MAMAS-tng (MAtch MAking Service - The Next Generation) is a Description Logics reasoning engine, in a subset of OWL-DL (ALN), endowed both of standard reasoning services and some unique non-standard inferences. It is developed by SisInf Research Group at Politecnico di Bari.

MAMAS-tng is a multi-user, multi-ontology Java servlet based system and exposes an exended DIG 1.1 interface [SEE DOCUMENTATION].

MAMAS-tng is available as an HTTP service at: http://dee227.poliba.it:8080/MAMAS-tng/DIG Remember that DIG uses only HTTP-POST requests. If you use your browser to visit the previous link you will be redirected to this page.

MAMAS-tng has applications in several fields where a semantic-based Resource Retrieval [1] is needed. See also [2] for an interesting application in an e-commerce scenario.

Non-standard services include:

Explanation services:

• Concept Abduction  [3,4,5]: Given an ontology O and two concept expressions representing a request D (for Demand) and the resource R to be matched with D, both satisifiable with respect to O. If D is compatible with R - Potential Match - i.e. their conjunction is satisfiable with respect to O, then with Concept Abduction it is possible to compute a concept expression H representing what is underspecified in R in order to completely satisify D - R is classified by D with respect to O - taking into account the information modeled in O. In other words, H represents an explanation on why R is not classified by D with respect to O.
  
  
• Concept Contraction  [4,5]: Given an ontology O and two concept expressions representing a request D (for Demand) and the resource R to be matched with D, both satisifiable with respect to O. If D is NOT compatible with R - Partial Match - i.e. their conjunction is NOT satisfiable with respect to O, then with Concept Contraction it is possible to compute a contraction K (for Keep) of D which is compatible with R taking into account the information modeled in O. The solution computed for the Concept Contraction problem is a pair of concept expressions G (for Give up) and K whose conjunciotn is equivalent to D with respect to O. In other words, G represents an explanation on what in D is not compatible with R (causing a Partial Match).

Semantic matchmaking and ranking:

• Rank Potential  [6]: Given an ontology O and two concept expressions representing a request D (for demand) and the resource R to be matched with D, both satisifiable with respect to O. If D is compatible with R - Potential Match - i.e. their conjunction is satisfiable with respect to O, then Rank Potential returns a score measuring the semantic distance of R from D (R is classified by D with respect to O) taking into account the information modeled in O. The score returned by Rank Potential can be seen as a measure of H for the related Concept Abduction Problem.
  
  
• Rank Partial  [6]: Given an ontology O and two concept expressions representing a request D (for Demand) and the resource R to be matched with D, both satisifiable with respect to O. If D is NOT compatible with R - Partial Match - i.e. their conjunction is NOT satisfiable with respect to O, then Rank Partial returns a score measuring the semantic incompatibility of D and R.

It also features:

• Match Type detection: given an ontology O, allows to determine the match category between a request D and a resource R. In particular, matches are classified within the following categories:
  1. Exact: The resource R is equivalent to the request D.
  2. Full: The resource R is more specific than the request D and then the former completely satisfies the latter.
  3. Plug-In: The request D is more specific than the resource R and then the former completely satisfies the latter.
  4. Potential: The conjunction of D and R is satisfiable with respect to the ontology O.
  5. Partial: The conjunction of D and R is NOT satisfiable with respect to the ontology O.
  
  
  
• Concept Covering [7]: Given an ontology O a concept expressions representing a request D (for Demand) and a set of the resources R = {R_i} all satisifiable with respect to O. Find a subset of R such that the conjunction of all the elements in such subset both is satisfiable with respect to O and is more specific than D. If the conjunction is not more specific than D - Potential Match, the uncovered part of D is also returned.

MAMAS-tng is also accessible using our novel ontology manager OWLEd, which features all named services plus standard ones, and can be used also with other standard inference services

[1] S. Colucci, S. Coppi, T. Di Noia, E. Di Sciascio, F.M. Donini, A. Pinto, A. Ragone. Semantic-Based Resource Retrieval using Non-Standard Inference Services in Description Logics. In Proceedings of Thirteenth Italian Symposium on ADVANCED DATABASE SYSTEMS Sistemi Evoluti per Basi di Dati (SEBD-2005), pp. 232-239, 2005.

[2] S. Colucci, T. Di Noia, E. Di Sciascio,F.M. Donini, and M. Mongiello. Concept Abduction and Contraction for Semantic-based Discovery of Matches and Negotiation Spaces in an E-Marketplace. Electronic Commerce Research and Applications, 4(4):345-361,2005. (doi:10.1016/j.elerap.2005.06.004)

[3] T. Di Noia, E. Di Sciascio, F.M. Donini,M. Mongiello. Abductive matchmaking using description logics. In Proceedings of the Eighteenth International Joint Conference on Artificial Intelligence (IJCAI '03),pp. 337-342, Morgan Kauffmann Acapulco, Messico, August 9-15 2003.

[4] S. Colucci, T. Di Noia, E. Di Sciascio,F.M. Donini, and M. Mongiello. Concept Abduction and Contraction in Description Logics. In Proceedings of the 16th International Workshop on Description Logics (DL'03), volume 81 of CEUR Workshop Proceedings, September 2003.

[5] S. Colucci, T. Di Noia, E. Di Sciascio,F.M. Donini, and M. Mongiello. Uniform Tableaux-Based Approach to Concept Abduction and Contraction in ALN DL. In Proceedings of the 17th International Workshop on Description Logics (DL'04), volume 104 of CEUR Workshop Proceedings, 2004.

[6] T. Di Noia, E. Di Sciascio, F.M. Donini,and M. Mongiello. A system for principled Matchmaking in an electronic marketplace. International Journal of Electronic Commerce,8(4):9-37, 2004. (short version available as: A system for principled Matchmaking in an electronic marketplace. Refereed paper track of Proceedings of the Twelfth International World Wide Web Conference WWW 2003, (ACM press), pp.321-330, 2003.)

[7] T. Di Noia, E. Di Sciascio, and F.M.Donini. Extending and computing the concept covering for the semantic web. Technical report, Tech-Rep n. 21/04/S, 2004.

Developed by SisInf Research Group @ Politecnico di Bari - Italy

Project Coordinators
Tommaso Di Noia
Eugenio Di Sciascio
Francesco Maria Donini