Automatic domain ontology extraction for context-sensitive opinion mining

Automated analysis of the sentiments presented in online consumer feedbacks can facilitate both organizations’ business strategy development and individual consumers’ comparison shopping. Nevertheless, existing opinion mining methods either adopt a context-free sentiment classification approach or rely on a large number of manually annotated training examples to perform context sensitive sentiment classification. Guided by the design science research methodology, we illustrate the design, development, and evaluation of a novel fuzzy domain ontology based contextsensitive opinion mining system. Our novel ontology extraction mechanism underpinned by a variant of Kullback-Leibler divergence can automatically acquire contextual sentiment knowledge across various product domains to improve the sentiment analysis processes. Evaluated based on a benchmark dataset and real consumer reviews collected from Amazon.com, our system shows remarkable performance improvement over the context-free baseline

The problem of learning non-taxonomic relationships of ontologies from text

Manual construction of ontologies by domain experts and knowledge engineers is a costly task. Thus, automatic and/or semi-automatic approaches to their development are needed. Ontology Learning aims at identifying its constituent elements, such as non-taxonomic relationships, from textual information sources. This article presents a discussion of the problem of Learning Non-Taxonomic Relationships of Ontologies and defines its generic process. Four techniques representing the state of the art of Learning Non-Taxonomic Relationships of Ontologies are described and the solutions they provide are discussed along with their advantages and limitations

Reviewing the problem of learning non-taxonomic relationships of ontologies from text

Learning Non-Taxonomic Relationships is a sub-field of Ontology Learning that aims at automating the extraction of these relationships from text. This article discusses the problem of Learning Non-Taxonomic Relationships of ontologies and proposes a generic process for approaching it. Some techniques representing the state of the art of this field are discussed along with their advantages and limitations. Finally, a framework for Learning Non- Taxonomic Relationships being developed by the authors is briefly discussed. This framework intends to be a customizable solution to reach good effectiveness in the process of extraction of non-taxonomic relationships according to the characteristics of the corpus.This work is supported by CNPq, CAPES and FAPEMA, research funding agencies of the Brazilian government

PARNT: A statistic based approach to extract non-taxonomic relationships of ontologies from text

Learning Non-Taxonomic Relationships is a subfield of Ontology learning that aims at automating the extraction of these relationships from text. This article proposes PARNT, a novel approach that supports ontology engineers in extracting these elements from corpora of plain English. PARNT is parametrized, extensible and uses original solutions that help to achieve better results when compared to other techniques for extracting non-taxonomic relationships from ontology concepts and English text. To evaluate the PARNT effectiveness, a comparative experiment with another state of the art technique was conducted.This work is supported by CNPq and CAPES, research funding agencies of the Brazilian government

Constructive Ontology Engineering

The proliferation of the Semantic Web depends on ontologies for knowledge sharing, semantic annotation, data fusion, and descriptions of data for machine interpretation. However, ontologies are difficult to create and maintain. In addition, their structure and content may vary depending on the application and domain. Several methods described in literature have been used in creating ontologies from various data sources such as structured data in databases or unstructured text found in text documents or HTML documents. Various data mining techniques, natural language processing methods, syntactical analysis, machine learning methods, and other techniques have been used in building ontologies with automated and semi-automated processes. Due to the vast amount of unstructured text and its continued proliferation, the problem of constructing ontologies from text has attracted considerable attention for research. However, the constructed ontologies may be noisy, with missing and incorrect knowledge. Thus ontology construction continues to be a challenging research problem. The goal of this research is to investigate a new method for guiding a process of extracting and assembling candidate terms into domain specific concepts and relationships. The process is part of an overall semi automated system for creating ontologies from unstructured text sources and is driven by the user’s goals in an incremental process. The system applies natural language processing techniques and uses a series of syntactical analysis tools for extracting grammatical relations from a list of text terms representing the parts of speech of a sentence. The extraction process focuses on evaluating the subject predicate-object sequences of the text for potential concept-relation-concept triples to be built into an ontology. Users can guide the system by selecting seedling concept-relation-concept triples to assist building concepts from the extracted domain specific terms. As a result, the ontology building process develops into an incremental one that allows the user to interact with the system, to guide the development of an ontology, and to tailor the ontology for the user’s application needs. The main contribution of this work is the implementation and evaluation of a new semi- automated methodology for constructing domain specific ontologies from unstructured text corpus

Ontology mapping: the state of the art

Ontology mapping is seen as a solution provider in today's landscape of ontology research. As the number of ontologies that are made publicly available and accessible on the Web increases steadily, so does the need for applications to use them. A single ontology is no longer enough to support the tasks envisaged by a distributed environment like the Semantic Web. Multiple ontologies need to be accessed from several applications. Mapping could provide a common layer from which several ontologies could be accessed and hence could exchange information in semantically sound manners. Developing such mapping has beeb the focus of a variety of works originating from diverse communities over a number of years. In this article we comprehensively review and present these works. We also provide insights on the pragmatics of ontology mapping and elaborate on a theoretical approach for defining ontology mapping

Analysing the problem and main approaches for ontology population

Knowledge systems are a suitable computational approach to solve complex problems and to provide decision support. Ontologies are an approach for knowledge representation and Ontology Population looks for instantiating the constituent elements of an ontology, like properties and non-taxonomic relationships. Manual population by domain experts and knowledge engineers is an expensive and time consuming task. Thus, automatic or semi-automatic approaches are needed. This paper discusses the problem of Automatic Ontology Population and proposes a generic process specifying its phases and what kind of techniques can be used to perform the activities of each phase. Some techniques representing the state of the art of this field are also described along with the solutions they adopt for each phase of the AOP process with their advantages and limitations. This work is part of HERMES, a Brazil/Portugal research cooperation project looking for techniques and tools for automating the process of ontology learning and population.This work is supported by CNPq, CAPES and FAPEMA, research funding agencies of the Brazilian government

Marinas and other ports and facilities for the recreational craft sector: an ontology domain to support spatial planning.

Marinas and other ports and facilities for the recreational craft sector in Sardinia (Italy) can host more than 19,000 pleasure boats and yachts, according to a recent estimate (Osservatorio Nautico Nazionale, 2010); this capacity, at the national level, is second only to that of the Liguria region. However, Sardinian infrastructures and facilities are not part of a coherent network. Moreover, they are unevenly scattered along the coastline and are very diverse, in terms of type, dimension, and endowment of facilities for sailors. A key issue to be taken into account in the early stages of the preparation of a plan for the pleasure craft sector, which might create the conditions for the setting up of a coherent network, is the lack of a proper, detailed knowledge of the system of Sardinian marinas and other facilities. To this end, this paper begins with an analysis of current information (both spatial and non-spatial) and attempts to build a spatial database that integrates available data. The analysis identifies differences in structure and semantics, together with differences in purpose and date of production/update of the data, as the roots of inconsistencies among existing data produced by different sources. Such differences in structure and semantics risk, if not properly identified, considered and handled, to cause an incorrect integration of data. Following the methodology provided by the guidelines produced by the Ordnance Survey with regards to domain ontologies (Hart et al., 2007; Hart e Goodwin, 2007; Kovacs et al., 2006), the construction of an ontology of the domain of infrastructure and facilities for the recreational craft sector is therefore proposed as a possible solution to the problem. By applying this methodology, a ‘knowledge glossary,’ consisting of a shared vocabulary of core and secondary concepts and of relationships (some of which spatial) among concepts is developed, leading to the construction of a conceptual model of the domain, later formalized by means of the software Protégé.