A Ranking Algorithm for Semantic search engine

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A Ranking Algorithm for Semantic search engine
spam and fake detection case study

• By Soheila Dehghanzadeh.
• Web technology lab weekly seminars.

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Agenda

• Web spam definition
• A brief overview of Search engines
• Search engine phases
• Crawling
• Indexing
• Index lookup
• Ranking lookup results
• My proposed ranking algorithm ?

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Web spam and fake

• In web of data anyone is able to say anything
  about anything.
• Low quality data should not be mentioned in top
  search results.

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A Search Engine
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A Search Engine
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web of data vs. web of documents.

• WODocNo link type and no trustworthiness (just
  popularity).
• WOData should consider link type and link
  context (for provenance and proof of trust).

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Crawling Indexing phase

• Using ldspider to crawl linked data.
• Using hexastore for complete indexing the crawled
  data. Special thanks to Panagiotis Karras for
  providing hexastore implementation in python.

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Index lookup results for extensionsome Results
may not include keyword but they have high
quality and relevance.Result expansion to hide
the locality effect. Some sites is referred many
times but in this special context other
professional sites lookup results are more
interested.
Web of data
result
Crawler
Raw rdf
lookup
ranking
indexing
index
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HexaStore

• Index structure that we use in our search engine.
• Each RDF element type deserve to have special
  index structure build round it.
• Every possible ordering of the importance or
  precedence of the three elements in the indexing
  scheme is materialized.
• Each index structure in a hexastore centers
  around one RDF element and defines a
  perioritation between the other 2 elements.

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Sample spo indexing in a hexastore
Si
P(I,1) P(I,2) P(I,Ni)
O(i2,1)
O(i2,2)





O(i2,ki2)
O(iNi,1)
O(iNi,2)





O(iNi,kiNi)
O(i1,1)
O(i1,2)





O(i1,ki1)
Space complexity Sposppsosopospo
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My idea!

• Import the base result set to jena and extend it.
• Extending the base set with ontology reasoning
  rules so that extra resources and relations will
  be added through reasoning rules.
• The added resources
• The added relation has no context so their
  trustworthiness is an aggregation function on
  (x,y,rule) relations---
• Resources will be added only through sameAs
  predicate
• Resources will be ranked according to relevance
  to query terms (using ontobroker pagerank
  objectrank- triplerank HITS,.)
• Query
• Keyword query
• Structured query
• Ontology based query (using an interface to get
  query) - ontobroker
• Relation (properties) will be ranked according to
  contexts(provenance) using relation ranking
  methods such as semRank or we can look at
  contexts pageRank.
• Note that First we rank resources and second we
  rank relations . However it depends on the user
  query whether it is looking for relations or
  resources.

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• Lookup on quads for keyword (Soheila)
• Q1 http//um.s11,givenname,Soheila,UM
• Q2http//NIOC/p25,fullname,Soheila
  Dehghan,NIOC
• Q3http//nigc-khrz/e66,firstname,Soheila,NIGC
• Q4 http//fake/f4,name,Soheila,fake

Cheese
B.Gates
Scott
Buy(Spam)
Meet(CNN)
Dancewith(FK)
SA(FB)
SA(UM)
http//facebook/u122
http//isport /us122
Q1
SA(UM)
SA(LI)
SA(NIGC)
Q3
Q2
http//linkedIn/u12
SA(FK)
SA(FK)
SA(FK)
Q4
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Result set expansion methods

• step1 using sameas predicate on found Qaudes and
  extend ResultSet to Q1,,Qr
• index LookUp
• Q1(S),SameAs,?,??Qr(S),SameAs,?,?
• ?,SameAs,Q1(S),?? ?,SameAs,Qr(S),?
• (Q1,,Q4?Q1,,Q4,FBURI,LinkedInURI,isportURI) in
  our case.
• apply PR on Extended graph with SameAs which
  SameAs links are replaced with PR weight of
  sameAs context.(to know the trustwothiness of
  each contexts).

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Result set expansion methods

• Step2 LookUp all properties of Q1(s),,Qr(s)
• Q1(s),?,?,??,?,Q1(s),?
• Qr(s),?,?,??,?,Qr(s),?
• Step4 add inferred relation using domain
  ontology(context is composed of
  ontologyinference process)
• Step4 rank Q1,,Qr according to their TpageRank
  (computed online from graph of step1 ), rank
  relations according to their context
  pageRank(which is computed by Google offline)
• Note contexts who has PR lower than a
  treshhold wont be mentioned.they maybe Spam or
  Fake Sites.

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Structured query on quads indexes

• Single pivot
• (S,?,?,?),(?,p,?,?),(?,?,o.?),(?,?,?.C)
• Double pivot
• (S,p,?,?),(s,?,O,?),(s,?,?,C),(?,P,O,?),(?,P,?,C),
  (?,?,o,C)
• Triple pivot
• (s,p,o,?),(s,p,?,c),(s,?,o,c),(?,p,o,c)

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• Step1 if the specified parts was URI then a
  direct lookup is performed by search engine .
  Otherwise if user have specified keyword for each
  parts then firstly a keyword search will be done
  and then for each result URI a lookup will be
  performed.

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Lookup on quads for ontological queries
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DehghanZadeh (GAS)
GAS
Worked at(GAS)
OwlsameAs(FUM)
Sally(NIOC)
OwlsameAs(NIOC)
Soheila(FUM)
played in(NIOC)
Studied in(FUM)
NIOC Team
FUM
Supervisor (FUM)
Kahani(FUM)
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Related works for ranking web of data

• Objectrank
• Ding
• Sindice ti-idf
• EntityRank.
• semRank
• ReConRank
• ontobroker

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Proof of trust ?

• Jena inference Explanation will be used to
  represent as a proof of trust

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Evaluation

• Compare Spam ranks
• Compare query time
• Compare index size

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Any question?
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Best things in the life are free. Thanks for
attention.