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Mohamed Nazih Omri

Publications and source records attributed to Mohamed Nazih Omri.

At least 19 recordsLinked to original sources
arXiv2019

Recursive algorithms to repair prioritized and inconsistent dl-lite knowledge base

The inconsistency in prioritized knowledge base is because the assertions (ABoxes) come from several sources with different levels of reliability. We introduce the handling of this inconsistency problem to query inconsistent \textit{DL-Lite} knowledge bases. In the literature, firstly, repair all the inconsistent assertions of the \textit{DL-Lite}'s inconsistent knowledge base. Then, interrogate it. However, our algorithm, on proceeds directly with an interrogation of the knowledge base in order to recover an exhaustive list of answers to a given query. In a second time, to repair the answers of this list. The novelty of our article is the proposition of a recurring function that calculates the rank of coherence in order to manage the inconsistencies in the set of responses. This strategy allowed us to reduce execution time compared to existing algorithms. The experimental study as well as the analysis of the results, which we carried out, showed that our algorithm is much more productive than the other algorithms since it gives the greatest number of answers while remaining the best from the point of view of the execution time. Finally, as shown in our experimental studies, they allow an efficient handling of inconsistency. Such facts make all the repairs suitable for \textit{DL-Lite

cs.LO↗
arXiv2016

Mouse Movement and Probabilistic Graphical Models Based E-Learning Activity Recognition Improvement Possibilistic Model

Automatically recognizing the e-learning activities is an important task for improving the online learning process. Probabilistic graphical models such as hidden Markov models and conditional random fields have been successfully used in order to identify a Web users activity. For such models, the sequences of observation are crucial for training and inference processes. Despite the efficiency of these probabilistic graphical models in segmenting and labeling stochastic sequences, their performance is adversely affected by the imperfect quality of data used for the construction of sequences of observation. In this paper, a formalism of the possibilistic theory will be used in order to propose a new approach for observation sequences preparation. The eminent contribution of our approach is to evaluate the effect of possibilistic reasoning during the generation of observation sequences on the effectiveness of hidden Markov models and conditional random fields models. Using a dataset containing 51 real manipulations related to three types of learners tasks, the preliminary experiments demonstrate that the sequences of observation obtained based on possibilistic reasoning significantly improve the performance of hidden Marvov models and conditional random fields models in the automatic recognition of the e-learning activities.

cs.AI↗
arXiv2014

Hidden Markov Model for Inferring Learner Task Using Mouse Movement

One of the issues of e-learning web based application is to understand how the learner interacts with an e-learning application to perform a given task. This study proposes a methodology to analyze learner mouse movement in order to infer the task performed. To do this, a Hidden Markov Model is used for modeling the interaction of the learner with an e-learning application. The obtained results show the ability of our model to analyze the interaction in order to recognize the task performed by the learner.

cs.HC↗
arXiv2013

Relevance Feedback for Goal's Extraction from Fuzzy Semantic Networks

In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries is the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of classical Knowledge Extraction techniques for novice user's query processing is robust to considerably high levels of goal recognition errors. Moreover, both standard relevance feedback and pseudo relevance feedback can be effectively employed to improve the effectiveness of novice user's query processing.

cs.IR↗
arXiv2012

Uncertain and Approximative Knowledge Representation to Reasoning on Classification with a Fuzzy Networks Based System

The approach described here allows to use the fuzzy Object Based Representation of imprecise and uncertain knowledge. This representation has a great practical interest due to the possibility to realize reasoning on classification with a fuzzy semantic network based system. For instance, the distinction between necessary, possible and user classes allows to take into account exceptions that may appear on fuzzy knowledge-base and facilitates integration of user's Objects in the base. This approach describes the theoretical aspects of the architecture of the whole experimental A.I. system we built in order to provide effective on-line assistance to users of new technological systems: the understanding of "how it works" and "how to complete tasks" from queries in quite natural languages. In our model, procedural semantic networks are used to describe the knowledge of an "ideal" expert while fuzzy sets are used both to describe the approximative and uncertain knowledge of novice users in fuzzy semantic networks which intervene to match fuzzy labels of a query with categories from our "ideal" expert.

cs.AI↗
arXiv2012

Softening Fuzzy Knowledge Representation Tool with the Learning of New Words in Natural Language

The approach described here allows using membership function to represent imprecise and uncertain knowledge by learning in Fuzzy Semantic Networks. This representation has a great practical interest due to the possibility to realize on the one hand, the construction of this membership function from a simple value expressing the degree of interpretation of an Object or a Goal as compared to an other and on the other hand, the adjustment of the membership function during the apprenticeship. We show, how to use these membership functions to represent the interpretation of an Object (respectively of a Goal) user as compared to an system Object (respectively to a Goal). We also show the possibility to make decision for each representation of an user Object compared to a system Object. This decision is taken by determining decision coefficient calculates according to the nucleus of the membership function of the user Object.

cs.AI↗
arXiv2012

Fuzzy Knowledge Representation, Learning and Optimization with Bayesian Analysis in Fuzzy Semantic Networks

This paper presents a method of optimization, based on both Bayesian Analysis technical and Gallois Lattice, of a Fuzzy Semantic Networks. The technical System we use learn by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by these kinds of learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an inductive Bayesian analysis, on the Net obtained from Gallois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.

cs.AI↗
arXiv2012

Optimization of Fuzzy Semantic Networks Based on Galois Lattice and Bayesian Formalism

This paper presents a method of optimization, based on both Bayesian Analysis technical and Galois Lattice of Fuzzy Semantic Network. The technical System we use learns by interpreting an unknown word using the links created between this new word and known words. The main link is provided by the context of the query. When novice's query is confused with an unknown verb (goal) applied to a known noun denoting either an object in the ideal user's Network or an object in the user's Network, the system infer that this new verb corresponds to one of the known goal. With the learning of new words in natural language as the interpretation, which was produced in agreement with the user, the system improves its representation scheme at each experiment with a new user and, in addition, takes advantage of previous discussions with users. The semantic Net of user objects thus obtained by learning is not always optimal because some relationships between couple of user objects can be generalized and others suppressed according to values of forces that characterize them. Indeed, to simplify the obtained Net, we propose to proceed to an Inductive Bayesian Analysis, on the Net obtained from Galois lattice. The objective of this analysis can be seen as an operation of filtering of the obtained descriptive graph.

cs.IR↗
arXiv2012

A Fuzzy Approach for Pertinent Information Extraction from Web Resources

Recent work in machine learning for information extraction has focused on two distinct sub-problems: the conventional problem of filling template slots from natural language text, and the problem of wrapper induction, learning simple extraction procedures ("wrappers") for highly structured text such as Web pages. For suitable regular domains, existing wrapper induction algorithms can efficiently learn wrappers that are simple and highly accurate, but the regularity bias of these algorithms makes them unsuitable for most conventional information extraction tasks. This paper describes a new approach for wrapping semistructured Web pages. The wrapper is capable of learning how to extract relevant information from Web resources on the basis of user supplied examples. It is based on inductive learning techniques as well as fuzzy logic rules. Experimental results show that our approach achieves noticeably better precision and recall coefficient performance measures than SoftMealy, which is one of the most recently reported wrappers capable of wrapping semi-structured Web pages with missing attributes, multiple attributes, variant attribute permutations, exceptions, and typos.

cs.IR↗
arXiv2012

Fuzzy Knowledge Representation Based on Possibilistic and Necessary Bayesian Networks

Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge. This paper proposes a framework that combines two disciplines to exploit their own advantages in uncertain and imprecise knowledge representation problems. The framework proposed is a possibilistic logic based one in which Bayesian nodes and their properties are represented by local necessity-valued knowledge base. Data in properties are interpreted as set of valuated formulas. In our contribution possibilistic Bayesian networks have a qualitative part and a quantitative part, represented by local knowledge bases. The general idea is to study how a fusion of these two formalisms would permit representing compact way to solve efficiently problems for knowledge representation. We show how to apply possibility and necessity measures to the problem of knowledge representation with large scale data. On the other hand fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The general aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic.

cs.AI↗
arXiv2012

Possibilistic Pertinence Feedback and Semantic Networks for Goal's Extraction

Pertinence Feedback is a technique that enables a user to interactively express his information requirement by modifying his original query formulation with further information. This information is provided by explicitly confirming the pertinent of some indicating objects and/or goals extracted by the system. Obviously the user cannot mark objects and/or goals as pertinent until some are extracted, so the first search has to be initiated by a query and the initial query specification has to be good enough to pick out some pertinent objects and/or goals from the Semantic Network. In this paper we present a short survey of fuzzy and Semantic approaches to Knowledge Extraction. The goal of such approaches is to define flexible Knowledge Extraction Systems able to deal with the inherent vagueness and uncertainty of the Extraction process. It has long been recognised that interactivity improves the effectiveness of Knowledge Extraction systems. Novice user's queries are the most natural and interactive medium of communication and recent progress in recognition is making it possible to build systems that interact with the user. However, given the typical novice user's queries submitted to Knowledge Extraction Systems, it is easy to imagine that the effects of goal recognition errors in novice user's queries must be severely destructive on the system's effectiveness. The experimental work reported in this paper shows that the use of possibility theory in classical Knowledge Extraction techniques for novice user's query processing is more robust than the use of the probability theory. Moreover, both possibilistic and probabilistic pertinence feedback can be effectively employed to improve the effectiveness of novice user's query processing.

cs.AI↗
arXiv2012

Pertinent Information retrieval based on Possibilistic Bayesian network : origin and possibilistic perspective

In this paper we present a synthesis of work performed on tow information retrieval models: Bayesian network information retrieval model witch encode (in) dependence relation between terms and possibilistic network information retrieval model witch make use of necessity and possibility measures to represent the fuzziness of pertinence measure. It is known that the use of a general Bayesian network methodology as the basis for an IR system is difficult to tackle. The problem mainly appears because of the large number of variables involved and the computational efforts needed to both determine the relationships between variables and perform the inference processes. To resolve these problems, many models have been proposed such as BNR model. Generally, Bayesian network models doesn't consider the fuzziness of natural language in the relevance measure of a document to a given query and possibilistic models doesn't undertake the dependence relations between terms used to index documents. As a first solution we propose a hybridization of these two models in one that will undertake both the relationship between terms and the intrinsic fuzziness of natural language. We believe that the translation of Bayesian network model from the probabilistic framework to possibilistic one will allow a performance improvement of BNRM.

cs.IR↗
arXiv2012

Loopy Belief Propagation in Bayesian Networks : origin and possibilistic perspectives

In this paper we present a synthesis of the work performed on two inference algorithms: the Pearl's belief propagation (BP) algorithm applied to Bayesian networks without loops (i.e. polytree) and the Loopy belief propagation (LBP) algorithm (inspired from the BP) which is applied to networks containing undirected cycles. It is known that the BP algorithm, applied to Bayesian networks with loops, gives incorrect numerical results i.e. incorrect posterior probabilities. Murphy and al. [7] find that the LBP algorithm converges on several networks and when this occurs, LBP gives a good approximation of the exact posterior probabilities. However this algorithm presents an oscillatory behaviour when it is applied to QMR (Quick Medical Reference) network [15]. This phenomenon prevents the LBP algorithm from converging towards a good approximation of posterior probabilities. We believe that the translation of the inference computation problem from the probabilistic framework to the possibilistic framework will allow performance improvement of LBP algorithm. We hope that an adaptation of this algorithm to a possibilistic causal network will show an improvement of the convergence of LBP.

cs.AI↗
arXiv2012

Use of Fuzzy Sets in Semantic Nets for Providing On-Line Assistance to User of Technological Systems

The main objective of this paper is to develop a new semantic Network structure, based on the fuzzy sets theory, used in Artificial Intelligent system in order to provide effective on-line assistance to users of new technological systems. This Semantic Networks is used to describe the knowledge of an "ideal" expert while fuzzy sets are used both to describe the approximate and uncertain knowledge of novice users who intervene to match fuzzy labels of a query with categories from an "ideal" expert. The technical system we consider is a word processor software, with Objects such as "Word" and Goals such as "Cut" or "Copy". We suggest to consider the set of the system's Goals as a set of linguistic variables to which corresponds a set of possible linguistic values based on the fuzzy set. We consider, therefore, a set of interpretation's levels for these possible values to which corresponds a set of membership functions. We also propose a method to measure the similarity degree between different fuzzy linguistic variables for the partition of the semantic network in class of similar objects to make easy the diagnosis of the user's fuzzy queries.

cs.AI↗
arXiv2012

Certain Bayesian Network based on Fuzzy knowledge Bases

In this paper, we are trying to examine trade offs between fuzzy logic and certain Bayesian networks and we propose to combine their respective advantages into fuzzy certain Bayesian networks (FCBN), a certain Bayesian networks of fuzzy random variables. This paper deals with different definitions and classifications of uncertainty, sources of uncertainty, and theories and methodologies presented to deal with uncertainty. Fuzzification of crisp certainty degrees to fuzzy variables improves the quality of the network and tends to bring smoothness and robustness in the network performance. The aim is to provide a new approach for decision under uncertainty that combines three methodologies: Bayesian networks certainty distribution and fuzzy logic. Within the framework proposed in this paper, we address the issue of extending the certain networks to a fuzzy certain networks in order to cope with a vagueness and limitations of existing models for decision under imprecise and uncertain knowledge.

cs.AI↗
arXiv2012

VIQI: A New Approach for Visual Interpretation of Deep Web Query Interfaces

Deep Web databases contain more than 90% of pertinent information of the Web. Despite their importance, users don't profit of this treasury. Many deep web services are offering competitive services in term of prices, quality of service, and facilities. As the number of services is growing rapidly, users have difficulty to ask many web services in the same time. In this paper, we imagine a system where users have the possibility to formulate one query using one query interface and then the system translates query to the rest of query interfaces. However, interfaces are created by designers in order to be interpreted visually by users, machines can not interpret query from a given interface. We propose a new approach which emulates capacity of interpretation of users and extracts query from deep web query interfaces. Our approach has proved good performances on two standard datasets.

cs.IR↗