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Geotagging Twitter Messages in Crisis Management

COMPUTER JOURNAL 2015年第9期58卷 1937-1954页

作者： Ghahremanlou, Lida Sherchan, Wanita Thom, James A. RMIT Univ IBM Res Australia Melbourne Vic Australia

During times of crisis microblogging platforms such as Twitter have played an important role as a communication channel to distribute information. Particularly, disaster-related tweets are valuable resources when tagged with their location for detecting unexpected events. However, they often contain different types of location and one of the main challenges is resolving the ambiguity involved in their locations. The process of identifying phrase portions in unstructured texts with possible spatial aspects and disambiguating these references by linking them to geographic coordinates is known as Geotagging. In the context of crisis management, this paper presents OzCT geotagger that automatically detects the location(s) mentioned in the content of tweets with three possibilities: definite, ambiguous and no-location. It also semantically annotates the tweet components utilizing existing and new ontologies. The OzCT geotagger has been recently deployed in a trial system of the OzCrisisTracker application. Experiments demonstrate that the precision and recall for detection of the definite locations against geotagging by human judgement are on average of 80%. We also conclude that the accuracy of geographical focus of the OzCT geotagger is considerably higher than other systems. While existing geocoding systems have lower coverage for suburb and street focus, our approach detects suburbs in more than 60% situations.

关键词： knowledge representation formalisms and methods ontology mircoblogging Twitter crisis informatics social media

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Automatic fuzzy ontology generation for Semantic Web

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2006年第6期18卷 842-856页

作者： Tho, QT Hui, SC Fong, ACM Cao, TH Nanyang Technol Univ Sch Comp Engn Singapore 639798 Singapore Hochiminh City Univ Technol Fac Informat Technol Ho Chi Minh City Vietnam

Ontology is an effective conceptualism commonly used for the Semantic Web. Fuzzy logic can be incorporated to ontology to represent uncertainty information. Typically, fuzzy ontology is generated from a predefined concept hierarchy. However, to construct a concept hierarchy for a certain domain can be a difficult and tedious task. To tackle this problem, this paper proposes the FOGA ( Fuzzy Ontology Generation frAmework) for automatic generation of fuzzy ontology on uncertainty information. The FOGA framework comprises the following components: Fuzzy Formal Concept Analysis, Concept Hierarchy Generation, and Fuzzy Ontology Generation. We also discuss approximating reasoning for incremental enrichment of the ontology with new upcoming data. Finally, a fuzzy-based technique for integrating other attributes of database to the ontology is proposed.

关键词： Intelligent Web services and semantic Web ontology design uncertainty "fuzzy," probabilistic knowledge representation formalisms and methods concept learning

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A basic mathematical framework for conceptual graphs

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2006年第2期18卷 261-271页

作者： Nguyen, PHP Corbett, D Govt S Australia Attorney Gen Dept Justice Technol Serv Adelaide SA 5000 Australia Sci Applicat Int Corp Mclean VA 22102 USA

Based on the original idea of Sowa on conceptual graph and a recent formalism by Corbett on ontology, this paper presents a rigorous mathematization of basic concepts encountered in the Conceptual Structure Theory, including canon, ontology, conceptual graph, projection, and canonical formation operations, with the aim of deriving their mathematical properties and applying them to future research and development on knowledge representation. Our proposed formalism enhances the Conceptual Structure Theory and enables it to compare favorably with other alternative methods such as the Formal Concept Analysis theory.

关键词： graph algorithms knowledge representation formalisms and methods ontology design

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Human Activity Recognition Models in Ontology Networks

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IEEE TRANSACTIONS ON CYBERNETICS 2022年第6期52卷 5587-5606页

作者： Buoncompagni, Luca Kareem, Syed Yusha Mastrogiovanni, Fulvio Univ Genoa Dept Informat Bioengn Robot & Syst Engn I-16145 Genoa Italy Teseo Srl I-16151 Genoa Italy

We present Arianna(+), a framework to design networks of ontologies for representing knowledge enabling smart homes to perform human activity recognition online. In the network, nodes are ontologies allowing for various data contextualisation, while edges are general-purpose computational procedures elaborating data. Arianna(+) provides a flexible interface between the inputs and outputs of procedures and statements, which are atomic representations of ontological knowledge. Arianna(+) schedules procedures on the basis of events by employing logic-based reasoning, that is, by checking the classification of certain statements in the ontologies. Each procedure involves input and output statements that are differently contextualized in the ontologies based on specific prior knowledge. Arianna(+) allows to design networks that encode data within multiple contexts and, as a reference scenario, we present a modular network based on a spatial context shared among all activities and a temporal context specialized for each activity to be recognized. In the article, we argue that a network of small ontologies is more intelligible and has a reduced computational load than a single ontology encoding the same knowledge. Arianna(+) integrates in the same architecture heterogeneous data processing techniques, which may be better suited to different contexts. Thus, we do not propose a new algorithmic approach to activity recognition, instead, we focus on the architectural aspects for accommodating logic-based and data-driven activity models in a context-oriented way. Also, we discuss how to leverage data contextualization and reasoning for activity recognition, and to support an iterative development process driven by domain experts.

关键词： knowledge representation formalisms and methods ontology design pattern recognition semantic networks

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Discrete Mereotopology for Spatial Reasoning in Automated Histological Image Analysis

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IEEE TRANSACTIONS ON PATTERN ANALYSIS AND MACHINE INTELLIGENCE 2013年第3期35卷 568-581页

作者： Randell, David A. Landini, Gabriel Galton, Antony Univ Birmingham Coll Med & Dent Sci Sch Dent Birmingham B4 6NN W Midlands England Univ Exeter Coll Engn Math & Phys Sci Exeter EC4 4QF Devon England

Discrete mereotopology (DM) is a first-order spatial logic that fuses together mereology (the theory of parthood relations) and topology to model discrete space. We show how a set of quasitopological functions defined within DM can be mapped to specific operators defined in mathematical morphology (MM) and easily implemented in scientific image processing programs. These functions provide the means to model topological properties of individual regions and spatial relations between them such as contact, overlap, and the relation of part to whole. DM not only extends the expressive power of image processing applications where mathematical morphology is used, but by functioning as a logic it also supplies the formal basis with which to prove the correctness of implemented algorithms as well as providing the computational basis to mechanically reason about segmented digital images using automated reasoning programs. In particular, we show how DM can supply a model-based and algorithmic context to the otherwise blind pixel-based image processing routines still dominating conventional imaging approaches. A number of worked examples drawn from the, histological domain are given, including segmentation of cells in culture, identifying basal cell layers from stratified epithelia sections, and cell sorting in blood smears.

关键词： Life and medical sciences knowledge representation formalisms and methods morphological modeling methodologies representation languages

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representation and Comprehension in Machine Translation and Intelligent Decision Support

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IEEE INTELLIGENT SYSTEMS 2011年第4期26卷 40-47页

作者： Bolia, Robert S. Slyh, Raymond E. US Off Naval Res Global Tokyo Japan USAF Res Lab Wright Patterson AFB OH 45433 USA

A contributing factor to failures of machine translation and large-scale intelligent decision-support systems is the unavailability of "common sense" about the world and about language and its contextual use... 详细信息

关键词： Intelligent Systems Machine Translation Natural Language Processing knowledge representation formalisms and methods Applications And Expert knowledge Intensive Systems Intelligent Web Services And Semantic Web knowledge Acquisition Philosophical Foundations

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Valid-Time Indeterminacy in Temporal Relational Databases: Semantics and representations

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2013年第12期25卷 2880-2894页

作者： Anselma, Luca Terenziani, Paolo Snodgrass, Richard T. Univ Turin Dipartimento Informat I-10149 Turin Italy Univ Piemonte Orientale Dipartimento Informat I-15121 Alessandria Italy Univ Arizona Dept Comp Sci Coll Sci Tucson AZ 85721 USA

Valid-time indeterminacy is "don't know when" indeterminacy, coping with cases in which one does not exactly know when a fact holds in the modeled reality. In this paper, we first propose a reference representation (data model and algebra) in which all possible temporal scenarios induced by valid-time indeterminacy can be extensionally modeled. We then specify a family of 16 more compact representational data models. We demonstrate their correctness with respect to the reference representation and analyze several properties, including their data expressiveness. Then, we compare these compact models along several relevant dimensions. Finally, we also extend the reference representation and a representative of compact representations to cope with probabilities.

关键词： Temporal databases artificial intelligence database design modeling and management knowledge representation formalisms and methods

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Supporting Flexible, Efficient, and User-Interpretable Retrieval of Similar Time Series

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IEEE TRANSACTIONS ON knowledge AND DATA ENGINEERING 2013年第3期25卷 677-689页

作者： Montani, Stefania Leonardi, Giorgio Bottrighi, Alessio Portinale, Luigi Terenziani, Paolo Univ Piemonte Orientale Inst Comp Sci DISIT I-15121 Alessandria Italy

Supporting decision making in domains in which the observed phenomenon dynamics have to be dealt with, can greatly benefit of retrieval of past cases, provided that proper representation and retrieval techniques are implemented. In particular, when the parameters of interest take the form of time series, dimensionality reduction and flexible retrieval have to be addresses to this end. Classical methodological solutions proposed to cope with these issues, typically based on mathematical transforms, are characterized by strong limitations, such as a difficult interpretation of retrieval results for end users, reduced flexibility and interactivity, or inefficiency. In this paper, we describe a novel framework, in which time-series features are summarized by means of Temporal Abstractions, and then retrieved resorting to abstraction similarity. Our approach grants for interpretability of the output results, and understandability of the (user-guided) retrieval process. In particular, multilevel abstraction mechanisms and proper indexing techniques are provided, for flexible query issuing, and efficient and interactive query answering. Experimental results have shown the efficiency of our approach in a scalability test, and its superiority with respect to the use of a classical mathematical technique in flexibility, user friendliness, and also quality of results.

关键词： Decision support knowledge representation formalisms and methods knowledge retrieval information search and retrieval

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A Basic Framework for Explanations in Argumentation

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IEEE INTELLIGENT SYSTEMS 2021年第2期36卷 72-80页

作者： Lindgren, Helena Kampik, Timotheus Rosero, Esteban Guerrero Blusi, Madeleine Nieves, Juan Carlos Umea Univ S-90187 Umea Sweden Umea Univ Dept Community Med & Rehabil S-90187 Umea Sweden Umea Univ Dept Nursing S-90187 Umea Sweden

We discuss explanations for formal (abstract and structured) argumentation-the question of whether and why a certain argument or claim can be accepted (or not) under various extension-based semantics. We introduce a flexible framework, which can act as the basis for many different types of explanations. For example, we can have simple or comprehensive explanations in terms of arguments for or against a claim, arguments that (indirectly) defend a claim, the evidence (knowledge base) that supports or is incompatible with a claim, and so on. We show how different types of explanations can be captured in our basic framework, discuss a real-life application and formally compare our framework to existing work.

关键词： Semantics knowledge based systems Intelligent systems Machine learning algorithms Law enforcement knowledge representation Augmented reality Artificial intelligence knowledge representation formalisms and methods nonmonotonic reasoning and belief revision

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Intelligent tutor for all: The constraint-based approach

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IEEE INTELLIGENT SYSTEMS 2007年第4期22卷 38-45页

作者： Mitrovic, Antonija Martin, Brent Suraweera, Pramuditha Univ Canterbury Dept Comp Sci & Software Engn Christchurch New Zealand

This paper presents a new type of intelligent tutoring systems, called constraint-based tutors. The system have been thoroughly evaluated and proven to achieve significant learning gains.

关键词： Computer aided instruction Constraint theory Educational institutions Electronic learning Feedback Humans Intelligent systems Internet Internet Internet knowledge representation Production Web-based learning artificial intelligence computer-assisted instruction constraint-based tutors domain knowledge education instructional domains intelligent tutoring systems intelligent tutoring systems intelligent tutoring systems knowledge modeling knowledge representation knowledge representation formalisms and methods

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