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This paper investigates the semantic modeling of smart cities and proposes two ontology matching frameworks, called Clustering for Ontology Matching‐based Instances (COMI) and Pattern mining for Ontology Matching‐based Instances (POMI). The goal is to discover the relevant knowledge by investigating the correlations among smart city data based on clustering and pattern mining approaches. The COMI method first groups the highly correlated ontologies of smart‐city data into similar clusters using the generic k‐means algorithm. The key idea of this method is that it clusters the instances of each ontology and then matches two ontologies by matching their clusters and the corresponding instances within the clusters. The POMI method studies the correlations among the data properties and selects the most relevant properties for the ontology matching process. To demonstrate the usefulness and accuracy of the COMI and POMI frameworks, several experiments on the DBpedia, Ontology Alignment Evaluation Initiative, and NOAA ontology databases were conducted. The results show that COMI and POMI outperform the state‐of‐the‐art ontology matching models regarding computational cost without losing the quality during the matching process. Furthermore, these results confirm the ability of COMI and POMI to deal with heterogeneous large‐scale data in smart‐city environments. [ABSTRACT FROM AUTHOR]

This paper presents an approach to the automated markup of texts with emotional labels. The approach considers two possible representations of emotions in parallel: emotional categories (emotional tags used to refer to emotions) and emotional dimensions (measures that try to model the essential aspects of emotions numerically). For each representation, a corpus of example texts previously annotated by human evaluators is mined for an initial assignment of emotional features to words. This results in a list of emotional words (LEW) which becomes a useful resource for later automated markup. The algorithm proposed for the automated markup of text closely mirrors the steps taken during feature extraction, employing a combination of the LEW resource and the ANEW word list for the actual assignment of emotional features, and WordNet for knowledge-based expansion of words not occurring in either and an ontology of emotional categories. The algorithm for automated markup is tested and the results are discussed with respect to three main issues: the relative adequacy of each of the representations used, correctness and coverage of the proposed algorithm, and additional techniques and solutions that may be employed to improve the results. The average percentage o f success obtained by our approach when it marks up with emotional dimensions is around 80% and when it marks up with emotional categories is around 50%. The main contribution of the approach presented in this paper is that it allows dimensions and categories at different levels of abstraction to operate simultaneously during markup. [ABSTRACT FROM AUTHOR]

Patterns are used as a fundamental means for analyzing data in many data mining applications. Many efficient techniques have been developed to discover patterns. However, the excessive number of discovered patterns and the lack of semantic information have made it difficult for a user to interpret and explore the patterns. A rough idea of the meanings of patterns can benefit the user in the process of exploring them. To address this issue, this paper presents a model for automatically annotating patterns with concepts. In addition, in a given context, the relative importance of each term that defines a concept is not the same. To define a context, there are a number of related information sources, such as documents, patterns, concepts, and an ontology. The question is which information sources are useful for estimating the relative importance of the terms? Should the most accurate one to be focused on or all of them be used to define the context? This research investigated these questions and defined an effective annotation context to estimate the relative importance of the terms, where the aim is to improve the performance of a machine that relies on the subject matter of a pattern set. The model is evaluated by comparing it with different baseline models on 2 standard datasets. The results show that the performance of the proposed model is significantly better. [ABSTRACT FROM AUTHOR]

Forgetting is an important tool for reducing ontologies by eliminating some redundant concepts and roles while preserving sound and complete reasoning. Attempts have previously been made to address the problem of forgetting in relatively simple description logics (DLs), such as DL-Lite and extended . However, the issue of forgetting for ontologies in more expressive DLs, such as and OWL DL, is largely unexplored. In particular, the problem of characterizing and computing forgetting for such logics is still open. In this paper, we first define semantic forgetting about concepts and roles in ontologies and state several important properties of forgetting in this setting. We then define the result of forgetting for concept descriptions in , state the properties of forgetting for concept descriptions, and present algorithms for computing the result of forgetting for concept descriptions. Unlike the case of DL-Lite, the result of forgetting for an ontology does not exist in general, even for the special case of forgetting in TBoxes. This makes the problem of computing the result of forgetting in more challenging. We address this problem by defining a series of approximations to the result of forgetting for ontologies and studying their properties. Our algorithms for computing approximations can be directly implemented as a plug-in of an ontology editor to enhance its ability of managing and reasoning in (large) ontologies. [ABSTRACT FROM AUTHOR]

The increasing number of service providers on the Web makes it challenging to select a provider for a specific service demand. Each service consumer has different expectations for a given service in different contexts, so the selection process should be consumer-oriented and context-dependent. Current approaches for service selection typically have consumers receive ratings of providers from other consumers, where the ratings reflect the consumers' overall subjective opinions. This may be misleading if consumers have different contexts and satisfaction criteria. In this paper, we propose that consumers objectively record their experiences, using an ontology to capture subtle details. This can then be interpreted by consumers according to their own criteria and contexts. We then integrate a method for addressing consumers who lie about their experiences, filtering them out during service selection. We demonstrate the value of our approach through experiments comparing our model with three recent rating-based service selection models. Our experiments show that using the proposed approach, service consumers can select the service providers for their needs more accurately even if the consumers have different criteria, they change the contexts of their service demands over time, or a significant portion of them are liars. [ABSTRACT FROM AUTHOR]

Over the years we have developed the Disciple theory, methodology, and family of tools for building knowledge-based agents. This approach consists of developing an agent shell that can be taught directly by a subject matter expert in a way that resembles how the expert would teach a human apprentice when solving problems in cooperation. This paper presents the most recent version of the Disciple approach and its implementation in the Disciple–RKF (rapid knowledge formation) system. Disciple–RKF is based on mixed-initiative problem solving, where the expert solves the more creative parts of the problem and the agent solves the more routine ones, integrated teaching and learning, where the agent helps the expert to teach it, by asking relevant questions, and the expert helps the agent to learn, by providing examples, hints, and explanations, and multistrategy learning, where the agent integrates multiple learning strategies, such as learning from examples, learning from explanations, and learning by analogy, to learn from the expert how to solve problems. Disciple–RKF has been applied to build learning and reasoning agents for military center of gravity analysis, which are used in several courses at the US Army War College. [ABSTRACT FROM AUTHOR]

In this paper we propose a distributed knowledge management framework for semantics and knowledge creation, population, and reuse on the grid. Its objective is to evolve the Grid toward the Semantic Grid with the ultimate purpose of facilitating problem solving in e-Science. The framework uses ontology as the conceptual backbone and adopts the service-oriented computing paradigm for information- and knowledge-level computation. We further present a semantics-based approach to problem solving, which exploits the rich semantic information of grid resource descriptions for resource discovery, instantiation, and composition. The framework and approach has been applied to a UK e-Science project—Grid Enabled Engineering Design Search and Optimisation in Engineering (GEODISE). An ontology-enabled problem solving environment (PSE) has been developed in GEODISE to leverage the semantic content of GEODISE resources and the Semantic Grid infrastructure for engineering design. Implementation and initial experimental results are reported. [ABSTRACT FROM AUTHOR]

The usage of association rules is playing a vital role in the field of knowledge data discovery. Numerous rules have to be processed and plot based on the ranges on the schema. The step in this process depends on the user's queries. Previously, several projects have been proposed to reduce work and improve filtration processes. However, they have some limitations in preprocessing time and filtration rate. In this article, an improved fuzzy weighted‐iterative concept is introduced to overcome the limitation based on the user request and visualization of discovering rules. The initial step includes the mix of client learning with posthandling to use the semantics. The above advance was trailed by surrounding rule schemas to fulfill and anticipate unpredictable guidelines dependent on client desires. Preparing the above developments can be imagined by the use of yet another clever method of study. Standards on guidelines are recognized by the average learning professionals. [ABSTRACT FROM AUTHOR]

Natural language question-and-answering is one of the most convenient means for communicating with the Semantic Web, which is typically in the form of online knowledge bases encoded in Web Ontology Language (OWL). To understand a natural language question, it is essential that it can be translated into a query that is understandable by the knowledge bases. This article is concerned with the task of semantic mapping from natural language questions to OWL queries, and proposes an automatic and domain-independent mapping framework, called Three-Phases Semantic Mapping (TPSM). The TPSM framework approaches the task of semantic mapping in three interrelated phases: (i) formalizing knowledge, (ii) building semantic mapping, and (iii) combining OWL queries. First, formalizing knowledge formalizes the units of mapping in the natural language and OWL knowledge. Second, semantic mapping builds the transverse mapping between questions and OWL knowledge, as formalized in the first phase, by means of working with the so-called Fuzzy Constraint Satisfaction Problems (FCSP). Third, combining OWL queries obtains valid Resource Description Framework (RDF) models by applying predefined templates and their corresponding combining methods. We have implemented a prototype semantic mapping system based on the framework, and have conducted a series of experimental validations involving OWL knowledge bases in different domains as queried by various types of questions. [ABSTRACT FROM AUTHOR]

This article proposes a first–order logic axiomatic description of some basic ontological notions underlying the meaning of different types of temporal constraints about repeated events. The article also shows that such an ontology provides an homogeneous framework that can be used to model and compare the intended meaning of different formalisms in the artificial intelligence and temporal databases literature which deal with user–defined calendar dates and/or qualitative and quantitative constraints between different types of repeated events. [ABSTRACT FROM AUTHOR]