Detecting fake news using machine learning and reasoning in Description Logics

Reasoning within expressive fuzzy rough description logics

The past 25 years have seen many attempts to introduce defeasible-reasoning capabilities into a description logic setting. Many, if not most, of these attempts are based on preferential extensions of description logics, with a significant number of these, in turn, following the so-called KLM approach to defeasible reasoning initially advocated for propositional logic by Kraus, Lehmann, and Magidor. Each of these attempts has its own aim of investigating particular constructions and variants of the (KLM-style) preferential approach. Here our aim is to provide a comprehensive study of the formal foundations of preferential defeasible reasoning for description logics in the KLM tradition.We start by investigating a notion ofdefeasible subsumptionin the spirit of defeasible conditionals as studied by Kraus, Lehmann, and Magidor in the propositional case. In particular, we consider a natural and intuitive semantics for defeasible subsumption, and we investigate KLM-style syntactic properties for bothpreferentialandrationalsubsumption. Our contribution includes two representation results linking our semantic constructions to the set of preferential and rational properties considered. Besides showing that our semantics is appropriate, these results pave the way for more effective decision procedures for defeasible reasoning in description logics. Indeed, we also analyse the problem of non-monotonic reasoning in description logics at the level ofentailmentand present an algorithm for the computation ofrational closureof a defeasible knowledge base. Importantly, our algorithm relies completely on classical entailment and shows that the computational complexity of reasoning over defeasible knowledge bases is no worse than that of reasoning in the underlying classical DLALC.

Bipolarity is an important feature of spatial information, involved in the expression of preferences and constraints about spatial positioning or in pairs of opposite spatial relations such as left and right. Another important feature is imprecision which has to be taken into account to model vagueness, inherent to many spatial relations (as for instance vague expressions such as close to, to the right of), and to gain in robustness in the representations. In previous works, we have shown that fuzzy sets and fuzzy mathematical morphology are appropriate frameworks, on the one hand, to represent bipolarity and imprecision of spatial relations and, on the other hand, to combine qualitative and quantitative reasoning in description logics extended with fuzzy concrete domains. The purpose of this paper is to integrate the bipolarity feature in the latter logical framework based on bipolar and fuzzy mathematical morphology and description logics with fuzzy concrete domains. Two important issues are addressed in this paper: the modeling of the bipolarity of spatial relations at the terminological level and the integration of bipolar notions in fuzzy description logics. At last, we illustrate the potential of the proposed formalism for spatial reasoning on a simple example in brain imaging.

We propose a formal specification framework for functional aspects of services. We define services as operations which are specified by means of pre- and postconditions, for the specification of which we use extensions of description logic. The (extensions of) description logic and the specification framework itself are defined as institutions. This gives the framework a uniformity of definition and a solid algebraic and logical foundation. The framework can be used for the specification of service requests and service providers. Given a signature morphism from request to provider, we define when a service request is matched by a service provider, which can be used in service discovery. We provide a model-theoretic definition of matching and show that matching can be characterized by a semantic entailment relation which is formulated over a particular standard description logic. Thus proofs of matching can be reduced to standard reasoning in description logic for which one can use description logic reasoners.

Ontology description can use Description Logic(DL) language, so DL's reasoning service can be used in ontology reasoning. In each kind DL, there is a corresponding tableau algorithm to do the reasoning process. There are some sophisticated software to do DL reasoning, they using tree with conflict to decide whether a problem can be using satisfiability to solve. There are a lot of optimization methods to optimize the reasoning service, their attention on the tree structure with conflict. We propose a new algorithm, Heuristic Skipping Rules Algorithm(HSRA), to simply the rule set called by tableau algorithms, and experimentally proved the effectiveness of the HSRA.

Amongst all the methods of machine learning, learning from examples is considered as a key to acquire knowledge automatically. Learning from examples is to obtain a general cover through induction from a given set of positive and negative examples of a concept, which may describe all the positive examples, but reject all the negative examples of that concept. Extension matrix theory, a branch of learning from examples, presents the training examples as two matrices which consist of positive and negative examples respectively, and then finds a path satisfied with a positive example against the background of negative examples. Obviously, such a path is a conjunction of conditions that is satisfied by the positive example. However, most of all of pattern recognition problems, such as handwritten Chinese character recognition, have an overlay area. It is necessary to solve the problem of nonlinear boundaries among different classes when machine learning is applied to acquire the recognition rules. An advanced extension matrix algorithm is proposed in this paper, in which a heuristic search based on the average entropy is used to get the approximate solutions of the minimal complex. A potential function is used to estimate the probability density function of the overlay area between positive and negative examples, so that the nonlinear interfaces of the interclass areas may be obtained. This algorithm is applied to supervised learning for Chinese character recognition to acquire recognition rules, and the implementations upon 751,000 loosely-constrained handwritten Chinese characters indicate that these methodologies can be applied to a practical recognition system with promising results.

The paper introduces a knowledge representation language that combines the event calculus with description logic in a logic programming framework. The purpose is to provide the user with an expressive language for modelling and analysing systems that evolve over time. The approach is exemplified with the logic programming language as implemented in the Fusemate system. The paper extends Fusemate's rule language with a weakly DL-safe interface to the description logic $\cal ALCIF$ and adapts the event calculus to this extended language. This way, time-stamped ABoxes can be manipulated as fluents in the event calculus. All that is done in the frame of Fusemate's concept of stratification by time. The paper provides conditions for soundness and completeness where appropriate. Using an elaborated example it demonstrates the interplay of the event calculus, description logic and logic programming rules for computing possible models as plausible explanations of the current state of the modelled system.

Algebraic tableau reasoning for the description logic [formula omitted

Reasoning with inconsistencies is an important issue for Semantic Web as imperfect information is unavoidable in real applications. For this, different paraconsistent approaches, due to their capacity to draw as nontrivial conclusions by tolerating inconsistencies, have been proposed to reason with inconsistent description logic knowledge bases. However, existing paraconsistent approaches are often criticized for being too skeptical. To this end, this paper presents a non-monotonic paraconsistent version of description logic reasoning, called minimally inconsistent reasoning, where inconsistencies tolerated in the reasoning are minimized so that more reasonable conclusions can be inferred. Some desirable properties are studied, which shows that the new semantics inherits advantages of both non-monotonic reasoning and paraconsistent reasoning. A complete and sound tableau-based algorithm, called multi-valued tableaux, is developed to capture the minimally inconsistent reasoning. In fact, the tableaux algorithm is designed, as a framework for multi-valued DL, to allow for different underlying paraconsistent semantics, with the mere difference in the clash conditions. Finally, the complexity of minimally inconsistent description logic reasoning is shown on the same level as the (classical) description logic reasoning.

Description Logic (DL) Class Expression Learning (CEL) is a recent research topic of interest in the field of machine learning. Given a set of positive and negative examples of individuals in an ontology, the learning problem consists of finding a new class expression or concept such that most of the positive examples are instances of that concept, whereas the negatives examples are not. Therefore, the class expression learning can be seen as a search process in the space of concepts. In this chapter, the use of CEL algorithms is proposed as a tool to find the class expression that describes as much of the instances of positive documents as possible, being the main novelty of the proposal that the ontology is focused on inferring knowledge at syntactic level to determine the orientation of opinion. Furthermore, the use of CEL algorithms can be an alternative to complement other types of classifiers for sentiment analysis, incorporating such description classes as relevant new features into the knowledge base. To do so, an ontology-based text model for the representation of text documents is presented. The process for the ontology population and the use of the class expression learning of sentiment concepts are also described. To show the usefulness and effectiveness of our proposal, we use a set of documents about positive feedback focused on films to learn the positive sentiment concept and to classify the documents, comparing the results obtained against the result obtained by a C4.5 decision tree classifier, using the standard bag of words structure. Finally, we describe the problems that have arisen and solutions that have been adopted in our proposal.

Classifying nodes in knowledge graphs is an important task, e.g., for predicting missing types of entities, predicting which molecules cause cancer, or predicting which drugs are promising treatment candidates. While black-box models often achieve high predictive performance, they are only post-hoc and locally explainable and do not allow the learned model to be easily enriched with domain knowledge. Towards this end, learning description logic concepts from positive and negative examples has been proposed. However, learning such concepts often takes a long time and state-of-the-art approaches provide limited support for literal data values, although they are crucial for many applications. In this paper, we propose EvoLearner - an evolutionary approach to learn concepts in ALCQ(D), which is the attributive language with complement (ALC) paired with qualified cardinality restrictions (Q) and data properties (D). We contribute a novel initialization method for the initial population: starting from positive examples, we perform biased random walks and translate them to description logic concepts. Moreover, we improve support for data properties by maximizing information gain when deciding where to split the data. We show that our approach significantly outperforms the state of the art on the benchmarking framework SML-Bench for structured machine learning. Our ablation study confirms that this is due to our novel initialization method and support for data properties.

Along with the advancement of the world wide web, the rise and far reaching appropriation of the social site initiative have distorted the manner in which news is shaped and distributed. News has gotten quicker, less expensive and effectively available among web based life. This modify has joined a few hindrances also. Specifically, flabbergasting content, for example, fake news made by online networking clients, is getting progressively perilous. The fake news issue, in spite of being presented just because as of late, has become a significant examination theme because of the high substance of online networking. Writing fake remarks and news via web-based networking media is simple for clients. The primary test is to decide the distinction among genuine and fake news. We developed a method for the fake news classification on twitter. Web- based GUI is developed for the fake news classification system to categorize the tweets as fake or genuine. We develop a machine learning program to identify fake news by comparing tweets with genuine sources. Naive bayes and passive aggressive machine learning algorithms are estimated with TF-IDF feature extraction method.

For generalization to be functional, it must occur with a precision that results in acquired responses occurring under appropriate, nontrained conditions, and acquired responses not occurring under inappropriate, nontrained conditions. This study examines the effect of differing types of negative teaching examples on the precision with which generalized grocery item selection is learned. Within a split-multiple baseline design, six young adults identified as mildly, moderately, or severely mentally retarded were trained to select or to reject grocery items using picture cards as cues. The dependent variables were correct selection of 10 trained “positive” grocery items and the correct rejection of 20 nontrained “negative” grocery items in a nontrained grocery store. Participants were trained in a grocery store to select 10 positive grocery examples matching their picture cards and to reject either (a) a set of negative examples that were maximally different from the positive examples, or (b) a set of negative examples that were minimally different from the positive examples. Both training sets resulted in participants correctly selecting the 10 positive items in a nontrained store. Training with the “minimally different” negative examples was functionally related to improved rejection of nontrained negative items in the nontrained store. The implications of teaching with minimally different, negative examples are discussed.

In the last two decades, modal and description logics have provided a theoretical framework for important applications in many areas of computer science. For this reason, the problem of automated reasoning in modal and description logics has been thoroughly investigated. In this chapter we show how efficient Boolean reasoning techniques have been imported, used and integrated into reasoning tools for modal and description logics. To this extent, we focus on modal logics, and in particular mainly on K(m). In particular, we provide some background in modal logics; we describe a basic theoretical framework and we present and analyze the basic tableau-based and DPLL-based techniques; we describe optimizations and extensions of the DPLL-based procedures; we introduce the automata-theoretic/OBDD-based approach; finally, we present the eager approach.

Semantic Web service matchmaking is the process of searching the space of possible matches between demands and supplies, and finds the best available ones. To achieve this, the semantic Web service matchmaking framework is one of the absolutely necessary components of service oriented architecture. This paper proposes a matchmaking framework based on Description Logic for semantic Web service described by RDF4S model, a novel semantic Web service description which consists of three layers: service oriented knowledge base, semantic description model and semantic description language. We describe the service description and service query conditions by using the ontologies that are included in service oriented knowledge base. The service resource specification is described according to Description Logics syntax and a Description Logic reasoner is used to compare service descriptions. By representing the semantics of service descriptions, the matchmaker enables the behavior of an intelligent agent to approach more closely that of a human user trying to locate suitable Web service resources.