Mapping Domain Names to Categories

Mapping Domain Names to Categories

Maya Rotmensch, Sorcha Gilroy, Corina GurauAcademic Mentor: Cristina Garcia-Cardona

Industry Sponsor: Oversee.net (Kryztof Urban)

Institute of Pure and Applied MathematicsResearch in Industrial Projects

August 15, 2013

Institute for Pure & Applied Mathematics

University of California, Los Angeles

(Institute of Pure and Applied Mathematics) Mapping Domain Names to Categories August 15, 2013 1 / 41

Outline

1 Oversee.net

2 Problem StatementWhy so complicated?ESA - Explicit Semantic AnalysisHow Oversee.net Does It

3 Our ProjectOur FocusMethodologyResults

4 Concluding Remarks


Outline

1 Oversee.net





Oversee.net’s Business Model

Person Website


Person looking for games A gaming website



Person looking for games Domain A gaming website

Direct Navigation: when users navigate to a website by using theaddress bar instead of a search engine.

looking for a gaming website → navigates to ’addictinggamas.com’



Domain parking + traffic matching −→ Oversee.net

Person Domain Category Website

Monetized Domain Parking

I The registration of internet domain names without placing anycontent on the domain.

I Owners monetize traffic by displaying links and advertisements



AdvertisersI Partners of Oversee.net

I Choose the types of traffic they want from Oversee.net’s category tree



Parked domains do not have any content

Mapping Domains to Categories is extremely difficult

I Oversee.net uses Keywords to describe Domains and Categories

Domain Keywords Keywords Category

Not enough, as we are not guaranteed use of same language!


Outline

1 Oversee.net





So what’s the big deal?

Reasoning about concepts

Scarcity of input information

I Example 1 - Spelling errorcheapvacatins.com

I Example 2 - Ambiguous meaningbigbearhuts.com (animals? huts? it’s supposed to be winter sports)


Text Categorization

Our problem can be thought of as a problem of categorization. Weneed to assign a domain to one or more classes or categories

I A natural choice is topic modeling

I However, unlike most text categorization problems, we don’t actuallyhave documents to classify, as we are dealing with undevelopeddomains


Topic Modeling

This method analyzes the relationships between documents in a corpus byisolating a set of topics from the documents

For meaningful results, one must work with a set of large texts

I Our data set consists of keywords, as our domains are undeveloped

This method results in organic generation of topics

I The categories we are attempting to map into are pre-defined


ESA - Explicit Semantic AnalysisBuilding a Semantic Interpreter

Using a Vector Space Model + an exogeneous knowledge base−→ represent the meaning of text

1

# of articles ∼ 3.5 Million# of terms ∼ 45 Million

1Evgeniy Gabrilovich and Shaul Markovitch. Computing Semantic Relatedness using Wikipedia-based Explicit

Semantic Analysis, 2007. Proceedings of the 20th International Joint Conference on Artificial Intelligence (IJCAI)


ESA - Explicit Semantic Analysis

Government Finance Toys Children Bank School . . .

Law 0.2 0.3 0.8 0.9 0.2 0.7 . . .Article2 0.8 0.9 0.1 0.3 0.7 0.5 . . .Article3 0.5 0.2 0.3 0.6 0.4 0.8 . . .Article4 0.1 0.2 0.1 0.3 0.4 0.2 . . ....

......

......

......

...

Term frequency inverse document frequency:

tfidf (t, d ,D) = tf (t, d)× idf (t,D)

Logarithmically scaled term frequency:

tf (t, d) = log(f (t, d) + 1)

Inverse document frequency:

idf (t,D) = log|D|

|d ∈ D : t ∈ d |(Institute of Pure and Applied Mathematics) Mapping Domain Names to Categories August 15, 2013 15 / 41

ESA - Explicit Semantic AnalysisUsing a Semantic Interpreter

Cosine similarity measure

similarity = cos(θ) =A · B||A|| ||B||


How Oversee.net Does It

Instead of comparing two texts - compare two small sets of words!

Use keywords to describe domains and categories

Represent these keywords in terms of DBpedia articles

I A keyword is significantly related to an article if the TF-IDF is above acertain threshold

I The set of articles associated to a domain/category is the union of thesets of articles associated to its keywords


How Oversee Does It

Compare the two sets of articles (A - domains, B - categories) usingthe Jaccard Index:

J(A,B) =|A ∩ B||A ∪ B|

Categories with highest scores using this index are matched to adomain


Outline

1 Oversee.net





Our Focus

Domain Keywords Keywords Category

Critical link: domains to keywords

Improve quality of keywordsI Click Through Rate

I String Similarity

I Semantic Analysis

Keyword CTR String Similarity Semantic Similarity

industrial 20 80 0

industriel 20 89 0

industrie 20 100 0

china manufacturer 20 0 88

industries 20 80 98

industrial companies 20 0 86


Domain Keywords

Focusing on developing the link between domains and keywords, the twomain questions we posed for our research were:

Could we use ESA to extend the number of meaningful keywords perdomain?

Could we use the keywords obtained through Oversee.net inhousestatistics as the basis of the new keywords?


MethodologyExtending the set of keywords:


MethodologyExtending the set of keywords:

When generating new keywords:

Only take top 3 articles

Only take top 2 terms


MethodologyMethod 2 for extending the set of keywords:

Breaking up and correcting the domain name

chaselogon.com

haselogonaselogon

cha selogonchas elogonchase logonchasel ogonchaselo gon

chaselogchaselogo

Example: domain = ’chaselogon.com’

If entire string matches a word in reference file then stop

If both parts of broken string are exact words then stop

If substring is an exact word then correct other part using editdistances

I Corrections used: deletions, transpositions, replacements, insertions


MethodologyMethod 2 for extending the set of keywords:

Reference file made up of collections of text, have added moreinformation

I Company namesI Popular websitesI Brand and store namesI Countries and major cities

Initial Keywords Keywords after parsing

chameloeon chas

chase

elson

login


MethodologyGenerating new keywords and mapping to categories

bankfianancial.com

ncofinancialban

bankfinancial

financial institutionsfinancial centre

lobstersofficial personal

societies chairman. . .

Jaccard Index = 0.240492

finance

retirement pensiondebit card

tenant credit check...


credit cards

debit cardcredit applicationsrewards program

...


banking

savings bankingchecks

community bank...


Results: Comparing Their Keywords to Semantic

We were given a sample of 300 domains that had been matched byhand to a total of 500 categories

CTR & String Similarity CTR, String Similarity & Semantic Analysis

Number of matches 25 309

percentage of match 5% 61.8%


Results: Generating New Keywords

Using Method 1:

CTR & String Similarity Method 1 CTR & String Similarity & 7 Random

Number of matches 25 21 24

percentage of match 5% 4.2% 4.8%

Most of the time, the different methods yielded the same results

Cases where the new keywords improved the system:I thhetrainline.com

Cases where the base case did better:I inindustries.com


Results

thhetrainline.com

thetrainline

Jaccard Index = 0.0001 microcars & city cars

Jaccard Index = 0.0002 property management

thhetrainline.com

thetrainlinestrafe train

moving departingtrain station

telecommunicationsgeorgia

rain shine. . .

Jaccard Index = 0.1348 bus & rail

Jaccard Index = 0.2255 libraries & museums


Results

inindustries.com

industrialindustriasindustriel

. . .

Jaccard Index = 0.0786 manufacturing

inindustries.com

industrialindustriasindustriel

. . .ministry

quarterly garden/outdoorfilipino footballer

. . .

Jaccard Index = 0.099 tourist destinations

Jaccard Index = 0.1326 real estate


Results: Parsing the Domains

Using Method 1 & 2:

CTR & String Similarity Method 1 & 2 CTR & String Similarity & 15 Random

Number of matches 25 93 23

percentage of match 5% 18.6% 4.6%


Results - Parsing the Domains

chaselogon.com

chameloeon

No category matched

addictinggamas.com

chameloeonchaschaseelsonlogin

passwordjournalists cyberlogins expensive

beatles. . .

Jaccard Index =0.4637 credit cards

Jaccard Index = 0.4637 banking


Results: Parsing the Domains

Using Method 2:CTR & String Sim. Method 1& 2 Method 2

Number of matches 25 97 77 out of 356

percentage of match 5% 19.4% ∼ 21.6 %

Initial results show that overall, just using parsing might be more beneficial→ depends on the amount of noise.

Example with a lot of noise:I mobilestorage.ca

Example with minimal noise:I addictinggamas.com


Results - Amplification of noise

mobilestorage.ca

gfilestoragemobileshop

mobilestorage

ageinvestor

vilest. . .

Jaccard Index = 0.1011 mobile & wireless

Jaccard Index = 0.0959 music & audio

mobilestorage.ca

gfilestoragemobileshop

mobilestorage

ageinvestor

vilest. . .

legal agetaylor

phone companiesmobil

. . .

Jaccard Index =0.0942 music & audio

Jaccard Index = 0.0887 education


Results - Minimal noise

addictinggamas.com

addictinggamsaddictivegamesadictigegames

. . .addict

addictinggamesingram

. . .

Jaccard Index = 0.0153 software

addictinggamas.com

addictinggamsaddictivegamesadictigegames

. . .addict

addictinggamesingram

. . .gameplay requires

gameimpulsedriven flash

add ons. . .

Jaccard Index = 0.2019 computer & video games

Jaccard Index = 0.1975 games


Results: Extended Matches

Using Extended Matches:

We extended possible matches to parent and root nodes of thecategory tree.

I Checked in how many cases did the parent or root node of thecategories we got matched the manual matching.

CTR & String Sim. Method 1 Method 1& 2 Method 2

Number of matches 25 21 97 77 out of 356

percentage of match 5% 4.2% 19.4% ∼ 21.6 %

Number of extended matches 32 29 128 102 out of 356

Percentage of matches 6.4% 5.8% 25.6% ∼ 28.7 %


Outline

1 Oversee.net





Conclusion

Implemented a program to match domains with categories

Created an ESA based method to amplify existing keywords

Adapted a domain name parsing and spell correcting method

Revisiting our research questions:

Could we use ESA to extend the number of meaningful keywords perdomain? → Yes

Could we use the keywords obtained through Oversee.net inhousestatistics as the basis of the new keywords? → No. Or at leastfurther processing must be done.

getting better & more keywords → getting a few good keywords


Future Directions

Find out how many good initial keywords are required to use ourmethod successfully

Explore a better way of ranking keywords and determine which arethe most descriptive ones

I Click through rate and string similarity comparisons are not sufficientlydescriptive, need a better scoring method

Have a reference of the most popular websites, so that the domainsgiven could be compared to these

I Analyze content in websites to amplify domain to category mapping


Thank you!

Academic Mentor: Cristina Garcia-Cardona

Industry Sponsor: Kryztof Urban and Oversee.net

RIPS Director: Dr. Michael Raugh

Director of IPAM: Dr. Russ Caflisch

IPAM Staff: Dimi, Stacey, Stacy, Roland, Stephanie, and everyonethat made RIPS possible


Questions?

Thank you for listening!


Technology

Mapping Domain Names to Categories