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Exact Learning of TBoxes in EL and DL-Lite

Boris Konev, Carsten Lutz, Frank Wolter
2013 International Workshop on Description Logics  
We study polynomial time learning of description logic TBoxes in Angluin et al.'s framework of exact learning via queries.  ...  nor acyclic EL TBoxes can be learned in polynomial time. (3) EL TBoxes with only concept names on the right-hand side of concept inclusions can be learned in polynomial time.  ...  It is also an interesting open question whether DL-LiteTBoxes can be learned in polynomial time using equivalence queries only.  ... 
dblp:conf/dlog/KonevLW13 fatcat:app6xwatlva6fh63k73zmbrd2q

A Model for Learning Description Logic Ontologies Based on Exact Learning

Boris Konev, Ana Ozaki, Frank Wolter
We investigate the problem of learning description logic (DL) ontologies in Angluin et al.'s framework of exact learning via queries posed to an oracle.  ...  fragments of EL with role inclusions and of DL-Lite and for data retrieval queries that range from atomic queries and EL/ELI-instance queries to conjunctive queries.  ...  This is in contrast to polynomial query learnability of DL-Lite ∃ H and EL rhs TBoxes using data retrieval examples with ELI-IQs and, respectively, EL-IQs.  ... 
doi:10.1609/aaai.v30i1.10087 fatcat:vcnpftst25cq5llhsg4raprwty

Exact Learning Description Logic Ontologies from Data Retrieval Examples

Boris Konev, Ana Ozaki, Frank Wolter
2015 International Workshop on Description Logics  
We show that (i) DL-Lite TBoxes with role inclusions and ELI concept expressions on the right-hand side of inclusions and (ii) EL TBoxes without complex concept expressions on the right-hand side of inclusions  ...  We investigate the complexity of learning description logic ontologies in Angluin et al.'s framework of exact learning via queries posed to an oracle.  ...  A DL-Lite ∃ R TBox is a finite set of DL-Lite ∃ R CIs and RIs.  ... 
dblp:conf/dlog/KonevOW15 fatcat:bdc2ne574vb27amkrt2p2glguy

Exact Learning of Lightweight Description Logic Ontologies [article]

Boris Konev, Carsten Lutz, Ana Ozaki, Frank Wolter
2017 arXiv   pre-print
We study the problem of learning description logic (DL) ontologies in Angluin et al.'s framework of exact learning via queries.  ...  We present three main results: (1) ontologies formulated in (two relevant versions of) the description logic DL-Lite can be learned with polynomially many queries of polynomial size; (2) this is not the  ...  Acknowledgements Lutz was supported by the DFG project Prob-DL (LU1417/1-1). Konev and Wolter were supported by the EPSRC project EP/H043594/1.  ... 
arXiv:1709.07314v1 fatcat:2tebaajqsfhr3nvaqzhchi7fza

On the Complexity of Learning Description Logic Ontologies [article]

Ana Ozaki
2021 arXiv   pre-print
We provide a formal specification of the exact and the probably approximately correct learning models from computational learning theory.  ...  Finally, we highlight other approaches proposed in the literature for learning DL ontologies.  ...  Therefore, F(DL-Lite R ) belongs to ElP(MQ). Learning an equivalent DL-Lite R TBox with only equivalence queries is also easy.  ... 
arXiv:2103.13694v1 fatcat:e34yjgnkkvezvpqokn742j7nnq

Probably Approximately Correct Completion of Description Logic Knowledge Bases

Sergei Obiedkov, Baris Sertkaya, Denis Zolotukhin
2019 International Workshop on Description Logics  
Our approach is based on Angluin's exact learning framework and on the attribute exploration method from Formal Concept Analysis.  ...  By asking implication questions to a domain expert, our method approximates the subsumption relationships that hold in expert's model and enriches the TBox with the newly discovered relationships between  ...  The exact learning version of this problem has already been studied in detail in [13] and relevant fragments of EL and DL-Lite have been identified that allow for learnability of ontologies with polynomial  ... 
dblp:conf/dlog/ObiedkovSZ19 fatcat:ylnnupmyqvbh7gqjxhtghbg6xe

Which Kind of Module Should I Extract?

Ulrike Sattler, Thomas Schneider, Michael Zakharyaschev
2009 International Workshop on Description Logics  
In this paper, we survey existing logic-based approaches, focus on syntactic approximations, and compare different kinds of modules with respect to their properties.  ...  These differ with respect to the size of the module, the complexity of its computation, and certain robustness properties.  ...  For the DL-Lite family [2] , deciding dCEs is Π p 2 -complete for DL-Lite bool and coNP-complete for DL-Lite horn [11] , i.e., it is most likely intractable in both cases.  ... 
dblp:conf/dlog/SattlerSZ09 fatcat:ouqi7ytuynethgsxauv747aiky

Towards Scalable Ontological Reasoning using Machine Learning

Daniel Ruffinelli
2017 International Web Rule Symposium  
Finally, it will be important to determine the degree of completeness and correctness of such methods based on machine learning, and compare them with approximate methods based on standard reasoning.  ...  For this purpose, we will study the use of currently available approximation approaches, and we will develop new machine learning based methods to compete with them.  ...  An example of this is the DL-Lite family of description logics, which allow for the definition of basic ontological languages while providing reasoning tasks in polynomial time [3] .  ... 
dblp:conf/ruleml/Ruffinelli17 fatcat:u6dyxywydrarra5qkk52dxb6wi

Chapter 3 Description Logics [chapter]

Franz Baader, Ian Horrocks, Ulrike Sattler
2008 Foundations of Artificial Intelligence  
In the terminological part, called the TBox, we can describe the relevant notions of an application domain by stating properties of concepts and roles, and relationships between them-it corresponds to  ...  Concept descriptions can be used to build statements in a DL knowledge base, which typically comes in two parts: a terminological and an assertional one.  ...  In fact there are 3 "species" of OWL: OWL Lite, OWL DL and OWL full, only the first two of which have DL based semantics.  ... 
doi:10.1016/s1574-6526(07)03003-9 fatcat:wa2lwv7ywrhvpnk3emfqv2pgyu

A Rigorous Characterization of Classification Performance - A Tale of Four Reasoners

Yong-Bin Kang, Yuan-Fang Li, Shonali Krishnaswamy
2012 International Workshop on OWL Reasoner Evaluation  
A number of ontology reasoners have been developed for reasoning over highly expressive ontology languages such as OWL DL and OWL 2 DL.  ...  In this paper, we carry out a comprehensive comparative study to analyze classification performance of four widely-used reasoners, FaCT++, HermiT, Pellet and TrOWL, using a dataset of over 300 real-world  ...  such as EL ++ (the OWL 2 EL profile) and DL-Lite (the OWL 2 QL profile), for which polynomialtime algorithms exist for standard DL inference tasks such as subsumption checking [1, 8] .  ... 
dblp:conf/ore/KangLK12 fatcat:zvavwcerfncl5jmc5ntuag3hvy

Habilitation à diriger des recherches

2015 Revue Mabillon  
Data management is a longstanding research topic in Knowledge Representation (KR), a prominent discipline of Artificial Intelligence (AI), and -of course -in Databases (DB).  ...  In particular, my work has covered (i) the design, (ii) the optimization, and (iii) the decentralization of ontology-based data management techniques in these data models.  ...  We define the SOMEOWL, SOMERDFS, and SOMEDL-LITE PDMSs, whose data models are the CLU description logic 3 , RDF, and DL-lite respectively.  ... 
doi:10.1484/j.rm.5.110608 fatcat:5dgccuz6zff3tpmhvvo2qprori

Conglomerating First Order, Descriptive and Modal Logics into Semantic Web – A Research

This evaluation paper digs into numerous vast research issues in depiction motive in the course of the last 2 pretty some time presenting the relationship amongst right judgment households and spotlights  ...  format oversee and insights bases extra frequently than not and in semantic internet uniquely.  ...  that exact a piece of the skill base occurrence.  ... 
doi:10.35940/ijitee.f1260.0486s419 fatcat:xebvzpxyhfcjri2xma2p5nrpie

On the possibility of correct concept learning in description logics

Ali Rezaei Divroodi, Quang-Thuy Ha, Linh Anh Nguyen, Hung Son Nguyen
2017 Vietnam Journal of Computer Science  
For this result, we introduce universal interpretations and bounded bisimulation in description logics and develop an appropriate learning algorithm.  ...  That is, there exists a learning algorithm such that, for every concept C of those logics, there exists a training information system such that applying the learning algorithm to it results in a concept  ...  , and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made.  ... 
doi:10.1007/s40595-017-0094-4 fatcat:l5x2egtolvcozjm6mugztstoka

Learning Query Inseparable ELH Ontologies [article]

Ana Ozaki, Cosimo Persia, Andrea Mazzullo
2020 arXiv   pre-print
We investigate the complexity of learning query inseparable ELH ontologies in a variant of Angluin's exact learning model.  ...  A* and Q. The learner is allowed to pose two kinds of questions. The first is 'Does (T,A)\models q?', with A an arbitrary data instance and q and query in Q.  ...  Acknowledgments This research has been supported by the Free University of Bozen-Bolzano through the projects PACO and MLEARN.  ... 
arXiv:1911.07229v5 fatcat:fetip4iv5bebldseanrn6kq7om

Frontiers and Exact Learning of ELI Queries under DL-Lite Ontologies [article]

Maurice Funk, Jean Christoph Jung, Carsten Lutz
2022 arXiv   pre-print
We use out results on frontiers to show that ELIQs are learnable in polynomial time in the presence of a DL-LiteH / restricted DL-LiteF ontology in Angluin's framework of exact learning with only membership  ...  We study ELI queries (ELIQs) in the presence of ontologies formulated in the description logic DL-Lite.  ...  We then consider in detail the application of our results in the context of exact learning and show that ELIQs can be learned in polynomial time w.r.t. ontologies O formulated in DL-Lite H or DL-Lite F  ... 
arXiv:2204.14172v1 fatcat:6bedpwuhergazh3enutshxo44a
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