Closed World Assumption Research Articles

Logic Negation with Spiking Neural P Systems

Nowadays, the success of neural networks as reasoning systems is doubtless. Nonetheless, one of the drawbacks of such reasoning systems is that they work as black-boxes and the acquired knowledge is not human readable. In this paper, we present a new step in order to close the gap between connectionist and logic based reasoning systems. We show that two of the most used inference rules for obtaining negative information in rule based reasoning systems, the so-called Closed World Assumption and Negation as Finite Failure can be characterized by means of spiking neural P systems, a formal model of the third generation of neural networks born in the framework of membrane computing.

Towards Robust Pattern Recognition: A Review

The accuracies for many pattern recognition tasks have increased rapidly year by year, achieving or even outperforming human performance. From the perspective of accuracy, pattern recognition seems to be a nearly solved problem. However, once launched in real applications, the high-accuracy pattern recognition systems may become unstable and unreliable due to the lack of robustness in open and changing environments. In this article, we present a comprehensive review of research toward robust pattern recognition from the perspective of breaking three basic and implicit assumptions: closed-world assumption, independent and identically distributed assumption, and clean and big data assumption, which form the foundation of most pattern recognition models. Actually, our brain is robust at learning concepts continually and incrementally, in complex, open, and changing environments, with different contexts, modalities, and tasks, by showing only a few examples, under weak or noisy supervision. These are the major differences between human intelligence and machine intelligence, which are closely related to the above three assumptions. After witnessing the significant progress in accuracy improvement nowadays, this review paper will enable us to analyze the shortcomings and limitations of current methods and identify future research directions for robust pattern recognition.

Polynomial rewritings from expressive Description Logics with closed predicates to variants of Datalog

In many scenarios, complete and incomplete information coexist. For this reason, the knowledge representation and database communities have long shown interest in simultaneously supporting the closed- and the open-world views when reasoning about logic theories. Here we consider the setting of querying possibly incomplete data using logic theories, formalized as the evaluation of an ontology-mediated query (OMQ) that pairs a query with a theory, sometimes called an ontology, expressing background knowledge. This can be further enriched by specifying a set of closed predicates from the theory that are to be interpreted under the closed-world assumption, while the rest are interpreted with the open-world view. In this way we can retrieve more precise answers to queries by leveraging the partial completeness of the data.The central goal of this paper is to understand the relative expressiveness of ontology-mediated query languages in which the ontology part is written in the expressive Description Logic (DL) ALCHOI and includes a set of closed predicates. We consider a restricted class of conjunctive queries. Our main result is to show that every query in this non-monotonic query language can be translated in polynomial time into Datalog with negation as failure under the stable model semantics. To overcome the challenge that Datalog has no direct means to express the existential quantification present in ALCHOI, we define a two-player game that characterizes the satisfaction of the ontology, and design a Datalog query that can decide the existence of a winning strategy for the game. If there are no closed predicates—in the case of querying an ALCHOI knowledge base—our translation yields a positive disjunctive Datalog program of polynomial size. To the best of our knowledge, unlike previous translations for related fragments with expressive (non-Horn) DLs, these are the first polynomial time translations.

Initialize once, start fast: application initialization at build time

Arbitrary program extension at run time in language-based VMs, e.g., Java's dynamic class loading, comes at a startup cost: high memory footprint and slow warmup. Cloud computing amplifies the startup overhead. Microservices and serverless cloud functions lead to small, self-contained applications that are started often. Slow startup and high memory footprint directly affect the cloud hosting costs, and slow startup can also break service-level agreements. Many applications are limited to a prescribed set of pre-tested classes, i.e., use a closed-world assumption at deployment time. For such Java applications, GraalVM Native Image offers fast startup and stable performance. GraalVM Native Image uses a novel iterative application of points-to analysis and heap snapshotting, followed by ahead-of-time compilation with an optimizing compiler. Initialization code can run at build time, i.e., executables can be tailored to a particular application configuration. Execution at run time starts with a pre-populated heap, leveraging copy-on-write memory sharing. We show that this approach improves the startup performance by up to two orders of magnitude compared to the Java HotSpot VM, while preserving peak performance. This allows Java applications to have a better startup performance than Go applications and the V8 JavaScript VM.

Knowledge Based System using Ontology for Accessing Sentiments of Indian Railways Tweets

The interactions in social networks like Twitter are reflecting sentiments of people at large. Especially opinion mining has wherewithal to provide social feedback that complements traditional feedback for making strategic decisions. The sentiments data is basically stored in the Relational Data Base. But the demerits of this data base is that it comes back with an answer at most once and it is very difficult to address complex queries over the data, lagging inherent properties such as transitivity or symmetry. It also has closed world assumption i.e., what is not known to be true in the data base is by default considered as false because knowledge represented in the data base is assumed to be complete. To overcome all these demerits, we have developed knowledge-based system using ontology, for accessing sentiments of Indian Railways tweets. Indian Railways (IR) is very huge organization which consistently strives to improve its services from time to time. In our prior work, a framework was proposed and implemented using sentiment analysis for Indian railways tweets. The results were grouped into clusters based on five attributes pertaining to IR such as Cleanliness, Staff Behaviour, Punctuality, Security and Timeliness. These clusters are being updated from time to time to reflect up to date social feedback. However, the problem with the existing system is that, its accessibility and ease of use to stakeholders is not easy. So, we proposed a knowledge based system which will represent clusters for a universal and interoperable data representation that is from database to RDF schema to ontology and apply inference rules and query using SPARQL. This system is accessible to humans and also programs in heterogeneous Machine-to-Machine (M2M) environments. We proposed a methodology to achieve this and the knowledge is made available for further processing and stakeholders can access. The proposed system is evaluated with a prototype application and found to be useful and flexible.

Context-aware composite SaaS using feature model

Abstract Software as a Service (SaaS) in cloud computing is delivered in a composite form to effectively address complex levels of user’s requirements. Composite SaaS runs in a dynamic distributed cloud environment where the quality of service (QoS) properties of the constituents may get violated at runtime. To face such dynamism and volatility, it is vital to support an online adaptation of composite SaaS. Recent research focused on centralized adaptation approaches based on the closed world assumption that the boundary between SaaS and the cloud environment is known. This is impractical for dynamic composition that requires distributed settings in the open world. To address these challenges, this paper proposes a distributed approach for composite SaaS adaptation applying the master/slave pattern. Slaves locally monitor and adapt the distributed SaaS constituents and send performance information to the master, which adapts the composite service to provide the global expected QoS and monitors the overall performance. To support dynamic adaptation by the master, we propose a solution based on the feature model that captures the variability of the composite SaaS. The activation and deactivation of nodes in the feature model reconfigure the workflow of the composition. Since the reconfiguration task is complex, we apply a meta-heuristic search technique to solve this problem while minimizing the adaptation cost (i.e., resource consumption and violation penalties). Furthermore, we propose an adaption approach for SaaS constituents that substitutes the failed ones promptly to avoid costly global SLA violations. Finally, we present a Kalman-based on-line QoS prediction approach for making decisions regarding the adaptation actions to be taken. Experimental results show that our approach is efficient in distributed and large-scale cloud environments compared to the centralized and off-line approaches.

A logical framework for view updating in indefinite databases

AbstractThis paper introduces and studies a declarative framework for updating views over indefinite databases. An indefinite database is a database with null values that are represented, following the standard database approach, by a single null constant. Typically a database is represented by a single set of facts $D$ that model what is known to be true. This paper proposes a model of an indefinite extensional database that is more expressive with respect to the closed-world assumption (CWA) adapted for the setting of indefinite databases (Libkin, 1995, A semantics-based approach to design of query languages for partial information. In Semantics in Databases, vol. 1358 of LNCS, pp. 170–208. Springer; der Meyden, 1998, Logical approaches to incomplete information: a survey. In Logics for Databases and Information Systems, pp. 307–356. Kluwer Academic Publishers, Norwell, MA, USA). More specifically, in our model, databases are determined by two sets of facts: $D$ representing, as usual, all facts that are true in the database, and $E$ that is meant to represent exceptions to the ‘unknown range’, i.e. facts that cannot be unknown. Intuitively, unless they are explicitly implied to be true by the first set, the facts specified as exceptions are assumed false. The semantics is given by means of a two-level CWA tailored to the case of indefinite information. The paper characterizes the semantics of indefinite databases in terms of their possible worlds that are obtained by instantiating occurrences of null values by concrete constants and defines several classes of database repairs that realize view-update requests. Most notable is the class of constrained repairs. Constrained repairs change the database ‘minimally’and avoid making arbitrary commitments. They narrow down the space of alternative ways to fulfill the view-update request to those that are grounded, in a certain strong sense, in the database, the view and the view-update request.

A Note on Incomplete Information Modeling in the Evidence Theory

Incomplete information exists widely in practical engineering. How to model the incomplete information in the Dempster-Shafer evidence theory (DST) framework is still an open issue. In this paper, we present a framework on incomplete information processing in DST. We extend the model of uncertain information from the closed world assumption to the open world assumption where the incomplete information is a new type of uncertain information excluded in the closed world assumption. After that, we give a framework of incomplete information modelling and fusion in the DST framework. Finally, an example of decision-making process in air combat situation assessment with incomplete situation is introduced to show the rationality of the proposed method.

Knowledge Completion for Generics using Guided Tensor Factorization

Given a knowledge base or KB containing (noisy) facts about common nouns or generics, such as “all trees produce oxygen” or “some animals live in forests”, we consider the problem of inferring additional such facts at a precision similar to that of the starting KB. Such KBs capture general knowledge about the world, and are crucial for various applications such as question answering. Different from commonly studied named entity KBs such as Freebase, generics KBs involve quantification, have more complex underlying regularities, tend to be more incomplete, and violate the commonly used locally closed world assumption (LCWA). We show that existing KB completion methods struggle with this new task, and present the first approach that is successful. Our results demonstrate that external information, such as relation schemas and entity taxonomies, if used appropriately, can be a surprisingly powerful tool in this setting. First, our simple yet effective knowledge guided tensor factorization approach achieves state-of-the-art results on two generics KBs (80% precise) for science, doubling their size at 74%–86% precision. Second, our novel taxonomy guided, submodular, active learning method for collecting annotations about rare entities (e.g., oriole, a bird) is 6x more effective at inferring further new facts about them than multiple active learning baselines.

Nonmonotonicity in the Framework of Parametric Logic

Parametric logic is a framework that generalises classical first-order logic. A generalised notion of logical consequence—a form of preferential entailment based on a closed world assumption—is defined as a function of some parameters. A concept of possible knowledge base—the counterpart to the consistent theories of first-order logic—is introduced. The notion of compactness is weakened. The degree of weakening is quantified by a nonnull ordinal—the larger the ordinal, the more significant the weakening. For every possible knowledge base T, a hierarchy of sentences that are generalised logical consequences of T is built. The first layer of the hierarchies corresponds to sentences that can be obtained by a deductive inference, characterised by the compactness property. The second layer of the hierarchies corresponds to sentences that can be obtained by an inductive inference, characterised by the property of weak compactness quantified by 1. Weaker forms of compactness—quantified by nonnull ordinals—determine higher layers in the hierarchies, corresponding to more complex inferences. The naturalness of the hierarchies built over the possible knowledge bases is attested by fundamental connections with notions from Learning theory and from topology. The naturalness of the hierarchies built over the possible knowledge bases is attested by fundamental connections with notions from Learning theory—classification in the limit, with or without a bounded number of mind changes—and from topology—in reference to the Borel and the difference hierarchies. In this paper, we introduce the key model-theoretic aspects of Parametric logic, justify the concept of the knowledge base, define the hierarchies of generalised logical consequences and illustrate their relevance to Nonmonotonic reasoning. More specifically, we show that the degree of nonmonotonicity that is required to infer a sentence can be characterised by the least nonnull ordinal that quantifies the weakening of compactness used to locate the inferred sentence in the hierarchies.

Open-World Knowledge Graph Completion

Knowledge Graphs (KGs) have been applied to many tasks including Web search, link prediction, recommendation, natural language processing, and entity linking. However, most KGs are far from complete and are growing at a rapid pace. To address these problems, Knowledge Graph Completion (KGC) has been proposed to improve KGs by filling in its missing connections. Unlike existing methods which hold a closed-world assumption, i.e., where KGs are fixed and new entities cannot be easily added, in the present work we relax this assumption and propose a new open-world KGC task. As a first attempt to solve this task we introduce an open-world KGC model called ConMask. This model learns embeddings of the entity's name and parts of its text-description to connect unseen entities to the KG. To mitigate the presence of noisy text descriptions, ConMask uses a relationship-dependent content masking to extract relevant snippets and then trains a fully convolutional neural network to fuse the extracted snippets with entities in the KG. Experiments on large data sets, both old and new, show that ConMask performs well in the open-world KGC task and even outperforms existing KGC models on the standard closed-world KGC task.

Validating EHR clinical models using ontology patterns

Clinical models are artefacts that specify how information is structured in electronic health records (EHRs). However, the makeup of clinical models is not guided by any formal constraint beyond a semantically vague information model. We address this gap by advocating ontology design patterns as a mechanism that makes the semantics of clinical models explicit.This paper demonstrates how ontology design patterns can validate existing clinical models using SHACL. Based on the Clinical Information Modelling Initiative (CIMI), we show how ontology patterns detect both modeling and terminology binding errors in CIMI models.SHACL, a W3C constraint language for the validation of RDF graphs, builds on the concept of “Shape”, a description of data in terms of expected cardinalities, datatypes and other restrictions. SHACL, as opposed to OWL, subscribes to the Closed World Assumption (CWA) and is therefore more suitable for the validation of clinical models.We have demonstrated the feasibility of the approach by manually describing the correspondences between six CIMI clinical models represented in RDF and two SHACL ontology design patterns. Using a Java-based SHACL implementation, we found at least eleven modeling and binding errors within these CIMI models. This demonstrates the usefulness of ontology design patterns not only as a modeling tool but also as a tool for validation.

Achieving autonomic Web service compositions with models at runtime

Several exceptional situations may arise in the complex, heterogeneous, and changing contexts where Web service operations run. For instance, a Web service operation may have greatly increased its execution time or may have become unavailable. The contribution of this article is to provide a tool-supported framework to guide autonomic adjustments of context-aware service compositions using models at runtime. During execution, when problematic events arise in the context, models are used by an autonomic architecture to guide changes of the service composition. Under the closed-world assumption, the possible context events are fully known at design time. Nevertheless, it is difficult to foresee all the possible situations arising in uncertain contexts where service compositions run. Therefore, the proposed framework also covers the dynamic evolution of service compositions to deal with unexpected events in the open world. An evaluation demonstrates that our framework is efficient during dynamic adjustments.

A Survey of Stealth Malware Attacks, Mitigation Measures, and Steps Toward Autonomous Open World Solutions

As our professional, social, and financial existences become increasingly digitized and as our government, healthcare, and military infrastructures rely more on computer technologies, they present larger and more lucrative targets for malware. Stealth malware in particular poses an increased threat because it is specifically designed to evade detection mechanisms, spreading dormant, in the wild for extended periods of time, gathering sensitive information or positioning itself for a high-impact zero-day attack. Policing the growing attack surface requires the development of efficient anti-malware solutions with improved generalization to detect novel types of malware and resolve these occurrences with as little burden on human experts as possible. In this paper, we survey malicious stealth technologies as well as existing solutions for detecting and categorizing these countermeasures autonomously. While machine learning offers promising potential for increasingly autonomous solutions with improved generalization to new malware types, both at the network level and at the host level, our findings suggest that several flawed assumptions inherent to most recognition algorithms prevent a direct mapping between the stealth malware recognition problem and a machine learning solution. The most notable of these flawed assumptions is the closed world assumption: that no sample belonging to a class outside of a static training set will appear at query time. We present a formalized adaptive open world framework for stealth malware recognition and relate it mathematically to research from other machine learning domains.

Semantic-Based Logic Representation and Reasoning for Automated Regulatory Compliance Checking

AbstractExisting automated compliance checking (ACC) efforts are limited in their automation and reasoning capabilities; the state of the art in ACC still uses ad hoc reasoning schema/methods, with lack of support for complete automation in ACC reasoning. First-order logic (FOL) representation and reasoning can provide a generalized reasoning method to facilitate complete automation in ACC reasoning. This paper presents a new FOL-based information representation and compliance reasoning (IRep and CR) schema for representing and reasoning about regulatory information and design information for checking regulatory compliance of building designs. The schema formalizes the representation of regulatory information and design information in the form of semantic-based (ontology-based) logic clauses that could be directly used for automated compliance reasoning. Two alternative subschemas, following a closed-world assumption and an open-world assumption for noncompliance detection, respectively, were proposed and...

Sensor fusion in the epistemic situation calculus

Robot sensors are usually subject to error. Since in many practical scenarios a probabilistic error model is not available, sensor readings are often dealt with in a hard-coded, heuristic fashion. In this paper, we propose a logic to address the problem from a KR perspective. In this logic, the epistemic effect of sensing actions is deferred to so-called fusion actions, which may resolve discrepancies and inconsistencies of recent sensing results. Moreover, a local closed-world assumption can be applied dynamically. When needed, this assumption can be revoked and fusions can be undone using a form of forgetting.

Modeling and predicting the user next input by Bayesian reasoning

We focus on enhancing the user experience by predicting entries when a form is filled, according to past interactions. The purpose of having a predictive model of form filling is to reduce the amount of time required to fill a form, and thus to reduce the fatigue and repetitiveness associated to this common task. Generally predictive models ignore the values entered by users in the other fields in the form, and just focus on the value getting entered at the current field. This is a limit to the model capabilities. Instead, we are aimed at predicting the sequence of entries in a form, instead of the value of single fields in isolation. This is done by means of inference over a Bayesian network, able to compute the a posteriori probability that remaining fields will assume certain values, given the set of values entered so far. The model structure and parameters can be learned from a dataset of past entries. The paper investigates computational and convergence issues under both the closed world assumption and the open world assumptions. As case study, we considered forms used for online payment of money order used at Poste Italiane, and we exploited this approach to prototype two different solutions for desktop and mobile applications. Results of experimentation with a user test group prove the proposed approach is able to provide an effective and appreciated support in filling a form.

Crowdsourcing Enumeration Queries: Estimators and Interfaces

Hybrid human/computer database systems promise to greatly expand the usefulness of query processing by incorporating the crowd for data gathering and other tasks. Such systems raise many implementation questions. Perhaps the most fundamental issue is that the closed world assumption underlying relational query semantics does not hold in such systems. As a consequence, the meaning of even simple queries can be called into question. Furthermore, query progress monitoring becomes difficult due to non-uniformities in the arrival of crowd-sourced data and peculiarities of how people work in crowd-sourcing systems. To address these issues, we develop statistical tools that enable users and systems developers to reason about query completeness. These tools can also help drive query execution and crowd-sourcing strategies. We evaluate our techniques using experiments on a popular crowd-sourcing platform.

Personalization and rule strategies in data-intensive intelligent context-aware systems

AbstractThe concept of personalization in its many forms has gained traction driven by the demands of computer-mediated interactions generally implemented in large-scale distributed systems and ad hoc wireless networks. Personalization requires the identification and selection of entities based on a defined profile (a context); an entity has been defined as a person, place, or physical or computational object. Context employs contextual information that combines to describe an entities current state. Historically, the range of contextual information utilized (in context-aware systems) has been limited to identity, location, and proximate data; there has, however, been advances in the range of data and information addressed. As such, context can be highly dynamic with inherent complexity. In addition, context-aware systems must accommodate constraint satisfaction and preference compliance.This article addresses personalization and context with consideration of the domains and systems to which context has been applied and the nature of the contextual data. The developments in computing and service provision are addressed with consideration of the relationship between the evolving computing landscape and context. There is a discussion around rule strategies and conditional relationships in decision support. Logic systems are addressed with an overview of the open world assumption versus the closed world assumption and the relationship with the Semantic Web. The event-driven rule-based approach, which forms the basis upon which intelligent context processing can be realized, is presented with an evaluation and proof-of-concept. The issues and challenges identified in the research are considered with potential solutions and research directions; alternative approaches to context processing are discussed. The article closes with conclusions and open research questions.

Gradient-based boosting for statistical relational learning: the Markov logic network and missing data cases

Recent years have seen a surge of interest in Statistical Relational Learning (SRL) models that combine logic with probabilities. One prominent and highly expressive SRL model is Markov Logic Networks (MLNs), but the expressivity comes at the cost of learning complexity. Most of the current methods for learning MLN structure follow a two-step approach where first they search through the space of possible clauses (i.e. structures) and then learn weights via gradient descent for these clauses. We present a functional-gradient boosting algorithm to learn both the weights (in closed form) and the structure of the MLN simultaneously. Moreover most of the learning approaches for SRL apply the closed-world assumption, i.e., whatever is not observed is assumed to be false in the world. We attempt to open this assumption. We extend our algorithm for MLN structure learning to handle missing data by using an EM-based approach and show this algorithm can also be used to learn Relational Dependency Networks and relational policies. Our results in many domains demonstrate that our approach can effectively learn MLNs even in the presence of missing data.