Toward a fuzzy domain ontology extraction method for adaptive e-learning

With the widespread applications of electronic learning (e-Learning) technologies to education at all levels, increasing number of online educational resources and messages are generated from the corresponding e-Learning environments. Nevertheless, it is quite difficult, if not totally impossible, for instructors to read through and analyze the online messages to predict the progress of their students on the fly. The main contribution of this paper is the illustration of a novel concept map generation mechanism which is underpinned by a fuzzy domain ontology extraction algorithm. The proposed mechanism can automatically construct concept maps based on the messages posted to online discussion forums. By browsing the concept maps, instructors can quickly identify the progress of their students and adjust the pedagogical sequence on the fly. Our initial experimental results reveal that the accuracy and the quality of the automatically generated concept maps are promising. Our research work opens the door to the development and application of intelligent software tools to enhance e-Learning.

Personalised ontology learning and mining for web information gathering

Over the last decade, the rapid growth and adoption of the World Wide Web has further exacerbated user needs for e±cient mechanisms for information and knowledge location, selection, and retrieval. How to gather useful and meaningful information from the Web becomes challenging to users. The capture of user information needs is key to delivering users' desired information, and user pro¯les can help to capture information needs. However, e®ectively acquiring user pro¯les is di±cult. It is argued that if user background knowledge can be speci¯ed by ontolo- gies, more accurate user pro¯les can be acquired and thus information needs can be captured e®ectively. Web users implicitly possess concept models that are obtained from their experience and education, and use the concept models in information gathering. Prior to this work, much research has attempted to use ontologies to specify user background knowledge and user concept models. However, these works have a drawback in that they cannot move beyond the subsumption of super - and sub-class structure to emphasising the speci¯c se- mantic relations in a single computational model. This has also been a challenge for years in the knowledge engineering community. Thus, using ontologies to represent user concept models and to acquire user pro¯les remains an unsolved problem in personalised Web information gathering and knowledge engineering. In this thesis, an ontology learning and mining model is proposed to acquire user pro¯les for personalised Web information gathering. The proposed compu- tational model emphasises the speci¯c is-a and part-of semantic relations in one computational model. The world knowledge and users' Local Instance Reposito- ries are used to attempt to discover and specify user background knowledge. From a world knowledge base, personalised ontologies are constructed by adopting au- tomatic or semi-automatic techniques to extract user interest concepts, focusing on user information needs. A multidimensional ontology mining method, Speci- ¯city and Exhaustivity, is also introduced in this thesis for analysing the user background knowledge discovered and speci¯ed in user personalised ontologies. The ontology learning and mining model is evaluated by comparing with human- based and state-of-the-art computational models in experiments, using a large, standard data set. The experimental results are promising for evaluation. The proposed ontology learning and mining model in this thesis helps to develop a better understanding of user pro¯le acquisition, thus providing better design of personalised Web information gathering systems. The contributions are increasingly signi¯cant, given both the rapid explosion of Web information in recent years and today's accessibility to the Internet and the full text world.

Sir Neville Howse (VC), Private John Simpson Kirkpatrick and Private Martin O’Meara (VC) and their contributions to Australian military medicine

Medical personnel serving with the Defence Forces have contributed to the evolution of trauma treatment and the advancement of prehospital care within the military environment. This paper investigates the stories of an Australian Medical Officer, Sir Neville Howse, and two stretcher bearers, Private John Simpson (Kirkpatrick) and Private Martin O’Meara, In particular it describes the gruelling conditions under which they performed their roles, and reflects on the legacy that they have left behind in Australian society. While it is widely acknowledged that conflicts such as World War One should never have happened, as civilian and defence force paramedics, we should never forget the service and sacrifice of defence force medical personnel and their contribution to the body of knowledge on the treatment of trauma. These men and women bravely provided emergency care in the most harrowing conditions possible. However, men like Martin O’Meara may not have been given the same status in society today as Sir Neville Howse or Simpson and his donkey, due to the public’s lack of awareness and acceptance of war neurosis and conditions such as post traumatic stress disorder, reactive psychosis and somatoform disorders which were suffered by many soldiers during their wartime service and on their return home after fighting in war.

Pre-service teacher education and the development of middle school teacher identity : an exploratory study

This research study investigated the factors that influenced the development of teacher identity in a small cohort of mature-aged graduate pre-service teachers over the course of a one-year Graduate Diploma program (Middle Years). It sought to illuminate the social and relational dynamics of these pre-service teachers’ experiences as they began new ways of being and learning during a newly introduced one-year Graduate Diploma program. A relational-ontological perspective underpinned the relational-cultural framework that was applied in a workshop program as an integral part of this research. A relational-ontological perspective suggests that the development of teacher identity is to be construed more as an ontological process than an epistemological one. Its focus is more on questions surrounding the person and their ‘becoming’ a teacher than about the knowledge they have or will come to have. Hence, drawing on work by researchers such as Alsup (2006), Gilligan, (1982), Isaacs, (2007), Miller (1976), Noddings, (2005), Stout (2001), and Taylor, (1989), teacher identity was defined as an individual pre-service teacher’s unique sense of self as a teacher that included his or her beliefs about teaching and learning (Alsup, 2006; Stout, 2001; Walkington, 2005). Case-study was the preferred methodology within which this research project was framed, and narrative research was used as a method to document the way teacher identity was shaped and negotiated in discursive environments such as teacher education programs, prior experiences, classroom settings and the practicum. The data that was collected included student narratives, student email written reflections, and focus group dialogue. The narrative approach applied in this research context provided the depth of data needed to understand the nature of the mature-aged pre-service teachers’ emerging teacher identities and experiences in the graduate diploma program. Findings indicated that most of the mature-aged graduate pre-service teachers came in to the one-year graduate diploma program with a strong sense of personal and professional selves and well-established reasons why they had chosen to teach Middle Years. Their choice of program involved an expectation of support and welcome to a middle-school community and culture. Two critical issues that emerged from the pre-service teachers’ narratives were the importance they placed on the human support including the affirmation of themselves and their emerging teacher identities. Evidence from this study suggests that the lack of recognition of preservice teachers’ personal and professional selves during the graduate diploma program inhibited the development of a positive middle-school teacher identity. However, a workshop program developed for the participants in this research and addressing a range of practical concerns to beginning teachers offered them a space where they felt both a sense of belonging to a community and where their thoughts and beliefs were recognized and valued. Thus, the workshops provided participants with the positive social and relational dynamics necessary to support them in their developing teacher identities. The overall findings of this research study strongly indicate a need for a relational support structure based on a relational-ontological perspective to be built into the overall course structure of Graduate Pre-service Diplomas in Education to support the development of teacher identity. Such a support structure acknowledges that the pre-service teacher’s learning and formation is socially embedded, relational, and a continual, lifelong process.

Ontology-based image annotation

With regard to the long-standing problem of the semantic gap between low-level image features and high-level human knowledge, the image retrieval community has recently shifted its emphasis from low-level features analysis to high-level image semantics extrac- tion. User studies reveal that users tend to seek information using high-level semantics. Therefore, image semantics extraction is of great importance to content-based image retrieval because it allows the users to freely express what images they want. Semantic content annotation is the basis for semantic content retrieval. The aim of image anno- tation is to automatically obtain keywords that can be used to represent the content of images. The major research challenges in image semantic annotation are: what is the basic unit of semantic representation? how can the semantic unit be linked to high-level image knowledge? how can the contextual information be stored and utilized for image annotation? In this thesis, the Semantic Web technology (i.e. ontology) is introduced to the image semantic annotation problem. Semantic Web, the next generation web, aims at mak- ing the content of whatever type of media not only understandable to humans but also to machines. Due to the large amounts of multimedia data prevalent on the Web, re- searchers and industries are beginning to pay more attention to the Multimedia Semantic Web. The Semantic Web technology provides a new opportunity for multimedia-based applications, but the research in this area is still in its infancy. Whether ontology can be used to improve image annotation and how to best use ontology in semantic repre- sentation and extraction is still a worth-while investigation. This thesis deals with the problem of image semantic annotation using ontology and machine learning techniques in four phases as below. 1) Salient object extraction. A salient object servers as the basic unit in image semantic extraction as it captures the common visual property of the objects. Image segmen- tation is often used as the �rst step for detecting salient objects, but most segmenta- tion algorithms often fail to generate meaningful regions due to over-segmentation and under-segmentation. We develop a new salient object detection algorithm by combining multiple homogeneity criteria in a region merging framework. 2) Ontology construction. Since real-world objects tend to exist in a context within their environment, contextual information has been increasingly used for improving object recognition. In the ontology construction phase, visual-contextual ontologies are built from a large set of fully segmented and annotated images. The ontologies are composed of several types of concepts (i.e. mid-level and high-level concepts), and domain contextual knowledge. The visual-contextual ontologies stand as a user-friendly interface between low-level features and high-level concepts. 3) Image objects annotation. In this phase, each object is labelled with a mid-level concept in ontologies. First, a set of candidate labels are obtained by training Support Vectors Machines with features extracted from salient objects. After that, contextual knowledge contained in ontologies is used to obtain the �nal labels by removing the ambiguity concepts. 4) Scene semantic annotation. The scene semantic extraction phase is to get the scene type by using both mid-level concepts and domain contextual knowledge in ontologies. Domain contextual knowledge is used to create scene con�guration that describes which objects co-exist with which scene type more frequently. The scene con�guration is represented in a probabilistic graph model, and probabilistic inference is employed to calculate the scene type given an annotated image. To evaluate the proposed methods, a series of experiments have been conducted in a large set of fully annotated outdoor scene images. These include a subset of the Corel database, a subset of the LabelMe dataset, the evaluation dataset of localized semantics in images, the spatial context evaluation dataset, and the segmented and annotated IAPR TC-12 benchmark.

Loculus : an ontology-based information management framework for the motion picture industry

"How do you film a punch?" This question can be posed by actors, make-up artists, directors and cameramen. Though they can all ask the same question, they are not all seeking the same answer. Within a given domain, based on the roles they play, agents of the domain have different perspectives and they want the answers to their question from their perspective. In this example, an actor wants to know how to act when filming a scene involving a punch. A make-up artist is interested in how to do the make-up of the actor to show bruises that may result from the punch. Likewise, a director wants to know how to direct such a scene and a cameraman is seeking guidance on how best to film such a scene. This role-based difference in perspective is the underpinning of the Loculus framework for information management for the Motion Picture Industry. The Loculus framework exploits the perspective of agent for information extraction and classification within a given domain. The framework uses the positioning of the agent’s role within the domain ontology and its relatedness to other concepts in the ontology to determine the perspective of the agent. Domain ontology had to be developed for the motion picture industry as the domain lacked one. A rule-based relatedness score was developed to calculate the relative relatedness of concepts with the ontology, which were then used in the Loculus system for information exploitation and classification. The evaluation undertaken to date have yielded promising results and have indicated that exploiting perspective can lead to novel methods of information extraction and classifications.

Questioning a "Compulsory Ontology of Pathology" regarding suicide and the suicidal

Review of Suicide : Foucault, History and Truth, by Ian Marsh

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.

Ontology-based specific and exhaustive user profiles for constraint information fusion for multi-agents

Intelligent agents are an advanced technology utilized in Web Intelligence. When searching information from a distributed Web environment, information is retrieved by multi-agents on the client site and fused on the broker site. The current information fusion techniques rely on cooperation of agents to provide statistics. Such techniques are computationally expensive and unrealistic in the real world. In this paper, we introduce a model that uses a world ontology constructed from the Dewey Decimal Classification to acquire user profiles. By search using specific and exhaustive user profiles, information fusion techniques no longer rely on the statistics provided by agents. The model has been successfully evaluated using the large INEX data set simulating the distributed Web environment.

A Personalized Ontology Model for Web Information Gathering

As a model for knowledge description and formalization, ontologies are widely used to represent user profiles in personalized web information gathering. However, when representing user profiles, many models have utilized only knowledge from either a global knowledge base or a user local information. In this paper, a personalized ontology model is proposed for knowledge representation and reasoning over user profiles. This model learns ontological user profiles from both a world knowledge base and user local instance repositories. The ontology model is evaluated by comparing it against benchmark models in web information gathering. The results show that this ontology model is successful.

Heidegger's hippies : a dissenting voice on "the problem of the subject"

This is a deliberately contentious paper about the future of the socio-political sphere in the West based on what we know about its past. I argue that the predominant public discourse in Western countries is best characterised as one of selective forgetfulness; a semi-blissful, amnesiacal state of collective dementia that manifests itself in symbolic idealism: informationalism. Informationalism is merely the latest form of idealism. It is a lot like religion insofar as it causally relates abstract concepts with reality and, consequently, becomes confused between the two. Historically, this has proven to be a dangerous state of affairs, especially when elites becomes confused between ideas about how a society should work, and the way it actually does work. Central to the idealism of the information age, at least in intellectual spheres, is the so called "problem of the subject". I argue that the "problem of the subject" is a largely synthetic, destabilising, and ultimately fruitless theoretical abstraction which turns on a synthetically derived, generalised intradiscursive space; existentialist nihilism; and the theoretical baubles of ontological metaphysics. These philosophical aberrations are, in turn, historically concomitant with especially destructive political and social configurations. This paper sketches a theoretical framework for identity formation which rejects the problem of the subject, and proposes potential resources, sources, and strategies with which to engage the idealism that underpins this obfuscating problematic in an age of turbulent social uncertainty. Quite simply, I turn to history as the source of human identity. While informationalism, like religion, is mostly focused on utopian futures, I assert that history, not the future, holds the solutions for substantive problematics concerning individual and social identities. I argue here that history, language, thought, and identity are indissolubly entangled and so should be understood as such: they are the fundamental parts of 'identities in action'. From this perspective, the ‘problem of the subject’ becomes less a substantive intellectual problematic and more a theoretical red herring.

Learning domain ontology for tag recommendation

Recently, user tagging systems have grown in popularity on the web. The tagging process is quite simple for ordinary users, which contributes to its popularity. However, free vocabulary has lack of standardization and semantic ambiguity. It is possible to capture the semantics from user tagging and represent those in a form of ontology, but the application of the learned ontology for recommendation making has not been that flourishing. In this paper we discuss our approach to learn domain ontology from user tagging information and apply the extracted tag ontology in a pilot tag recommendation experiment. The initial result shows that by using the tag ontology to re-rank the recommended tags, the accuracy of the tag recommendation can be improved.