Optimization and inference under fuzzy numerical constraints

Εκτεταμένη έρευνα έχει γίνει στους τομείς της Ικανοποίησης Περιορισμών με διακριτά (ακέραια) ή πραγματικά πεδία τιμών. Αυτή η έρευνα έχει οδηγήσει σε πολλαπλές σημασιολογικές περιγραφές, πλατφόρμες και συστήματα για την περιγραφή σχετικών προβλημάτων με επαρκείς βελτιστοποιήσεις. Παρά ταύτα, λόγω της ασαφούς φύσης πραγματικών προβλημάτων ή ελλιπούς μας γνώσης για αυτά, η σαφής μοντελοποίηση ενός προβλήματος ικανοποίησης περιορισμών δεν είναι πάντα ένα εύκολο ζήτημα ή ακόμα και η καλύτερη προσέγγιση. Επιπλέον, το πρόβλημα της μοντελοποίησης και επίλυσης ελλιπούς γνώσης είναι ακόμη δυσκολότερο. Επιπροσθέτως, πρακτικές απαιτήσεις μοντελοποίησης και μέθοδοι βελτιστοποίησης του χρόνου αναζήτησης απαιτούν συνήθως ειδικές πληροφορίες για το πεδίο εφαρμογής, καθιστώντας τη δημιουργία ενός γενικότερου πλαισίου βελτιστοποίησης ένα ιδιαίτερα δύσκολο πρόβλημα. Στα πλαίσια αυτής της εργασίας θα μελετήσουμε το πρόβλημα της μοντελοποίησης και αξιοποίησης σαφών, ελλιπών ή ασαφών περιορισμών, καθώς και πιθανές στρατηγικές βελτιστοποίησης. Καθώς τα παραδοσιακά προβλήματα ικανοποίησης περιορισμών λειτουργούν βάσει συγκεκριμένων και προκαθορισμένων κανόνων και σχέσεων, παρουσιάζει ενδιαφέρον η διερεύνηση στρατηγικών και βελτιστοποιήσεων που θα επιτρέπουν το συμπερασμό νέων ή/και αποδοτικότερων περιορισμών. Τέτοιοι επιπρόσθετοι κανόνες θα μπορούσαν να βελτιώσουν τη διαδικασία αναζήτησης μέσω της εφαρμογής αυστηρότερων περιορισμών και περιορισμού του χώρου αναζήτησης ή να προσφέρουν χρήσιμες πληροφορίες στον αναλυτή για τη φύση του προβλήματος που μοντελοποιεί.Extensive research has been done in the areas of Constraint Satisfaction with discrete/integer and real domain ranges. Multiple platforms and systems to deal with these kinds of domains have been developed and appropriately optimized. Nevertheless, due to the incomplete and possibly vague nature of real-life problems, modeling a crisp and adequately strict satisfaction problem may not always be easy or even appropriate. The problem of modeling incomplete knowledge or solving an incomplete/relaxed representation of a problem is a much harder issue to tackle. Additionally, practical modeling requirements and search optimizations require specific domain knowledge in order to be implemented, making the creation of a more generic optimization framework an even harder problem.In this thesis, we will study the problem of modeling and utilizing incomplete and fuzzy constraints, as well as possible optimization strategies. As constraint satisfaction problems usually contain hard-coded constraints based on specific problem and domain knowledge, we will investigate whether strategies and generic heuristics exist for inferring new constraint rules. Additional rules could optimize the search process by implementing stricter constraints and thus pruning the search space or even provide useful insight to the researcher concerning the nature of the investigated problem

A survey of large-scale reasoning on the Web of data

As more and more data is being generated by sensor networks, social media and organizations, the Webinterlinking this wealth of information becomes more complex. This is particularly true for the so-calledWeb of Data, in which data is semantically enriched and interlinked using ontologies. In this large anduncoordinated environment, reasoning can be used to check the consistency of the data and of asso-ciated ontologies, or to infer logical consequences which, in turn, can be used to obtain new insightsfrom the data. However, reasoning approaches need to be scalable in order to enable reasoning over theentire Web of Data. To address this problem, several high-performance reasoning systems, whichmainly implement distributed or parallel algorithms, have been proposed in the last few years. Thesesystems differ significantly; for instance in terms of reasoning expressivity, computational propertiessuch as completeness, or reasoning objectives. In order to provide afirst complete overview of thefield,this paper reports a systematic review of such scalable reasoning approaches over various ontologicallanguages, reporting details about the methods and over the conducted experiments. We highlight theshortcomings of these approaches and discuss some of the open problems related to performing scalablereasoning

Knowledge base ontological debugging guided by linguistic evidence

Le résumé en français n'a pas été communiqué par l'auteur.When they grow in size, knowledge bases (KBs) tend to include sets of axioms which are intuitively absurd but nonetheless logically consistent. This is particularly true of data expressed in OWL, as part of the Semantic Web framework, which favors the aggregation of set of statements from multiple sources of knowledge, with overlapping signatures.Identifying nonsense is essential if one wants to avoid undesired inferences, but the sparse usage of negation within these datasets generally prevents the detection of such cases on a strict logical basis. And even if the KB is inconsistent, identifying the axioms responsible for the nonsense remains a non trivial task. This thesis investigates the usage of automatically gathered linguistic evidence in order to detect and repair violations of common sense within such datasets. The main intuition consists in exploiting distributional similarity between named individuals of an input KB, in order to identify consequences which are unlikely to hold if the rest of the KB does. Then the repair phase consists in selecting axioms to be preferably discarded (or at least amended) in order to get rid of the nonsense. A second strategy is also presented, which consists in strengthening the input KB with a foundational ontology, in order to obtain an inconsistency, before performing a form of knowledge base debugging/revision which incorporates this linguistic input. This last step may also be applied directly to an inconsistent input KB. These propositions are evaluated with different sets of statements issued from the Linked Open Data cloud, as well as datasets of a higher quality, but which were automatically degraded for the evaluation. The results seem to indicate that distributional evidence may actually constitute a relevant common ground for deciding between conflicting axioms

Min-based Assertional Merging Approach for Prioritized DL-Lite Knowledge Bases

Lecture Notes in Computer ScienceInternational audienc

OPTIMIZATION OF NONSTANDARD REASONING SERVICES

The increasing adoption of semantic technologies and the corresponding increasing complexity of application requirements are motivating extensions to the standard reasoning paradigms and services supported by such technologies. This thesis focuses on two of such extensions: nonmonotonic reasoning and inference-proof access control. Expressing knowledge via general rules that admit exceptions is an approach that has been commonly adopted for centuries in areas such as law and science, and more recently in object-oriented programming and computer security. The experiences in developing complex biomedical knowledge bases reported in the literature show that a direct support to defeasible properties and exceptions would be of great help. On the other hand, there is ample evidence of the need for knowledge confidentiality measures. Ontology languages and Linked Open Data are increasingly being used to encode the private knowledge of companies and public organizations. Semantic Web techniques facilitate merging different sources of knowledge and extract implicit information, thereby putting at risk security and the privacy of individuals. But the same reasoning capabilities can be exploited to protect the confidentiality of knowledge. Both nonmonotonic inference and secure knowledge base access rely on nonstandard reasoning procedures. The design and realization of these algorithms in a scalable way (appropriate to the ever-increasing size of ontologies and knowledge bases) is carried out by means of a diversified range of optimization techniques such as appropriate module extraction and incremental reasoning. Extensive experimental evaluation shows the efficiency of the developed optimization techniques: (i) for the first time performance compatible with real-time reasoning is obtained for large nonmonotonic ontologies, while (ii) the secure ontology access control proves to be already compatible with practical use in the e-health application scenario.

Knowledge base ontological debugging guided by linguistic evidence

Le résumé en français n'a pas été communiqué par l'auteur.When they grow in size, knowledge bases (KBs) tend to include sets of axioms which are intuitively absurd but nonetheless logically consistent. This is particularly true of data expressed in OWL, as part of the Semantic Web framework, which favors the aggregation of set of statements from multiple sources of knowledge, with overlapping signatures.Identifying nonsense is essential if one wants to avoid undesired inferences, but the sparse usage of negation within these datasets generally prevents the detection of such cases on a strict logical basis. And even if the KB is inconsistent, identifying the axioms responsible for the nonsense remains a non trivial task. This thesis investigates the usage of automatically gathered linguistic evidence in order to detect and repair violations of common sense within such datasets. The main intuition consists in exploiting distributional similarity between named individuals of an input KB, in order to identify consequences which are unlikely to hold if the rest of the KB does. Then the repair phase consists in selecting axioms to be preferably discarded (or at least amended) in order to get rid of the nonsense. A second strategy is also presented, which consists in strengthening the input KB with a foundational ontology, in order to obtain an inconsistency, before performing a form of knowledge base debugging/revision which incorporates this linguistic input. This last step may also be applied directly to an inconsistent input KB. These propositions are evaluated with different sets of statements issued from the Linked Open Data cloud, as well as datasets of a higher quality, but which were automatically degraded for the evaluation. The results seem to indicate that distributional evidence may actually constitute a relevant common ground for deciding between conflicting axioms

Answering Object Queries over Knowledge Bases with Expressive Underlying Description Logics

Many information sources can be viewed as collections of objects and descriptions about objects. The relationship between objects is often characterized by a set of constraints that semantically encode background knowledge of some domain. The most straightforward and fundamental way to access information in these repositories is to search for objects that satisfy certain selection criteria. This work considers a description logics (DL) based representation of such information sources and object queries, which allows for automated reasoning over the constraints accompanying objects. Formally, a knowledge base K=(T, A) captures constraints in the terminology (a TBox) T, and objects with their descriptions in the assertions (an ABox) A, using some DL dialect L. In such a setting, object descriptions are L-concepts and object identifiers correspond to individual names occurring in K. Correspondingly, object queries are the well known problem of instance retrieval in the underlying DL knowledge base K, which returns the identifiers of qualifying objects. This work generalizes instance retrieval over knowledge bases to provide users with answers in which both identifiers and descriptions of qualifying objects are given. The proposed query paradigm, called assertion retrieval, is favoured over instance retrieval since it provides more informative answers to users. A more compelling reason is related to performance: assertion retrieval enables a transfer of basic relational database techniques, such as caching and query rewriting, in the context of an assertion retrieval algebra. The main contributions of this work are two-fold: one concerns optimizing the fundamental reasoning task that underlies assertion retrieval, namely, instance checking, and the other establishes a query compilation framework based on the assertion retrieval algebra. The former is necessary because an assertion retrieval query can entail a large volume of instance checking requests in the form of K|= a:C, where "a" is an individual name and "C" is a L-concept. This work thus proposes a novel absorption technique, ABox absorption, to improve instance checking. ABox absorption handles knowledge bases that have an expressive underlying dialect L, for instance, that requires disjunctive knowledge. It works particularly well when knowledge bases contain a large number of concrete domain concepts for object descriptions. This work further presents a query compilation framework based on the assertion retrieval algebra to make assertion retrieval more practical. In the framework, a suite of rewriting rules is provided to generate a variety of query plans, with a focus on plans that avoid reasoning w.r.t. the background knowledge bases when sufficient cached results of earlier requests exist. ABox absorption and the query compilation framework have been implemented in a prototypical system, dubbed CARE Assertion Retrieval Engine (CARE). CARE also defines a simple yet effective cost model to search for the best plan generated by query rewriting. Empirical studies of CARE have shown that the proposed techniques in this work make assertion retrieval a practical application over a variety of domains