Hypotheses, evidence and relationships: The HypER approach for representing scientific knowledge claims

Biological knowledge is increasingly represented as a collection of (entity-relationship-entity) triplets. These are queried, mined, appended to papers, and published. However, this representation ignores the argumentation contained within a paper and the relationships between hypotheses, claims and evidence put forth in the article. In this paper, we propose an alternate view of the research article as a network of 'hypotheses and evidence'. Our knowledge representation focuses on scientific discourse as a rhetorical activity, which leads to a different direction in the development of tools and processes for modeling this discourse. We propose to extract knowledge from the article to allow the construction of a system where a specific scientific claim is connected, through trails of meaningful relationships, to experimental evidence. We discuss some current efforts and future plans in this area

Adaptation of NLP Techniques to Cultural Heritage Research and Documentation

The WissKI system provides a framework for ontology based science communication and cultural heritage documentation. In many cases, the documentation consists of semi-structured data records with free text fields. Most references in the texts comprise of person and place names, as well as time specifications. We present the WissKI tools for semantic annotation using controlled vocabularies and formal ontologies derived from CIDOC Conceptual Reference Model (CRM). Current research deals with the annotations as building blocks for event recognition. Finally, we outline how the CRM helps to build bridges between documentation in different scientific disciplines

The Semantic Web: Apotheosis of annotation, but what are its semantics?

This article discusses what kind of entity the proposed Semantic Web (SW) is, principally by reference to the relationship of natural language structure to knowledge representation (KR). There are three distinct views on this issue. The first is that the SW is basically a renaming of the traditional AI KR task, with all its problems and challenges. The second view is that the SW will be, at a minimum, the World Wide Web with its constituent documents annotated so as to yield their content, or meaning structure, more directly. This view makes natural language processing central as the procedural bridge from texts to KR, usually via some form of automated information extraction. The third view is that the SW is about trusted databases as the foundation of a system of Web processes and services. There's also a fourth view, which is much more difficult to define and discuss: If the SW just keeps moving as an engineering development and is lucky, then real problems won't arise. This article is part of a special issue called Semantic Web Update

From Text to Knowledge with Graphs: modelling, querying and exploiting textual content

This paper highlights the challenges, current trends, and open issues related to the representation, querying and analytics of content extracted from texts. The internet contains vast text-based information on various subjects, including commercial documents, medical records, scientific experiments, engineering tests, and events that impact urban and natural environments. Extracting knowledge from this text involves understanding the nuances of natural language and accurately representing the content without losing information. This allows knowledge to be accessed, inferred, or discovered. To achieve this, combining results from various fields, such as linguistics, natural language processing, knowledge representation, data storage, querying, and analytics, is necessary. The vision in this paper is that graphs can be a well-suited text content representation once annotated and the right querying and analytics techniques are applied. This paper discusses this hypothesis from the perspective of linguistics, natural language processing, graph models and databases and artificial intelligence provided by the panellists of the DOING session in the MADICS Symposium 2022

Semiotic Annotation of Narrative Video Commercials: Bridging the Gap between Artifacts and Ontologies

Drawing on semiotic theories, the paper proposes a new concept of annotation \u2013 called semiotic annotation \u2013 whose goal is to describe the multilayered articulation of meaning inscribed within narrative video commercials by their designers. The approach exploits the use of a meta-model of the narrative video genre providing the conceptualizations and the vocabulary for analysis and annotation. By explicating design knowledge embodied in the video, semiotic annotation plays the role of intermediate level knowledge between the meta-model (an informal ontology) and practice (the concrete video artifact). In order to assess the feasibility of the approach, a test bed is presented and results are reported. A final discussion about the potential contribution of semiotic annotation in the fields of Research Through Design, Technological Mediation, and Interface Criticism concludes the study

An open annotation ontology for science on web 3.0

Background: There is currently a gap between the rich and expressive collection of published biomedical ontologies, and the natural language expression of biomedical papers consumed on a daily basis by scientific researchers. The purpose of this paper is to provide an open, shareable structure for dynamic integration of biomedical domain ontologies with the scientific document, in the form of an Annotation Ontology (AO), thus closing this gap and enabling application of formal biomedical ontologies directly to the literature as it emerges. Methods: Initial requirements for AO were elicited by analysis of integration needs between biomedical web communities, and of needs for representing and integrating results of biomedical text mining. Analysis of strengths and weaknesses of previous efforts in this area was also performed. A series of increasingly refined annotation tools were then developed along with a metadata model in OWL, and deployed for feedback and additional requirements the ontology to users at a major pharmaceutical company and a major academic center. Further requirements and critiques of the model were also elicited through discussions with many colleagues and incorporated into the work. Results: This paper presents Annotation Ontology (AO), an open ontology in OWL-DL for annotating scientific documents on the web. AO supports both human and algorithmic content annotation. It enables "stand-off" or independent metadata anchored to specific positions in a web document by any one of several methods. In AO, the document may be annotated but is not required to be under update control of the annotator. AO contains a provenance model to support versioning, and a set model for specifying groups and containers of annotation. AO is freely available under open source license at http://purl.org/ao/, and extensive documentation including screencasts is available on AO's Google Code page: http://code.google.com/p/annotation-ontology/. Conclusions: The Annotation Ontology meets critical requirements for an open, freely shareable model in OWL, of annotation metadata created against scientific documents on the Web. We believe AO can become a very useful common model for annotation metadata on Web documents, and will enable biomedical domain ontologies to be used quite widely to annotate the scientific literature. Potential collaborators and those with new relevant use cases are invited to contact the authors

SWA-KMDLS: An Enhanced e-Learning Management System Using Semantic Web and Knowledge Management Technology

In this era of knowledge economy in which knowledge have become the most precious resource, surveys have shown that e-Learning has been on the increasing trend in various organizations including, among others, education and corporate. The use of e-Learning is not only aim to acquire knowledge but also to maintain competitiveness and advantages for individuals or organizations. However, the early promise of e-Learning has yet to be fully realized, as it has been no more than a handout being published online, coupled with simple multiple-choice quizzes. The emerging of e-Learning 2.0 that is empowered by Web 2.0 technology still hardly overcome common problem such as information overload and poor content aggregation in a highly increasing number of learning objects in an e-Learning Management System (LMS) environment. The aim of this research study is to exploit the Semantic Web (SW) and Knowledge Management (KM) technology; the two emerging and promising technology to enhance the existing LMS. The proposed system is named as Semantic Web Aware-Knowledge Management Driven e-Learning System (SWA-KMDLS). An Ontology approach that is the backbone of SW and KM is introduced for managing knowledge especially from learning object and developing automated question answering system (Aquas) with expert locator in SWA-KMDLS. The METHONTOLOGY methodology is selected to develop the Ontology in this research work. The potential of SW and KM technology is identified in this research finding which will benefit e-Learning developer to develop e-Learning system especially with social constructivist pedagogical approach from the point of view of KM framework and SW environment. The (semi-) automatic ontological knowledge base construction system (SAOKBCS) has contributed to knowledge extraction from learning object semiautomatically whilst the Aquas with expert locator has facilitated knowledge retrieval that encourages knowledge sharing in e-Learning environment. The experiment conducted has shown that the SAOKBCS can extract concept that is the main component of Ontology from text learning object with precision of 86.67%, thus saving the expert time and effort to build Ontology manually. Additionally the experiment on Aquas has shown that more than 80% of users are satisfied with answers provided by the system. The expert locator framework can also improve the performance of Aquas in the future usage. Keywords: semantic web aware – knowledge e-Learning Management System (SWAKMDLS), semi-automatic ontological knowledge base construction system (SAOKBCS), automated question answering system (Aquas), Ontology, expert locator

Ontology-based information standards development

This thesis was submitted for the degree of Doctor of Philosophy and awarded by Brunel University.Standards may be argued to be important enablers for achieving interoperability as they aim to provide unambiguous specifications for error-free exchange of documents and information. By implication, therefore, it is important to model and represent the concept of a standard in a clear, precise and unambiguous way. Although standards development organisations usually provide guidelines for the process of developing and approving standards, they are usually more concerned with administrative aspect of the process. As a consequence, the state-of-the-art lacks practical support for developing the structure and content of a standard specification. In short, there is no systematic development method currently available: (a) For developing the conceptual model underpinning a standard; and/or (b) to guide a group of stakeholders to develop a standard specification. Semantic interoperability is considered to be an essential factor for effective interoperation – the ability to achieve semantic interoperability effectively and efficiently being strongly equated with quality by some. Semantics require that the meaning of terms, their relationships and also the restrictions and rules in the standards should be clearly defined in the early stages of standard development and act as a basis for the latter stages. This research proposes that ontology can help standards developers and stakeholders to address the issues of improving conceptual models and providing a robust and shared understanding of the domain. This thesis presents OntoStanD, a comprehensive ontology-based standards development methodology, which utilises the best practices of the existing ontology creation methods. The potential value of OntoStanD is in providing a comprehensive, clear and unambiguous method for developing robust information standards, which are more test friendly and of higher quality. OntoStanD also facilitates standards conformance testing and change management, impacts interoperability and also assists in improved communication among the standards development team. Last, OntoStanD provides an approach that is repeatable, teachable and potentially general enough for creating any kinds of information standard.Fujitsu Laboratories of Europe Ltd, Google Anitaborg Memorial Scholarshi

Collaborative tagging : folksonomy, metadata, visualization, e-learning, thesis

Collaborative tagging is a simple and effective method for organizing and sharing web resources using human created metadata. It has arisen out of the need for an efficient method of personal organization, as the number of digital resources in everyday lives increases. While tagging has become a proven organization scheme through its popularity and widespread use on the Web, little is known about its implications and how it may effectively be applied in different situations. This is due to the fact that tagging has evolved through several iterations of use on social software websites, rather than through a scientific or an engineering design process. The research presented in this thesis, through investigations in the domain of e-learning, seeks to understand more about the scientific nature of collaborative tagging through a number of human subject studies. While broad in scope, touching on issues in human computer interaction, knowledge representation, Web system architecture, e-learning, metadata, and information visualization, this thesis focuses on how collaborative tagging can supplement the growing metadata requirements of e-learning. I conclude by looking at how the findings may be used in future research, through using information based in the emergent social networks of social software, to automatically adapt to the needs of individual users