Semantic Matchmaking and Ranking: Beyond Deduction in Retrieval Scenarios

897 views

Published on

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).

Published in: Technology, Business
0 Comments
0 Likes
Statistics
Notes
  • Be the first to comment

  • Be the first to like this

No Downloads
Views
Total views
897
On SlideShare
0
From Embeds
0
Number of Embeds
16
Actions
Shares
0
Downloads
16
Comments
0
Likes
0
Embeds 0
No embeds

No notes for slide

Semantic Matchmaking and Ranking: Beyond Deduction in Retrieval Scenarios

  1. 1. Semantic Matchmaking and Ranking:Beyond Deduction in Retrieval Scenarios Tommaso Di Noia SisInf Lab - Politecnico di Bari, Bari, Italy t.dinoia@poliba.it http://sisinflab.poliba.it/dinoia/ The 6th International Conference on Web Reasoning and Rule Systems September 10, 2012, Vienna, Austria
  2. 2. 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
  3. 3. 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
  4. 4. 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
  5. 5. P2P Marketplaces Looking for a non smoking room with WiFi included. Bedroom for non smokers with cable connectiona Twin room with Internet connection. Smoking.b Single room. Price includes SAT TV and use of SPAc Twin room. Smoking not allowed. Price includes WiFi,d SAT TV and Breakfast Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  6. 6. Matching and ranking Looking for a non smoking room with WiFi included. At least I know that I have an Internet connection Bedroom for non smokers with cable connectiona Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  7. 7. Matching and ranking Looking for a non smoking room with WiFi included. I will ask if they have WiFi Twin room with Internet connection. Smoking.b Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  8. 8. 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 Single room. Price includes SAT TV and use of SPAc Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  9. 9. Matching and ranking Looking for a non smoking room with WiFi included. Twin room. Smoking not allowed. Price includes WiFi,d SAT TV and Breakfast Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  10. 10. Matching and ranking Looking for a smoking room with WiFi included.abcd Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  11. 11. Matching and ranking Looking for a smoking room with WiFi included.abc Best!d Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  12. 12. Matching and ranking Looking for a smoking room with WiFi included.ab How to rank?cd Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  13. 13. OWAOpen-World Assumption (deal with incompleteinformation)– 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
  14. 14. Non-SymmetricNon-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
  15. 15. Whats 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
  16. 16. 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
  17. 17. The Ontology O CableConnection v InternetConnection WiFi v InternetConnection WiFi v :CableConnection SPA v FitnessFacilities SAT-TV v TV v HotelFacilities FitnessFacilities t Breakfast v HotelFacilities Bedroom ´ Room u 9hasBeds u 9guests u 9price includes SingleRoom ´ Bedroom u (· 1 hasBeds) u (· 1 guests) TwinRoom ´ Bedroom u (= 2 hasBeds) u (· 2 guests) SmokingRoom ´ Bedroom u 8guests.SmokerNonSmokingRoom ´ Bedroom u 8guests.(:Smoker) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  18. 18. Deductive Inference Services in DLs• Satisfiability: the query q is (not) compatible with resource description r O j= q u r v ? a b O j= q u r 6v ? c• Subsumption: the resource description completely satisfies the query O j= r v q d Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  19. 19. Match classes O j= r v q FULL Match: the resource description implies 1 all the characteristics required by the query. r 2 F u(q; O) The query is fully satisfied.Full, match is also known as Subsumption match The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  20. 20. Match classes O j= r v q FULL Match: the resource description implies 1 all the characteristics required by the query. r 2 F u(q; O) The query is fully satisfied. O 6j= r u q v ? POTENTIAL Match: the resource description is 2 compatible with the query. It could potentially r 2 P o(q; O) match the query.Potential, match is also known as Intersection match The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  21. 21. Match classes O j= r v q FULL Match: the resource description implies 1 all the characteristics required by the query. r 2 F u(q; O) The query is fully satisfied. O 6j= r u q v ? POTENTIAL Match: the resource description is 2 compatible with the query. It could potentially r 2 P o(q; O) match the query. PARTIAL Match: the resource description is O j= r u q v ? not compatible with the query. There are some 3 chacteristics in the query that overlap the ones r 2 P a(q; O) represented in the resource description. This latter, partially matches the query.Partial match is also known as Disjoint match The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  22. 22. Some issuesWithin a class, all the items are eqivalently good! 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 users point of view is not so useful to have a bunch of resources that match potentially/partially the query The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  23. 23. Problem statementIn case of Potential or Partial match, how can asemantic 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
  24. 24. Penalty FunctionsNon-Symmetric: the penalty degree has a direction p(r; q; O) 6= p(q; r; O)Syntax Independent: the penalty depends only onthe semantics of q and r 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
  25. 25. Potential Match pP o (r; q; O)monotonic over implication if r1 ; r2 2 P o(q; O) and O j= r1 v r2 then pP o (r1 ; q; O) · pP o (r2 ; q; O) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  26. 26. Example WiFi v InternetConnection ::: SAT-TV v TV v HotelFacilities ::: Bedroom ´ Room u 9hasBeds u 9guests u 9price includes NoSmokingRoom ´ Bedroom u 8guests.(:Smoker)q = NoSmokingRoomu 8price includes.WiFir1 = NoSmokingRoomu 8price includes.(SAT-TV u InternetConnection)r2 = Bedroom u 8price includes. u InternetConnection) (TV O j= r1 v r2 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  27. 27. Example q = NoSmokingRoomu 8price includes.WiFir1 = NoSmokingRoomu 8price includes.(SAT-TV u InternetConnection) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  28. 28. Example q = NoSmokingRoomu 8price includes.WiFir1 = NoSmokingRoomu 8price includes.(SAT-TV u InternetConnection)q = NoSmokingRoomu 8price includes.WiFir2 = Bedroom u 8price includes. u InternetConnection) (TV The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  29. 29. Partial Matches pP a (r; q; O)antimonotonic over implication if r1 ; r2 2 P a(q; O) and O j= r1 v r2 then pP a (r1 ; q; O) ¸ pP a (r2 ; q; O) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  30. 30. Example CableConnection t WiFi v InternetConnection WiFi v :CableConnection ::: Bedroom ´ Room u 9hasBeds u 9guests u 9price includes NoSmokingRoom ´ Bedroom u 8guests.(:Smoker) SmokingRoom ´ Bedroom u 8guests.Smokerq = NoSmokingRoom u 8price includes.WiFir3 = SmokingRoom u 8price includes.CableConnectionr4 = Bedroom u 8guests.Smoker O j= r3 v r4 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  31. 31. Exampleq = NoSmokingRoom u 8price includes.WiFir3 = SmokingRoom u 8price includes.CableConnection The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  32. 32. Example q = NoSmokingRoom u 8price includes.WiFir3 = SmokingRoom u 8price includes.CableConnectionq = NoSmokingRoomu 8price includes.WiFir4 = Bedroom u 8guests.Smoker The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  33. 33. 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
  34. 34. Concept ContractionLet L be a Description Logic, O an ontology in L andboth r and q two concepts in L satisfiable w.r.t. O suchthat O ⊧ r ⊓ q ⊑ ⟂.Find two concepts G (for Give up) and K (for Keep) inL such that O j= GuK ´ q O 6j= K ur v ? The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  35. 35. Concept ContractionLet L be a Description Logic, O an ontology in L andboth r and q two concepts in L satisfiable w.r.t. O suchthat O ⊧ r ⊓ q ⊑ ⟂. Partial matchFind two concepts G (for Give up) and K (for Keep) inL such that O j= GuK ´ q O 6j= K ur v ? The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  36. 36. Concept AbductionLet L be a Description Logic, O an ontology in L andboth r and q two concepts in L satisfiable w.r.t. O suchthat O ⊭ r ⊓ q ⊑ ⟂.Find a concept H (for Hypotheses) in L such that O 6j= ruH v ? O j= ruH v q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  37. 37. Concept AbductionLet L be a Description Logic, O an ontology in L andboth r and q two concepts in L satisfiable w.r.t. O suchthat O ⊭ r ⊓ q ⊑ ⟂. Potential matchFind a concept H (for Hypothesis) in L such that O 6j= ruH v ? O j= ruH v q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  38. 38. 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
  39. 39. From Partial Match to Full MatchO j= q u r v ? Partial matchO j= GuK ´qO 6j= K ur v ? The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  40. 40. From Partial Match to Full MatchO j= q u r v ? Partial matchO j= GuK ´qO 6j= K ur v ? Concept Contraction The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  41. 41. From Partial Match to Full Match O j= q u r v ? Partial matchO j= GuK ´qO 6j= K ur v ? Potential matchContracted query The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  42. 42. From Partial Match to Full MatchO j= q u r v ? Partial matchO j= GuK ´qO 6j= K ur v ? Potential matchO 6j= ruH v? Concept AdbuctionO j= ruH vK The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  43. 43. From Partial Match to Full MatchO j= q u r v ? Partial matchO j= GuK ´qO 6j= K ur v ? Potential matchO 6j= ruH v?O j= ruH vK Full match The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  44. 44. Back to the initial example... Looking for a non smoking room with WiFi included. I will ask if they have WiFi Twin room with Internet connection. Smoking.b Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  45. 45. Back to the initial example... O j= NoSmokingRoomu 8price includes.WiFi u TwinRoom u SmokingRoom ub 8price includes.InternetConnection v? Icons by http://dryicons.com The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  46. 46. Back to the initial example...q ´ Bedroom u 8price includes.WiFi u 8guests.(:Smoker)K = Bedroom u 8price includes.WiFiG = 8guests.(:Smoker) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  47. 47. Back to the initial example...K = Bedroom u 8price includes.WiFir = TwinRoom u SmokingRoomu 8price includes.InternetConnectionH = 8price includes.WiFi The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  48. 48. 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
  49. 49. Relationship between Penalty Functions and Concept Contraction incompatibility degreepP a (r; q; O) of r with respect to q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  50. 50. Relationship between Penalty Functions and Concept Contraction incompatibility degreepP a (r; q; O) of q with respect to rO 6j= K ur v ?O j= GuK ´ qThe reason why q is notcompatible with r The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  51. 51. IntuitionO j= r1 v r2 O j= G1 u K1 ´ qO j= q u r1 v ? O j= K1 u r1 6v ? O j= G2 u K2 ´ qO j= q u r2 v ? O j= K2 u r2 6v ? The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  52. 52. IntuitionO j= r1 v r2 O j= G1 u K1 ´ qO j= q u r1 v ? O j= K1 u r1 6v ? O j= G2 u K2 ´ qO j= q u r2 v ? O j= K2 u r2 6v ?We espect O j= G1 v G2 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  53. 53. Intuition r1 is “more incompatibile”O j= G1 v G2 than r2 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  54. 54. Intuition r1 is “more incompatibile”O j= G1 v G2 than r2pP a (r1 ; q; O) ¸ pP a (r2 ; q; O) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  55. 55. IntuitionO j= G1 v G2pP a (r1 ; q; O) ¸ pP a (r2 ; q; O)Antimonotonic property of pPa The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  56. 56. IntuitionO j= G1 v G2pP a (r1 ; q; O) ¸ pP a (r2 ; q; O)1.pPa depends on G and K The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  57. 57. IntuitionO j= G1 v G2pP a (r1 ; q; O) ¸ pP a (r2 ; q; O)1.pPa depends on G and K2.G and K represent an explanation for pPa The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  58. 58. 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
  59. 59. Relationship between Penalty Functions and Concept ContractionpP o (r; q; O) how much we do not know about r with respect to q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  60. 60. Relationship between Penalty Functions and Concept ContractionpP o (r; q; O) how much we do not know about r with respect to qO 6j= ruH v ?O j= ruH v qThe reason why r is notsubsumed by q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  61. 61. IntuitionO j= r1 v r2O 6j= q u r1 v ? O j= r1 u H1 v qO 6j= q u r2 v ? O j= r2 u H2 v q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  62. 62. IntuitionO j= r1 v r2O 6j= q u r1 v ? O j= r1 u H1 v qO 6j= q u r2 v ? O j= r2 u H2 v qWe espect O j= H2 v H1 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  63. 63. Intuition r1 is “more informative”O j= H2 v H1 than r2 with respect to q The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  64. 64. Intuition r1 is “more informative”O j= H2 v H1 than r2 with respect to qpP a (r1 ; q; O) · pP a (r2 ; q; O) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  65. 65. IntuitionO j= H2 v H1pP a (r1 ; q; O) · pP a (r2 ; q; O)Monotonic property of pPo The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  66. 66. IntuitionO j= H2 v H1pP a (r1 ; q; O) · pP a (r2 ; q; O)1.pPo depends on H The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  67. 67. IntuitionO j= H2 v H1pP a (r1 ; q; O) ¸ pP a (r2 ; q; O)1.pPo depends on H2.H represents an explanation for pPo The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  68. 68. 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
  69. 69. Global Penalty Function and Explanation p(r; q; O) = f (pP a (r; q; O); pP o (r; K; O))G, K and H represent an explanation for p The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  70. 70. 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
  71. 71. 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
  72. 72. Query refinement O j= q u Hq v r• 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 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  73. 73. Exampleq = NoSmokingRoomu 8price includes. WiFir = NoSmokingRoomu 8price includes.(SAT-TV u InternetConnection)O j= q u Hq v r8price includes.SAT-TV The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  74. 74. 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
  75. 75. 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
  76. 76. 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
  77. 77. 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
  78. 78. Weighted formulas and User Profilep = hP; vi The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  79. 79. Weighted formulas and User Profile p = hP; vi Utility associated to PLogic Formula The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  80. 80. Weighted formulas and User Profilep = hP; viU = fhP1 ; v1 i; : : : ; hPn ; vn igFor each pair hPi ; vi i; hPj ; vj i 2 Usuch that O j= Pi ´ Pjthen vi = vj The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  81. 81. 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
  82. 82. Weighted propositional formulas X u(m) = fv j hP; vi 2 U and m j= P g The final agreement is a propositional modelThe 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  83. 83. Examplep1 = htwin-room _ double-room; 0:3ip2 = hcable-connection^ :twin-room; 0:4ip3 = hdouble-room ! king-size; 0:3im = ftwin-room = true; double-room = f alse; cable-connection = true; king-size = truegm j= twin-room_ double-roomm 6j= cable-connection^ :twin-room u(m) = 0:3 + 0:3m j= double-room ! king-size The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  84. 84. Weighted DLs formulasInfinite set of modelsPossible solution: Subsumption (implication-based) Xuv (A) = fv j hP; vi 2 P and O j= A v P g The final agreement is a satisfiable DL formula The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  85. 85. Issuep1 = h9price includes. FitnessFacilities; v1 ip2 = h9price includes. Breakfast; v2 ip3 = hNoSmokingRoom; v3 iA = (9price includes.FitnessFacilitiest 9price includes.Breakfast)u NoSmokingRoomO 6j= A v 9price includes.FitnessFacilitiesO 6j= A v 9price includes.Breakfast uv (A) = v3O j= A v NoSmokingRoom The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  86. 86. Issue O 6j= Av?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
  87. 87. Issue O 6j= Av?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
  88. 88. Issue O 6j= Av?AI 6= ; (NoSmokingRoom)I 6= ;(9price includes.FitnessFacilities)I [ (9price includes.Breakfast)I 6= ;u(A) = minfv1 + v3 ; v2 + v3 g The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  89. 89. InterpretationsAI 6= ; ) I j= A The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  90. 90. InterpretationsAI 6= ; ) I j= AI1 j= 9price includes.FitnessFacilitiesu 9price includes.Breakfast u NoSmokingRoomI2 j= :9price includes.FitnessFacilitiesu 9price includes.Breakfastu NoSmokingRoomI3 j= 9price includes.FitnessFacilitiesu :9price includes.Breakfastu NoSmokingRoom The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  91. 91. InterpretationsAI 6= ; ) I j= AI1 j= 9price includes.FitnessFacilitiesu 9price includes.Breakfast u 2 NoSmokingRoom What if weI j= :9price includes.FitnessFacilitiesu 9price includes. also have an Breakfastu NoSmokingRoom ontology?I3 j= 9price includes.FitnessFacilitiesu :9price includes.Breakfastu NoSmokingRoom The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  92. 92. Minimal models and minimal utility valueMinimal model I j= fAg [ O Xu(A) = fv j hP; vi 2 U and I j= P g is minimalMinimal utility value The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  93. 93. Ontological constraintsU = fhP1 ; v1 i; : : : ; hPn ; vn ig² O j= A v Pi² 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
  94. 94. Ontological closureO j= A v :Pi t Pj t : : : t :Pk The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  95. 95. Ontological closureO j= A v :Pi t Pj t : : : t :Pk minimal ÁCL(A; O; U) = fÁ1 ; : : : ; Áh g The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  96. 96. From Ontological closure to minimal utility valueRevert to ILPCL(A; O; U) = fÁ1 ; : : : ; Áh g XÁi 2 CL(A; O; U) ) f(1 ¡ p) j :P 2 Ái g + X fp j P 2 Ái g ¸ 1 {0,1}-variable The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  97. 97. From Ontological closure to minimal utility valueRevert to ILPCL(A; O; U) = fÁ1 ; : : : ; Áh g XÁi 2 CL(A; O; U) ) f(1 ¡ p) j :P 2 Ái g + X fp j P 2 Ái g ¸ 1 Xu(A; O) = min fv ¢ p j hP; vi 2 Ug The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  98. 98. ExampleA = Bedroom u (8guests.Smoker t 8guests.:Smoker) u 8price includes.WiFip1 = 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
  99. 99. ExampleA = Bedroom u (8guests.Smoker t 8guests.:Smoker) u 8price includes.WiFip1 = hNoSmokingRoom; 0:5ip2 = hSmokingRoom; 0:1ip3 = h8price includes. InternetConnection; 0:4iA v NoSmokingRoomt SmokingRoomA u NoSmokingRoom v :SmokingRoom OntologyA v 8price includes.InternetConnection The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  100. 100. ExampleCL(A; O; U) = fNoSmokingRoomt SmokingRoom ; :NoSmokingRoomt :SmokingRoom ; 8price includes.InternetConnectiong ns + s ¸ 1(1 ¡ ns) + (1 ¡ s) ¸ 1 i ¸ 1min(0:5 ¢ s + 0:1 ¢ ns + 0:4 ¢ i) The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  101. 101. Bilateral matchmaking U1 = 1 1 1 1 fhP1 ; v1 i; : : : ; hPn ; vn igTwo user profiles U2 = 2 2 2 2 fhP1 ; v1 i; : : : ; hPm ; vm ig The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  102. 102. Bilateral matchmaking U1 = 1 1 1 1 fhP1 ; v1 i; : : : ; hPn ; vn ig Two user profiles U2 = 2 2 2 2 fhP1 ; v1 i; : : : ; hPm ; vm igUtility Pareto frontieruser2 Utility user1 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  103. 103. Pareto efficiency• Nash solution: maximize u1 ¢ u2• Welfare: maximize u1 + u2Utilityuser2 Utility user1 The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  104. 104. 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
  105. 105. AcknowledgmentsIn alphabetical order:Simona Colucci, Eugenio Di Sciascio,Francesco M. Donini, Agnese Pinto, AzzurraRagone, Michele Ruta, Eufemia Tinelliand all the other guys at SisInf Lab The 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria
  106. 106. 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
  107. 107. 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
  108. 108. 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
  109. 109. Thank YouThe 6th Int. Conf. on Web Reasoning and Rule Systems – Sep. 10, 2012, Vienna, Austria

×