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Supporting competitive business analysis of financial reports through the automated analysis and interpretation of their natural language sections, presents specific challenges including information that can be ambiguous, camouflaged, or tacitly hidden within the narrative. These sections present terminology and structural challenges for information extraction that require the application of linguistic and heuristic based domain modelling to identify the information requirement. This thesis investigates a modelling approach that incrementally builds the business analysts information requirement as a series of Semantic Paths grounded in domain linguistic and user heuristics. A Competitive Analysis Ontology (CAO) is defined to provide semantic representation of the information requirement necessary to drive linguistic analysis and information extraction. The evaluation of the CAO within the financial sub-domain of competitive analysis is investigated, through the development of the Analyst Work Bench (AWB), is presented. The AWB linguistically analyses a Form 10-Q’s disclosure sections, automatically populates the CAO and provides the analyst’s information requirement. The AWB leverages the CAO Semantic Paths for information search and extraction capability, to support an analyst perform a competitive analysis, with reduced manual effort. Evaluation based on design-science principles, use methods from information retrieval and information system success to determine CAO performance and usability. A controlled experiment that compares competitive analysis performance using the AWB, against its manual performed equivalent, reported a 37% performance increase using the AWB to identify relevant information. Usability evaluation further found that CAO use contributed to task structuring, and structured information provision in a manner that directly supported task performance.
To date, the relation between multilingualism and the Semantic Web has not yet received enough attention in the research community. One major challenge for the Semantic Web community is to develop architectures, frameworks and systems that can help in overcoming national and language barriers, facilitating equal access to information produced in different cultures and languages. As such, this volume aims at documenting the state-of-the-art with regard to the vision of a Multilingual Semantic Web, in which semantic information will be accessible in and across multiple languages. The Multilingual Semantic Web as envisioned in this volume will support the following functionalities: (1) responding to information needs in any language with regard to semantically structured data available on the Semantic Web and Linked Open Data (LOD) cloud, (2) verbalizing and accessing semantically structured data, ontologies or other conceptualizations in multiple languages, (3) harmonizing, integrating, aggregating, comparing and repurposing semantically structured data across languages and (4) aligning and reconciling ontologies or other conceptualizations across languages. The volume is divided into three main sections: Principles, Methods and Applications. The section on “Principles” discusses models, architectures and methodologies that enrich the current Semantic Web architecture with features necessary to handle multiple languages. The section on “Methods” describes algorithms and approaches for solving key issues related to the construction of the Multilingual Semantic Web. The section on “Applications” describes the use of Multilingual Semantic Web based approaches in the context of several application domains. This volume is essential reading for all academic and industrial researchers who want to embark on this new research field at the intersection of various research topics, including the Semantic Web, Linked Data, natural language processing, computational linguistics, terminology and information retrieval. It will also be of great interest to practitioners who are interested in re-examining their existing infrastructure and methodologies for handling multiple languages in Web applications or information retrieval systems.
This book constitutes the refereed proceedings of the 8th International Conference on Knowledge Engineering and the Semantic Web, KESW 2017, held Szczecin, Poland, in November 2017. The 16 full papers presented were carefully reviewed and selected from 58 submissions. The papers are organized in topical sections on natural language processing; knowledge representation and reasoning; ontologies and controlled vocabularies; scalable data access and storage solutions; semantic Web and education; linked data; semantic technologies in manufacturing and business.
We wrote this book to help financial experts and investors to understand the state of the art of artificial intelligence and machine learning in finance. But first, what is artificial intelligence? The foundations of artificial intelligence lie in the human desire to automate. Often this desire has had foundations in grand civilization-defining visions or economic needs, such as the Antikythera mechanism, circa 200 BCE. Considered to be the oldest known example of an analog computer, it is thought that the mechanism automated the prediction of the positions of the sun, the moon, and the planets to assist in navigation.No matter the specific industry or application, AI has become a new engine of growth. Both finance and banking have been leveraging AI technologies and algorithms, applying them to automate routine tasks, procedures and forecasting, thereby improving overall customer experience.The topics covered in this book make it an invaluable resource for academics, researchers, policymakers, and practitioners alike who want to understand how AI has affected the banking and financial industries and how it will continue to change them in the years to come.
RuleML 2003 was the second international workshop on rules and rule markup languages for the Semantic Web, held in conjunction with the International Semantic Web Conference (ISWC). The aim of the RuleML workshop series is to stimulate research on all issues related to web rule languages and to provide an annual forum for presenting and discussing new research results. The Semantic Web is a major world-wide endeavor to advance the Web by enriching its multimedia document content with propositional information that can be processed by inference-enabled Web applications. Rules and rule markup languages, such as RuleML, will play an important role in the success of the Semantic Web. Rules will act as a means to draw inferences, to express constraints,tospecifypoliciesforreactingtoevents,totransformdata,etc.Rule markup languages will allow us to enrich Web ontologies by adding de?nitions of derived concepts, to publish rules on the Web, to exchange rules between di?erent systems and tools, etc. RuleML 2003 built on the success of RuleML 2002, which was held in c- junction with ISWC 2002, Sardinia, Italy. The proceedings of RuleML 2002 can be found at http://www.ceur-ws.org/Vol-60/. Special highlights of the RuleML 2003 workshop were the two invited pres- tationsgivenbyPeterChenon“Rules,XML,andtheERModel”andbyHarold Boley on “Object-Oriented RuleML: User-Level Roles, URI-Grounded Clauses, and Order-Sorted Terms”. This proceedings volume also contains an invited - per by Francois ̧ Bry and Sebastian Scha?ert on “An Entailment Relation for Reasoning on the Web”.
Semantic technologies are experimenting an increasing popularity in the context of different domains and applications. The understanding of any class of system can be significantly changed under the assumption any system is part of a global ecosystem known as Semantic Web.The Semantic Web would be an evolving extension of current Web model (normally referred as Syntactic Web) that introduces a semantic layer in which semantics, or meaning of information, are formally defined.So, semantics should integrate web-centric standard information infrastructures improving several aspects of interaction among heterogeneous systems. This is because common interoperability models are progressively becoming obsolete if compared with the intrinsic complexity and always more distributed focus that feature modern systems. For example, the basic interoperability model, that assumes the interchange of messages among systems without any interpretation, is simple but effective only in the context of close environments. Also more advanced models, such as the functional interoperability model that integrates basic interoperability model with the ability of intepretating data context under the assumption of a shared schema for data fields accessing, appears not able to provide a full sustainable technologic support for open systems.The Semantic Interoperability model would improve common interoperability models introducing the interpretation of means of data. Semantic interoperability is a concretely applicable interaction model under the assumption of adopting rich data models (commonly called Ontology) composed of concepts within a domain and the relationships among those concepts.In practice, semantic technologies are partially inverting the common view at actor intelligence: intelligence is not implemented (only) by actors but it is implicitly resident in the knowledge model. In other words, schemas contain information and the "code" to interpretate it.
From a widely published, international expert in both the theory and practical applications of the entity-relationship approach, this reference takes the reader from data entity analysis at the enterprise level through data element analysis and physical design considerations.
Advancements in technology regularly influence the healthcare field and developing aspects on medical patient safety. Implementing electronic health records, decision support systems, and computerized physician order entry systems reduces risk in the potential for e-health to make errors leading to adverse events. E-Health Technologies and Improving Patient Safety: Exploring Organizational Factors presents an overview on information and communication technologies and addresses the impacts on the field of both patient safety and e-health. This book offers insightful perspectives and concentrated research on concepts related to these areas, as well as issues and current trends in patient safety in e-health.
The Global Financial Crisis and the Eurozone crisis that has followed have drawn attention to weaknesses in financial records, information and data. These weaknesses have led to operational risks in financial institutions, flawed bankruptcy and foreclosure proceedings following the Crisis, and inadequacies in financial supervisors’ access to records and information for the purposes of a prudential response. Research is needed to identify the practices that will provide the records, information and data needed to support more effective financial analysis and risk management. The unique contribution of this volume is in bringing together researchers in distinct domains that seldom interact to identify theoretical, technological, policy and practical issues related to the management of financial records, information and data. The book will, therefore, appeal to researchers or advanced practitioners in the field of finance and those with an interest in risk management, computer science, cognitive science, sociology, management information systems, information science, and archival science as applied to the financial domain.