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Matchmaking can be basically seen as the process of computing a ranked list of resources with respect to a given query. Semantic matchmaking can be hence described as the process of computing such ordered list also taking into account the semantics of resources description and of the query, provided with reference to a logic theory (an ontology, a set of rules, etc.). A matchmaking step is fundamental in a number of retrieval scenarios spanning from (Web) service discovery and composition to e-commerce transactions up to recruitment in human resource management for task assignment, just to cite a few of them. Also in interactive exploratory tasks, matchmaking and ranking play a fundamental role in the selection of relevant resources to be presented to the user and, in case, further explored. In all the above mentioned frameworks, the user query may contain only hard (strict) requirements or may represent also her preferences. In this talk we see how to use not only deductive reasoning tasks, e.g., subsumption and consistency checking, while computing the ranked list of most promising resources with respect to a query (with preferences).
Citation preview
The 6th International Conference on Web Reasoning and Rule Systems
September 10, 2012, Vienna, Austria
Tommaso Di Noia SisInf Lab - Politecnico di Bari, Bari, Italy
http://sisinflab.poliba.it/dinoia/
Semantic Matchmaking and Ranking:Semantic Matchmaking and Ranking:Beyond Deduction in Retrieval ScenariosBeyond Deduction in Retrieval Scenarios
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matchmaking
“Matchmaking is the process of matching two people together, usually for the purpose of marriage, but the word is also used in the context of sporting events, such as boxing, and in business.”
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matchmaking
Matchmaking is an information retrieval task whereby queries and resources are expressed using semi-structured text in the form of advertisements, and task results are ordered (ranked) lists of those resources best fulfilling the query.
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Semantic Matchmaking
Semantic matchmaking is a matchmaking task whereby queries and resources advertisements are expressed with reference to a shared specification of a schema for the knowledge domain at hand, i.e. an ontology.
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
P2P Marketplaces
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Looking for a non smoking room with WiFi included.
Bedroom for non smokers with cable connection
a
b
c
d
Twin room with Internet connection. Smoking.
Single room. Price includes SAT TV and use of SPA
Twin room. Smoking not allowed. Price includes WiFi,
SAT TV and Breakfast
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
Looking for a non smoking room with WiFi included.
Bedroom for non smokers with cable connection
a
At least I know that I have an Internet
connection
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The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
Looking for a non smoking room with WiFi included.
I will ask if they have WiFi
b
Twin room with Internet connection. Smoking.
Icons by http://dryicons.com
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
Looking for a non smoking room with WiFi included.
I will ask if they have WiFi and if I it is a
non smoking room
c
Single room. Price includes SAT TV and use of SPA
Icons by http://dryicons.com
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
Looking for a non smoking room with WiFi included.
d
Twin room. Smoking not allowed. Price includes WiFi,
SAT TV and Breakfast
Icons by http://dryicons.com
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
a
b
c
d
Looking for a smoking room with WiFi included.
Icons by http://dryicons.com
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
a
b
c
d
Looking for a smoking room with WiFi included.
B e s t !
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The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Matching and ranking
a
b
c
d
Looking for a smoking room with WiFi included.
How to rank?
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The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
OWA
Open-World Assumption (deal with incomplete information)
– The absence of any characteristic must not be interpreted as a constraint of absence
– Any characteristic can be added during a refinement process
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Non-Symmetric
Non-Symmetric Evaluation
– The process is performed matching Resource description with respect to the Query
– The matchmaking result is different if we flip over Resource/Query descriptions
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
What's next
• Penalty functions for ranking
• Non-standard reasoning tasks for matching
• A logic-based framework for matching and ranking
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Running example and logic language
• B&B / Hotel– Semanticized Craiglist
• Description Logics– The framework can be
easily adapted to other logic languages
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
The Ontology OCableConnection v InternetConnection
WiFi v InternetConnection
WiFi v :CableConnection
SPA v FitnessFacilities
SAT-TV v TV v HotelFacilities
FitnessFacilities t Breakfast v HotelFacilities
Bedroom ´ Roomu 9hasBedsu 9guestsu 9price includesSingleRoom ´ Bedroomu (· 1 hasBeds) u (· 1 guests)
TwinRoom ´ Bedroomu (= 2 hasBeds) u (· 2 guests)
SmokingRoom ´ Bedroomu 8guests.SmokerNonSmokingRoom ´ Bedroomu 8guests.(:Smoker)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Deductive Inference Services in DLs
• Satisfiability: the query q is (not) compatible with resource description r
• Subsumption: the resource description completely satisfies the query
O j= r v q
a b
c
d
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O j= q u r v ?
O j= q u r 6v ?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Match classes
1FULL Match: the resource description implies all the characteristics required by the query. The query is fully satisfied.
Full, match is also known as Subsumption match
O j= r v q
r 2 Fu(q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Match classes
1
2
FULL Match: the resource description implies all the characteristics required by the query. The query is fully satisfied.
POTENTIAL Match: the resource description is compatible with the query. It could potentially match the query.
O 6j= r u q v ?r 2 Po(q;O)
O j= r v q
r 2 Fu(q;O)
Potential, match is also known as Intersection match
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Match classes
1
2
3
FULL Match: the resource description implies all the characteristics required by the query. The query is fully satisfied.
POTENTIAL Match: the resource description is compatible with the query. It could potentially match the query.
PARTIAL Match: the resource description is not compatible with the query. There are some chacteristics in the query that overlap the ones represented in the resource description. This latter, partially matches the query.
Partial match is also known as Disjoint match
O j= r u q v ?r 2 Pa(q;O)
O 6j= r u q v ?r 2 Po(q;O)
O j= r v q
r 2 Fu(q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Some issues
FULL matches represent equivalently good resources with respect to the query
POTENTIAL and PARTIAL matches are the most common cases in resource discovery scenarios.
From the user's point of view is not so useful to have a bunch of resources that match potentially/partially the query
Within a class, all the items are eqivalently good!
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Problem statement
In case of Potential or Partial match, how can a semantic matchmaker help users to choose the
best or at least the most promising results?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Penalty Functions
Non-Symmetric: the penalty degree has a direction
Syntax Independent: the penalty depends only on the semantics of q and r
p(r; q;O) 6= p(q; r;O)
O j= r1 ´ r2 ¡! p(r1; q;O) = p(r2; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Potential Match
monotonic over implication
if and
then
O j= r1 v r2r1; r2 2 Po(q;O)
pPo(r1; q;O) · pPo(r2; q;O)
pPo(r; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
q = NoSmokingRoomu 8price includes.WiFi
r1 = NoSmokingRoomu 8price includes.(SAT-TVu InternetConnection)
r2 = Bedroomu 8price includes.(TVu InternetConnection)
WiFi v InternetConnection
: : :
SAT-TV v TV v HotelFacilities
: : :
Bedroom ´ Roomu 9hasBedsu 9guestsu 9price includesNoSmokingRoom ´ Bedroomu 8guests.(:Smoker)
O j= r1 v r2
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
q = NoSmokingRoomu 8price includes.WiFi
r1 = NoSmokingRoomu 8price includes.(SAT-TVu InternetConnection)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
q = NoSmokingRoomu 8price includes.WiFi
r1 = NoSmokingRoomu 8price includes.(SAT-TVu InternetConnection)
q = NoSmokingRoomu 8price includes.WiFi
r2 = Bedroomu 8price includes.(TVu InternetConnection)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Partial Matches
antimonotonic over implication
if and
then
pPa(r; q;O)
O j= r1 v r2r1; r2 2 Pa(q;O)
pPa(r1; q;O) ¸ pPa(r2; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
CableConnection t WiFi v InternetConnection
WiFi v :CableConnection: : :
Bedroom ´ Roomu 9hasBedsu 9guestsu 9price includesNoSmokingRoom ´ Bedroomu 8guests.(:Smoker)SmokingRoom ´ Bedroomu 8guests.Smoker
q = NoSmokingRoomu 8price includes.WiFi
r3 = SmokingRoomu 8price includes.CableConnection
r4 = Bedroomu 8guests.Smoker
O j= r3 v r4
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
q = NoSmokingRoomu 8price includes.WiFi
r3 = SmokingRoomu 8price includes.CableConnection
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
q = NoSmokingRoomu 8price includes.WiFi
r3 = SmokingRoomu 8price includes.CableConnection
q = NoSmokingRoomu 8price includes.WiFi
r4 = Bedroomu 8guests.Smoker
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Concept Contraction
Let L be a Description Logic, O an ontology in L and
both r and q two concepts in L satisfiable w.r.t. O such
that O ⊧ r ⊓ q ⊑ ⟂.
Find two concepts G (for Give up) and K (for Keep) in L such that
O j= GuK ´ q
O 6j= K u r v ?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Concept Contraction
Let L be a Description Logic, O an ontology in L and
both r and q two concepts in L satisfiable w.r.t. O such
that O ⊧ r ⊓ q ⊑ ⟂.
Find two concepts G (for Give up) and K (for Keep) in L such that
O j= GuK ´ q
O 6j= K u r v ?
Partial match
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Concept Abduction
Let L be a Description Logic, O an ontology in L and
both r and q two concepts in L satisfiable w.r.t. O such
that O ⊭ r ⊓ q ⊑ ⟂.
Find a concept H (for Hypotheses) in L such that
O 6j= r uH v ?O j= r uH v q
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Concept Abduction
Let L be a Description Logic, O an ontology in L and
both r and q two concepts in L satisfiable w.r.t. O such
that O ⊭ r ⊓ q ⊑ ⟂.
Find a concept H (for Hypothesis) in L such that
Potential match
O 6j= r uH v ?O j= r uH v q
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Partial Match to Full Match
O j= G uK ´ q
O 6j= K u r v ?
O j= q u r v ? Partial match
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Partial Match to Full Match
O j= G uK ´ q
O 6j= K u r v ?
O j= q u r v ? Partial match
Concept Contraction
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Partial Match to Full Match
O j= G uK ´ q
O 6j= K u r v ?
O j= q u r v ?
Potential match
Contracted query
Partial match
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Partial Match to Full Match
O j= G uK ´ q
O 6j= K u r v ?
O j= q u r v ?
Potential match
Partial match
O 6j= r uH v ?O j= r uH v K
Concept Adbuction
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Partial Match to Full Match
O j= G uK ´ q
O 6j= K u r v ?
O j= q u r v ?
Potential match
Partial match
O 6j= r uH v ?O j= r uH v K Full match
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Back to the initial example...
Icons by http://dryicons.com
Looking for a non smoking room with WiFi included.
I will ask if they have WiFi
b
Twin room with Internet connection. Smoking.
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Back to the initial example...
b
Icons by http://dryicons.com
NoSmokingRoomu 8price includes.WiFi
O j=
u
v ?
TwinRoomu SmokingRoom u8price includes.InternetConnection
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Back to the initial example...
q ´ Bedroom u 8price includes.WiFi u8guests.(:Smoker)
K = Bedroom u 8price includes.WiFi
G = 8guests.(:Smoker)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Back to the initial example...
r = TwinRoomu SmokingRoomu8price includes.InternetConnection
H = 8price includes.WiFi
K = Bedroom u 8price includes.WiFi
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Relationship between Penalty Functions and Concept Contraction
pPa(r; q;O)incompatibility degree of r with respect to q
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Relationship between Penalty Functions and Concept Contraction
pPa(r; q;O)incompatibility degree of q with respect to r
O 6j= K u r v ?O j= G uK ´ q
The reason why q is not compatible with r
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= r1 v r2
O j= q u r1 v ?
O j= q u r2 v ?
O j= G1 uK1 ´ q
O j= K1 u r1 6v ?O j= G2 uK2 ´ q
O j= K2 u r2 6v ?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= r1 v r2
O j= q u r1 v ?
O j= q u r2 v ?
We espect O j= G1 v G2
O j= G1 uK1 ´ q
O j= K1 u r1 6v ?O j= G2 uK2 ´ q
O j= K2 u r2 6v ?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= G1 v G2r1 is “more incompatibile” than r2
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= G1 v G2r1 is “more incompatibile” than r2
pPa(r1; q;O) ¸ pPa(r2; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= G1 v G2
Antimonotonic property of pPa
pPa(r1; q;O) ¸ pPa(r2; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= G1 v G2
1.pPa depends on G and K
pPa(r1; q;O) ¸ pPa(r2; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= G1 v G2
1.pPa depends on G and K
2.G and K represent an explanation for pPa
pPa(r1; q;O) ¸ pPa(r2; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Relationship between Penalty Functions and Concept Contraction
how much we do not know about r with respect to q
pPo(r; q;O)
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Relationship between Penalty Functions and Concept Contraction
The reason why r is not subsumed by q
how much we do not know about r with respect to q
pPo(r; q;O)
O 6j= r uH v ?O j= r uH v q
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
O j= r1 v r2
O 6j= q u r1 v ?
O 6j= q u r2 v ?
O j= r1 uH1 v q
O j= r2 uH2 v q
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
We espect
O j= r1 v r2
O 6j= q u r1 v ?
O 6j= q u r2 v ?
O j= r1 uH1 v q
O j= r2 uH2 v q
O j= H2 v H1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
r1 is “more informative” than r2 with respect to qO j= H2 v H1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
r1 is “more informative” than r2 with respect to q
pPa(r1; q;O) · pPa(r2; q;O)
O j= H2 v H1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
Monotonic property of pPo
pPa(r1; q;O) · pPa(r2; q;O)
O j= H2 v H1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
1.pPo depends on H
pPa(r1; q;O) · pPa(r2; q;O)
O j= H2 v H1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Intuition
1.pPo depends on H
2.H represents an explanation for pPo
pPa(r1; q;O) ¸ pPa(r2; q;O)
O j= H2 v H1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Global Penalty Function and Explanation
G, K and H represent an explanation for p
p(r; q;O) = f(pPa(r; q;O); pPo(r;K;O))
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Query refinement
• We are under an Open World Assumption. • What the user does not specify in the query is
something the user – did not know– did not care– completely forgot to mention
• We can suggest the user to refine her query by using extra information found in the resources retrieved by the matchmaker
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Query refinement
• Hq represents what has to be hypothesized in q in order to satisfy r
• Hq represents those characteristics described in r but not specified (yet) in q
O j= q uHq v r
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
O j= q uHq v r
q = NoSmokingRoomu 8price includes.WiFir = NoSmokingRoomu
8price includes.(SAT-TVu InternetConnection)
8price includes.SAT-TV
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Questions
• Are match classes related with each other?• Since partial matches are not necessarily
unrecoverable matches, can they be compared with potential matches?
• What does the penalty score represent for partial matches?
• What does the penalty score represent for potential matches?
• Can we have a single ranked list of resources?• How to use descriptions of discovered resources to
help the user in refining her query?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
So far...
• Matchmaking as discovery
• Unilateral process
• The query “represents” the user
• No interaction with the two parties
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
What if...
• We have information on user preferences (profile)
• Both the users involved in the process express their own preferences
• The process is bilateral
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
What if...
• We have information on user preferences (profile)
• Both the users involved in the process express their own preferences
• The process is bilateral
Bilateral Matchmaking = Bilateral Negotiation
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Weighted formulas and User Profile
p = hP; vi
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Weighted formulas and User Profile
p = hP; vi
Logic Formula
Utility associated to P
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Weighted formulas and User Profile
p = hP; vi
U = fhP1; v1i; : : : ; hPn; vnig
For each pair
such that
then
hPi; vii; hPj ; vji 2 U
O j= Pi ´ Pj
vi = vj
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Problem statement
What is the utility, for each user, associated to the final agreement?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Weighted propositional formulas
The final agreement is a propositional model
u(m) =X
fv j hP; vi 2 U and m j= Pg
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
p1 = htwin-room_ double-room; 0:3ip2 = hcable-connection^ :twin-room; 0:4ip3 = hdouble-room! king-size; 0:3i
m = ftwin-room= true;double-room= false;
cable-connection= true;king-size= trueg
m j= twin-room_ double-room
m 6j= cable-connection^ :twin-roomm j= double-room! king-size
u(m) = 0:3 + 0:3
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Weighted DLs formulas
Infinite set of models
Possible solution: Subsumption (implication-based)
The final agreement is a satisfiable DL formula
uv(A) =X
fv j hP; vi 2 P and O j= A v Pg
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Issue
uv(A) = v3
p1 = h9price includes.FitnessFacilities; v1ip2 = h9price includes.Breakfast; v2ip3 = hNoSmokingRoom; v3i
A = (9price includes.FitnessFacilitiest9price includes.Breakfast)uNoSmokingRoom
O 6j= A v 9price includes.FitnessFacilitiesO 6j= A v 9price includes.BreakfastO j= A v NoSmokingRoom
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Issue
O 6j= A v ?AI 6= ;
(NoSmokingRoom)I 6= ;
(9price includes.FitnessFacilities)I [(9price includes.Breakfast)I 6= ;
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Issue
O 6j= A v ?AI 6= ;
(NoSmokingRoom)I 6= ;
(9price includes.FitnessFacilities)I [(9price includes.Breakfast)I 6= ;
At least one of these two sets must be non empty
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Issue
O 6j= A v ?AI 6= ;
(NoSmokingRoom)I 6= ;
(9price includes.FitnessFacilities)I [(9price includes.Breakfast)I 6= ;
u(A) = minfv1 + v3; v2 + v3g
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Interpretations
AI 6= ; ) I j= A
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Interpretations
AI 6= ; ) I j= A
I1 j= 9price includes.FitnessFacilitiesu9price includes.BreakfastuNoSmokingRoom
I2 j= :9price includes.FitnessFacilitiesu9price includes.BreakfastuNoSmokingRoom
I3 j= 9price includes.FitnessFacilitiesu:9price includes.BreakfastuNoSmokingRoom
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Interpretations
AI 6= ; ) I j= A
I1 j= 9price includes.FitnessFacilitiesu9price includes.BreakfastuNoSmokingRoom
I2 j= :9price includes.FitnessFacilitiesu9price includes.BreakfastuNoSmokingRoom
I3 j= 9price includes.FitnessFacilitiesu:9price includes.BreakfastuNoSmokingRoom
What if we also have an
ontology?
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Minimal models and minimal utility value
I j= fAg [ Ou(A) =
Xfv j hP; vi 2 U and I j= Pg is minimal
Minimal utility value
Minimal model
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Ontological constraints
U = fhP1; v1i; : : : ; hPn; vnig
² O j= A v Pi
² O j= A v Pi t :Pj t : : :
² O j= A u Pi u :Pj u : : : v ?
² O j= A u Pi u :Pj u : : : v Pk u :Ph u : : :
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Ontological closure
O j= A v :Pi t Pj t : : : t :Pk
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Ontological closure
Á
minimalO j= A v :Pi t Pj t : : : t :Pk
CL(A;O;U) = fÁ1; : : : ; Áhg
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Ontological closure to minimal utility value
Revert to ILP
{0,1}-variable
CL(A;O;U) = fÁ1; : : : ; Áhg
Ái 2 CL(A;O;U) )X
f(1 ¡ p) j :P 2 Áig +X
fp j P 2 Áig ¸ 1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
From Ontological closure to minimal utility value
Revert to ILP
u(A;O) = minX
fv ¢ p j hP; vi 2 Ug
CL(A;O;U) = fÁ1; : : : ; Áhg
Ái 2 CL(A;O;U) )X
f(1 ¡ p) j :P 2 Áig +X
fp j P 2 Áig ¸ 1
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
A = Bedroomu (8guests.Smokert 8guests.:Smoker) u8price includes.WiFi
p1 = hNoSmokingRoom; 0:5ip2 = hSmokingRoom; 0:1ip3 = h8price includes.InternetConnection; 0:4i
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
A = Bedroomu (8guests.Smokert 8guests.:Smoker) u8price includes.WiFi
p1 = hNoSmokingRoom; 0:5ip2 = hSmokingRoom; 0:1ip3 = h8price includes.InternetConnection; 0:4i
A v NoSmokingRoomt SmokingRoom
A u NoSmokingRoomv :SmokingRoomA v 8price includes.InternetConnection
Ontology
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Example
min(0:5 ¢ s+ 0:1 ¢ ns + 0:4 ¢ i)
ns+ s ¸ 1
(1 ¡ ns) + (1 ¡ s) ¸ 1
i ¸ 1
CL(A;O;U) = fNoSmokingRoomt SmokingRoom ;
:NoSmokingRoomt :SmokingRoom ;8price includes.InternetConnectiong
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Bilateral matchmaking
Two user profilesU1 = fhP 11 ; v11i; : : : ; hP 1n ; v1nigU2 = fhP 21 ; v21i; : : : ; hP 2m; v2mig
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Bilateral matchmaking
Two user profilesU1 = fhP 11 ; v11i; : : : ; hP 1n ; v1nigU2 = fhP 21 ; v21i; : : : ; hP 2m; v2mig
Utility user1
Utility user2
Pareto frontier
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Pareto efficiency
Utility user2
Utility user1
• Nash solution: maximize• Welfare: maximize
u1 ¢ u2u1 + u2
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Conclusion
• Semantic resource retrieval needs frameworks and tools that go beyond pure deductive procedures
• Non-standard reasoning as a powerful tool for matchmaking as discovery
• Utility theory for bilateral matchmaking as negotiation
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Acknowledgments
In alphabetical order:
Simona Colucci, Eugenio Di Sciascio, Francesco M. Donini, Agnese Pinto, Azzurra Ragone, Michele Ruta, Eufemia Tinelli
and all the other guys at SisInf Lab
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Some references [1/3]
• T. Di Noia, E. Di Sciascio, and F.M. Donini. Extending Semantic-Based Matchmaking via Concept Abduction and Contraction. In EKAW ’04, pp. 307–320, 2004.
• T. Di Noia, E. Di Sciascio, and F.M. Donini. Semantic Matchmaking as Non-Monotonic Reasoning: A Description Logic Approach. JAIR, 29:269–307, 2007.
• T. Di Noia, E. Di Sciascio, and F.M. Donini. Semantic matchmaking via non-monotonic reasoning: the MaMas-tng matchmaking engine. Communications of SIWN, 5:67–72, 2008.
• T. Di Noia, E. Di Sciascio, F.M. Donini, and M. Mongiello. A system for principled Matchmaking in an electronic marketplace. IJEC, 8(4):9–37, 2004.
• T. Di Noia, E. Di Sciascio, and F. M. Donini. Computing information minimal match explanations for logic-based matchmaking. In WI/IAT ’09, pp. 411–418, 2009.
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Some references [2/3]
• S. Colucci, T. Di Noia, E. Di Sciascio, F.M. Donini, and M. Mongiello. A Uniform Tableaux-Based Method for Concept Abduction and Contraction in Description Logics. In ECAI ’04, pp. 975–976, 2004.
• S. Colucci, T. Di Noia, E. Di Sciascio, F. M. Donini, and A. Ragone. A unified framework for non-standard reasoning services in description logics. In ECAI ’10, pp. 479–484, 2010.
• S. Colucci, T. Di Noia, A. Pinto, A. Ragone, M. Ruta, and E. Tinelli. A non-monotonic approach to semantic matchmaking and request refinement in e-marketplaces. IJEC, 12(2):127–154, 2007.
• A. Ragone, T. Di Noia, E. Di Sciascio, and F.M. Donini. Logic-based automated multi-issue bilateral negotiation in peer-to-peer e-marketplaces. J.AAMAS, 16(3):249–270, 2008.
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Some references [3/3]
• A. Ragone, U. Straccia, T. Di Noia, E. Di Sciascio, and F.M. Donini. Fuzzy matchmaking in e-marketplaces of peer entities using Datalog. FSS, 10(2):251–268, 2009.
• A. Ragone, T. Di Noia, E. Di Sciascio, F. M. Donini, and Michael Wellman. Computing utility from weighted description logic preference formulas. In DALT ’09, pp. 158–173, 2009.
• A. Ragone, T. Di Noia, F. M. Donini, E. Di Sciascio, and Michael Wellman. Weighted description logics preference formulas for multiattribute negotiation. In SUM ’09, pp. 193–205, 2009.
The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
Thank You