Automatic key phrase extraction is fundamental to the success of many recent digital library applications and semantic information retrieval techniques and a difficult and essential problem in Vietnamese natural language processing (NLP). In this work, we propose a novel method for key phrase extracting of Vietnamese text that exploits the Vietnamese Wikipedia as an ontology and exploits specific characteristics of the Vietnamese language for the key phrase selection stage.
Extracting knowledge from unstructured text is a long-standing goal of NLP. Although learning approaches to many of its subtasks have been developed (e.g., parsing, taxonomy induction, information extraction), all end-to-end solutions to date require heavy supervision and/or manual engineering, limiting their scope and scalability. We present OntoUSP, a system that induces and populates a probabilistic ontology using only dependency-parsed text as input.
In the work of Baader and Distel, a method has been proposed to axiomatize all general concept inclusions (GCIs) expressible in the description logic ℰℒK and valid in a given interpretation ℐ. This provides us with an effective method to learn ℰℒK-ontologies from interpretations. In this work, we want to extend this approach in the direction of handling errors, which might be present in the data-set.
Chapter 2 - Representation and patterns: an introduction to the REA enterprise ontology. The objective of this chapter is to help you understand how to analyze and create representations of enterprises that serve as the core foundation for their information systems.
Chapter 3 - The REA enterprise ontology: Value system and value chain modeling. The objectives of this chapter are to describe the components of a typical enterprise's value system and value chain and to discuss the procedures for developing models of enterprise value systems and value chains.
Chapter 4 - The REA enterprise ontology: Business process level modeling. The objective of this chapter is to present a template pattern for modeling enterprise business processes (transaction cycles) that serves as the structure for an enterprisewide database.
Chapter 8 - The sales/collection business process. The objective of this chapter is to encourage an in-depth understanding of the sales/collection business process, with a focus on the modeling of this transaction cycle with the REA enterprise ontology and querying to meet information needs for this cycle.
Chapter 9 - The acquisition/payment business process. The objective of this chapter is to encourage an in-depth understanding of the acquisition/payment business process, with a focus on the modeling of this transaction cycle with the REA enterprise ontology and querying to meet information needs for this cycle.
Chapter 11 - The conversion business process. The objectives of this chapter are to introduce the conversion business process; to discuss the REA ontology representation of conversion processes, and to describe some of the typical information needs in the conversion business process.
Chapter 12 - The human resource business process. The objectives of this chapter are to introduce the human resource business process, to discuss the REA ontology representation of human resource processes, and to describe some of the typical information needs in the human resource business process.
Chapter 13 - The financing business process. The objectives of this chapter are to introduce the financing business process; to discuss the REA ontology representation of financing processes, and to describe some typical information needs in the financing business process.
Chapter 15 - ERP systems and e-commerce: Intra- and inter-enterprise modeling. One objective of this chapter is to compare the REA enterprise ontology with current developments in enterprise resource planning (ERP) systems and electronic commerce (e-commerce) in the context of the types of integration introduced in Chapter 1. The prospects for progress in using the REA enterprise ontology as a foundation for intra-enterprise systems (similar to ERP systems) and for inter-enterprise systems needed for seamless e-commerce are discussed.
In this paper, participants in the Translational Medicine task force of the World
Wide Web Consortium’s Health Care and Life Sciences Interest Group (W3C
HCLSIG) present the Translational Medicine Ontology (TMO) and the Translational
Medicine Knowledge Base (TMKB). The TMKB consists of the TMO, mappings to
other terminologies and ontologies, and data in RDF format spanning discovery
research and drug development, which are of therapeutic relevance to clinical research
and clinical practice.
Existing works on sentiment analysis on product reviews suffer from the following limitations: (1) The knowledge of hierarchical relationships of products attributes is not fully utilized. (2) Reviews or sentences mentioning several attributes associated with complicated sentiments are not dealt with very well. In this paper, we propose a novel HL-SOT approach to labeling a product’s attributes and their associated sentiments in product reviews by a Hierarchical Learning (HL) process with a deﬁned Sentiment Ontology Tree (SOT). ...
This paper describes a series of experiments to test the hypothesis that the parallel application of multiple NLP tools and the integration of their results improves the correctness and robustness of the resulting analysis. It is shown how annotations created by seven NLP tools are mapped onto toolindependent descriptions that are deﬁned with reference to an ontology of linguistic annotations, and how a majority vote and ontological consistency constraints can be used to integrate multiple alternative analyses of the same token in a consistent way. ...
Human categorization is neither a binary nor a context-free process. Rather, some concepts are better examples of a category than others, while the criteria for category membership may be satisﬁed to different degrees by different concepts in different contexts. In light of these empirical facts, WordNet’s static category structure appears both excessively rigid and unduly fragile for processing real texts. In this paper we describe a syntagmatic, corpus-based approach to redeﬁning WordNet’s categories in a functional, gradable and context-sensitive fashion.
This paper presents a novel algorithm for the acquisition of Information Extraction patterns. The approach makes the assumption that useful patterns will have similar meanings to those already identiﬁed as relevant. Patterns are compared using a variation of the standard vector space model in which information from an ontology is used to capture semantic similarity. Evaluation shows this algorithm performs well when compared with a previously reported document-centric approach.
In this paper, we present a method for the semantic tagging of word chunks extracted from a written transcription of conversations. This work is part of an ongoing project for an information extraction system in the ﬁeld of maritime Search And Rescue (SAR). Our purpose is to automatically annotate parts of texts with concepts from a SAR ontology. Our approach combines two knowledge sources a SAR ontology and the Wordsmyth dictionarythesaurus, and it uses a similarity measure for the classiﬁcation.
We present a weakly supervised approach to automatic Ontology Population from text and compare it with other two unsupervised approaches. In our experiments we populate a part of our ontology of Named Entities. We considered two high level categories - geographical locations and person names and ten sub-classes for each category. For each sub-class, from a list of training examples and a syntactically parsed corpus, we automatically learn a syntactic model - a set of weighted syntactic features, i.e.
Chapter 1 - An introduction to integrated enterprise information systems. The objectives of this chapter are to provide a definition for integrated enterprise information systems, discuss the need for integrated information systems in enterprises, and assess the extent to which current enterprise information systems are integrated.