 798-0008

We want to extract area code. Start rules:

R1: SkipTo(()R2: SkipTo(-)

End rules:R3: SkipTo())R4: SkipTo()

Learning extraction rules

Stalker uses sequential covering to learn extraction rules for each target item. In each iteration, it learns a perfect rule that covers as

many positive items as possible without covering any negative items.

Once a positive item is covered by a rule, the whole example is removed.

The algorithm ends when all the positive items are covered. The result is an ordered list of all learned rules.

Rule induction through an example

Training examples:E1: 513 Pico, Venice, Phone 1-800-555-1515E2: 90 Colfax, Palms, Phone (800) 508-1570E3: 523 1st St., LA, Phone 1-800-578-2293E4: 403 La Tijera, Watts, Phone: (310) 798-0008

We learn start rule for area code. Assume the algorithm starts with E2. It creates three initial

candidate rules with first prefix symbol and two wildcards: R1: SkipTo(() R2: SkipTo(Punctuation) R3: SkipTo(Anything)

R1 is perfect. It covers two positive examples but no negative example.

Rule induction (cont …)

E1: 513 Pico, Venice, Phone 1-800-555-1515E2: 90 Colfax, Palms, Phone (800) 508-1570E3: 523 1st St., LA, Phone 1-800-578-2293E4: 403 La Tijera, Watts, Phone: (310) 798-0008

R1 covers E2 and E4, which are removed. E1 and E3 need additional rules.

Three candidates are created: R4: SkiptTo() R5: SkipTo(HtmlTag) R6: SkipTo(Anything)

None is good. Refinement is needed. Stalker chooses R4 to refine, i.e., to add additional symbols, to

specialize it. It will find R7: SkipTo(-), which is perfect.

Limitations of Supervised Learning

Manual Labeling is labor intensive and time consuming, especially if one wants to extract data from a huge number of sites.

Wrapper maintenance is very costly: If Web sites change frequently It is necessary to detect when a wrapper stops to work

properly. Any change may make existing extraction rules

invalid. Re-learning is needed, and most likely manual re-

labeling as well.

Road map Structured data extraction

Wrapper induction Automatic extraction

Information integration Summary

The RoadRunner System(Crescenzi et al. VLDB-01)

Given a set of positive examples (multiple sample pages). Each contains one or more data records.

From these pages, generate a wrapper as a union-free regular expression (i.e., no disjunction).

The approach To start, a sample page is taken as the wrapper. The wrapper is then refined by solving mismatches between the

wrapper and each sample page, which generalizes the wrapper.

Compare with wrapper induction

No manual labeling, but need a set of positive pages of the same template which is not necessary for a page with multiple data records

not wrapper for data records, but pages. A Web page can have many pieces of irrelevant information.

Issues of automatic extraction Hard to handle disjunctions Hard to generate attribute names for the extracted data. extracted data from multiple sites need integration, manual or

automatic.

The DEPTA system (Zhai & Liu WWW-05)

Data region1

Data region2

A data record

Align and extract data items (e.g., region1)image1 EN7410 17-

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1. Mining Data Records(Liu et al, KDD-03; Zhai and Liu, WWW-05)

Given a single page with multiple data records (a list page), it extracts data records.

The algorithm is based on two observations about data records in a Web page a string matching algorithm (tree matching ok too)

Considered both contiguous non-contiguous data records

The Approach

Given a page, three steps: Building the HTML Tag Tree

Erroneous tags, unbalanced tags, etc Some problems are hard to fix

Mining Data Regions Spring matching or tree matching

Identifying Data Records

Rendering (or visual) information is very useful in the whole process

Building tree based on visual cues

1 <table>

2 <tr>

3 <td> … </td>

4 <td> … </td>

5 </tr>

6 <tr>

7 <td> … </td>

8 <td> … </td>

9 </tr>

10</table>

left right top bottom100 300 200 400

100 300 200 300

100 200 200 300

200 300 200 300

100 300 300 400

100 200 300 400

200 300 300 400

td td td td

The tag treetable

Mining Data Regions

Region 2

14 15 16 17 191813 20

Region 1

Region 3

Identify Data Records

A generalized node may not be a data record.

Extra mechanisms are needed to identify true atomic objects (see the papers).

Some highlights: Contiguous non-contiguous data records.

Name 1

Description of object 1

Name 2

Name 3

Name 4

Name 1 Name 2

Name 3 Name 4

2. Extract Data from Data Records

Once a list of data records are identified, we can align and extract data items in them.

Approaches (align multiple data records): Multiple string alignment

Many ambiguities due to pervasive use of table related tags. Multiple tree alignment (partial tree alignment)

Together with visual information is effective Most multiple alignment methods work like hierarchical

clustering, Not effective, and very expensive

Tree Matching (tree edit distance)

Intuitively, in the mapping each node can appear no more than once in a mapping, the order between sibling nodes are preserved, and the hierarchical relation between nodes are also preserved.

The Partial Tree Alignment approach

Choose a seed tree: A seed tree, denoted by Ts, is picked with the maximum number of data items.

Tree matching:

For each unmatched tree Ti (i ≠ s), match Ts and Ti. Each pair of matched nodes are linked (aligned). For each unmatched node nj in Ti do

expand Ts by inserting nj into Ts if a position for insertion can be uniquely determined in Ts.

The expanded seed tree Ts is then used in subsequent matching.

a b e dc eb

pNew part of Ts

e ab x

p pTsTi

Insertion is possible

Insertion is not possible

Illustration of partial tree alignment

dx… b

c k gn

dx… b

kcx…

c k gn

nx… b

c d h k

No node inserted

New Ts

d h kc

c, h, and k inserted

Ts = T1

T2 is matched again

A complete example

Output Data Table

… x b n c d h k g

T1 … 1 1 1

T2 1 1 1 1 1

T3 1 1 1 1 1

DEPTA does not work with nested data records.

NET (Liu & Zhai, WISE-05)extracts data from both flat and nested data records.

Some other systems and techniques

IEPAD (Chang & Lui WWW-01), DeLa (Wang & Lochovsky WWW-03) These systems treat a page as a long string, and find repeated

substring patterns. They often produce multiple patterns (rules). Hard to decide

which is correct. EXALG(Arasu & Garcia-Molina SIGMOD-03), (Lerman et al,

SIGMOD-04). Require multiple pages to find patterns. Which is not necessary for pages with multiple records.

(Zhao et al, WWW-04) It extracts data records in one area of a page.

Limitations and issues

Not for a page with only a single data record Does not generate attribute names for the extracted data (yet!) extracted data from multiple sites need integration. It is possible in each specific application domain, e.g.,

products sold online. need “product name”, “image”, and “price”. identify only these three fields may not be too hard.

Job postings, publications, etc …

Road map

Structured data extraction Wrapper induction Automatic extraction

Web query interface integration

Many integration tasks, Integrating Web query interfaces (search forms) Integrating extracted data Integrating textual information Integrating ontologies (taxonomy) …

We only introduce integration of query interfaces. Many web sites provide forms to query deep web Applications: meta-search and meta-query

Global Query Interface

united.com airtravel.com

delta.com hotwire.com

Synonym Discovery (He and Chang, KDD-04) Discover synonym attributes

Author – Writer, Subject – Category

Holistic Model Discovery

author name subject categorywriter

S2:writertitlecategoryformat

S3:nametitlekeywordbinding

S1:authortitlesubjectISBN

Pairwise Attribute Correspondence

S2:writertitlecategoryformat

S3:nametitlekeywordbinding

S1:authortitlesubjectISBN

S1.author S3.nameS1.subject S2.category

Schema matching as correlation mining

Across many sources: Synonym attributes are negatively correlated

synonym attributes are semantically alternatives. thus, rarely co-occur in query interfaces

Grouping attributes with positive correlation grouping attributes semantically complement thus, often co-occur in query interfaces

1. Positive correlation mining as potential groups

2. Negative correlation mining as potential matchings

Mining positive correlations

Last Name, First Name

Mining negative correlationsAuthor =

{Last Name, First Name}

3. Matching selection as model constructionAuthor (any) = {Last Name, First Name}

Subject = Category

Format = Binding

A clustering approach to schema matching (Wu et al. SIGMOD-04)

1:1 mapping by clustering Bridging effect

“a2” and “c2” might not look similar themselves but they might both be similar to “b3”

1:m mappings Aggregate and is-a types

User interaction helps in: learning of matching thresholds resolution of uncertain mappings

Find 1:1 Mappings via ClusteringInterfaces:

After one merge:

…, final clusters:

{{a1,b1,c1}, {b2,c2},{a2},{b3}}

Initial similarity matrix:

Similarity functions linguistic similarity domain similarity

Find 1:m Complex Mappings

Aggregate type – contents of fields on the many side are part ofthe content of field on the one side

Commonalities – (1) field proximity, (2) parent label similarity, and (3) value characteristics

Complex Mappings (Cont’d)

Is-a type – contents of fields on the many side are sum/union ofthe content of field on the one side

Commonalities – (1) field proximity, (2) parent label similarity,and (3) value characteristics

Instance-based matching via query probing (Wang et al. VLDB-04) Both query interfaces and returned results (called

instances) are considered in matching. It assumes a global schema (GS) is given and a set of instances are also given.

Uses each instance value (V) in GS to probe the underlying database to obtain the count of V appeared in the returned results. These counts are used to help matching.

Query interface and result page

Search InterfaceResult Page

Format

Publish Date

Publisher

Author

Data Attributes

Road map Structured data extraction

Wrapper Induction Automatic extraction

Summary Give an overview of two topics

Structured data extraction Information integration

Some technologies are ready for industrial exploitation, e.g., data extraction.

Simple integration is do-able, complex integration still needs further research.

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