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Extended Keyword Index & Improved Search

for Semantic e-Catalog

이동주

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Contents

Motivation Semantic e-Catalog Search In e-Catalog Search Strategy Keyword Index Scoring Fucntion CatOnt Conclusion & Future Work

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Motivation

Keyword Search e-Catalog take a very important role in e-

Business many people want to search product

information using simple keyword Semantic e-Catalog

legacy e-Catalog couldn’t fully express the various and complex product information and relationship

semantic e-Catalog system needs suitable search strategy needs

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Semantic e-Catalog (1)

Product Data

Attribute

P2v

P4v

P3v P4

v

P1v

…

……

ClassificationScheme2

……

ClassificationScheme3

……

ClassificationScheme1

……

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Semantic e-Catalog (2)

EC = {E, R}, E = {P, C, A, U} ME {C, A, U}, ∈ MA = {α1, α2, ..., αm} me = {(α, v)| α MA, v VALUE} ∈ ∈p = { (a, v)| a A, v VALUE} ∈ ∈R = { (e1, e2, r)| e1 E∈ 1, e2 E∈ 2, E1 E, E∈ 2 E, r DR}∈ ∈

EC : Electronic CatalogE : EntityR : RelationshipDR : Definition of RelationshipME : Meta Entity, MA : Meta AttributeP : Product , C : Classification SchemeA : Attribute, U : Unit Of Measure

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Search In e-Catalog

Search Query

e-CatalogDB

SortedList

Query Analyzer

DB Interface

Ranker

Search Engine

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Search Strategy

use simple keyword use semantics implied in e-Catalog

relationship between entities construct keyword index of entity’s

information (values of attributes) construct extended keyword index with

tagging use semantics implied in search query

extract useful keyword and tag meaning

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Extended Keyword Index

extended keyword (voc, tag1, tag2, …, tagt)

extend the definition of semantic e-Catalog with extended keyword index

e = { (a, v)| a ATT, v VALUE} ∈ ∈if e is Product ATT is A else ATT is MA

ivoc = (voc, tag1, tag2, …, tagt) tag1 is a’s identifier

e = {ivoc1, ivoc2, …, ivocv}

VOC : Vocabulary

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RDB Structure for Semantic e-Catalog

e-CatalogDB

Product(ComAtt)

ClassificationScheme

G2B

Attribute

UOM

AttributeGroup

UOMGroup

Product(IndAtt)

ClassificationSchemeGUNGB

ClassificationSchemeUNSPSC

VOC

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Extracting Keyword Indexes

different extracting mechanism according to attributes name description numeral just use original

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Process of Keyword Index Extraction

Analyze Morpheme Structure

Select possible result

Extends the word using dictionaries

Eliminate the useless word

Count frequency and mark order

Eliminate duplicated word

use KLT module

it’s different according to attribute

Do tagging and return Keyword List

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Tags

attr attribute identifier

klt_patn word pattern

klt_pos types of stem

klt_pos2 normal types of stem

klt_josa josa

klt_eomi eomi

domain

composed indicate how ivoc was composed & extended

k_idx order of the ivoc in original v

k_cnt total num extended ivoc from original v

freq frequency of voc in original v

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Scoring Function

from extended definition with extended keyword indexe = {ivoc1, ivoc2, …, ivoca}Score(Q, e) = ∑I,jScore(qi, ivocj)

Score(Q, e)

extend the queryQ = {q1, q2, …, qi, …, qn}qi = {voc, tag1, tag2, …, tags}

generalize with relationship r related eScore(Q, e) = ∑I,jScore(qi, ivocj) + ∑k,lwrk*Score(Q,e’l)

wrk : weight of relation rk

e’l : related entity using rk

Score(qi, ivocj), wr dominate total score

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CatOnt

Parser Loader

easily extensible semi-automated loading tool using XML specification

Searcher not implemented yet

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Loading Process Specification - Entity Converting

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Loading Process Specification - Relationship Converting

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Loading Process Specification - Keyword Index Construction (1)

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Loading Process Specification - Keyword Index Construction (2)

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Conclusion & Future Work

Conclusion propose extended keyword index using various tag for

semantic e-Catalog implement semi-automated converting tool from

legacy e-Catalog to semantic e-Catalog with easily extensible XML specification

propose scoring function which extended keyword index is applicable

Future work contrive feasible scoring function and methods to

assign weights of each relationship implement Searcher extend this motel to general E-R model