Common vocabularies for collective intelligence - work in progress

Web based applications and tools offer a great potential to increase the efficiency of information flow and communication among different agents during emergencies. Among the different factors, technical and non technical, that hinder the integration of an information model in emergency management sector, is a lack of a common, shared vocabulary. This paper furthers previous work in the area of ontology development, and presents a summary and overview of the goal, process and methodology to construct a shared set of metadata that can be used to map existing vocabulary. This paper is a work in progress report

HILT IV : subject interoperability through building and embedding pilot terminology web services

A report of work carried out within the JISC-funded HILT Phase IV project, the paper looks at the project's context against the background of other recent and ongoing terminologies work, describes its outcome and conclusions, including technical outcomes and terminological characteristics, and considers possible future research and development directions. The Phase IV project has taken HILT to the point where the launch of an operational support service in the area of subject interoperability is a feasible option and where both investigation of specific needs in this area and practical collaborative work are sensible and feasible next steps. Moving forward requires detailed work, not only on terminology interoperability and associated service delivery issues, but also on service and end user needs and engagement, service sustainability issues, and the practicalities of interworking with other terminology services and projects in UK, Europe, and global contexts

Framework for Enhanced Ontology Alignment using BERT-Based

This framework combines a few approaches to improve ontology alignment by using the data mining method with BERT. The method utilizes data mining techniques to identify the optimal characteristics for picking the data attributes of instances to match ontologies. Furthermore, this framework was developed to improve current precision and recall measures for ontology matching techniques. Since knowledge integration began, the main requirement for ontology alignment has always been syntactic and structural matching. This article presents a new approach that employs advanced methods like data mining and BERT embeddings to produce more expansive and contextually aware ontology alignment. The proposed system exploits contextual representation of BERT, semantic understanding, feature extraction, and pattern recognition through data mining techniques. The objective is to combine data-driven insights with semantic representation advantages to enhance accuracy and efficiency in the ontology alignment process. The evaluation conducted using annotated datasets as well as traditional approaches demonstrates how effective and adaptable, according to domains, our proposed framework is across several domains

Terminology server for improved resource discovery: analysis of model and functions

This paper considers the potential to improve distributed information retrieval via a terminologies server. The restriction upon effective resource discovery caused by the use of disparate terminologies across services and collections is outlined, before considering a DDC spine based approach involving inter-scheme mapping as a possible solution. The developing HILT model is discussed alongside other existing models and alternative approaches to solving the terminologies problem. Results from the current HILT pilot are presented to illustrate functionality and suggestions are made for further research and development

Understanding analogical reasoning : viewpoints from psychology and related disciplines

Analogy and metaphor have a long history of study in linguistics, education, philosophy and psychology. Consensus over what analogy is or how analogy functions in language and thought, however, has been elusive. This paper, the first in a two part series, examines these various research traditions, attempting to bring out major lines of agreement over the role of analogy in individual human experience. As well as being a general literature review which may be helpful for newcomers to the study of analogy, this paper attempts to extract from these literatures existing theories, models and concepts which may be interesting or useful for computational studies of analogical reasoning

Fusing Automatically Extracted Annotations for the Semantic Web

This research focuses on the problem of semantic data fusion. Although various solutions have been developed in the research communities focusing on databases and formal logic, the choice of an appropriate algorithm is non-trivial because the performance of each algorithm and its optimal configuration parameters depend on the type of data, to which the algorithm is applied. In order to be reusable, the fusion system must be able to select appropriate techniques and use them in combination. Moreover, because of the varying reliability of data sources and algorithms performing fusion subtasks, uncertainty is an inherent feature of semantically annotated data and has to be taken into account by the fusion system. Finally, the issue of schema heterogeneity can have a negative impact on the fusion performance. To address these issues, we propose KnoFuss: an architecture for Semantic Web data integration based on the principles of problem-solving methods. Algorithms dealing with different fusion subtasks are represented as components of a modular architecture, and their capabilities are described formally. This allows the architecture to select appropriate methods and configure them depending on the processed data. In order to handle uncertainty, we propose a novel algorithm based on the Dempster-Shafer belief propagation. KnoFuss employs this algorithm to reason about uncertain data and method results in order to refine the fused knowledge base. Tests show that these solutions lead to improved fusion performance. Finally, we addressed the problem of data fusion in the presence of schema heterogeneity. We extended the KnoFuss framework to exploit results of automatic schema alignment tools and proposed our own schema matching algorithm aimed at facilitating data fusion in the Linked Data environment. We conducted experiments with this approach and obtained a substantial improvement in performance in comparison with public data repositories