Ontology learning for semantic web services

Semantic Web Services promise automatic service discovery and composition, relying heavily on domain ontology as a core component. With large Web Service repository, manual ontology development is proving a bottleneck (with associated expense and likely errors) to the realisation of a semantic Web of services. Providing the appropriate tools that assist in and automate ontology development is essential for a dynamic service vision to be realised. As a statement of research-in-progress, this paper proposes combining different ontology learning paradigms in Web Services domain, highlighting the need for further research that accommodates the variation in Web Service descriptive and operational sources. A research agenda is proposed that recognises this variation in artefacts as they are selected, pre-processed and analyzed by ontology learning techniques

Producing Navigable Knowledge Organization with Knowledge Interaction

Knowledge interaction combines concept theory, instantiation theory, and the taxonomy of knowledge elements to suggest that knowledge organization systems might be used effectively to integrate different dimensional representations of information objects. Understanding knowledge structurally as well as semantically can lead to a variety of implementations that might provide temporal interfaces for understanding relationships among information objects that are not obviously semantically related. An experimental test-­‐bed would rely on the actual experience of working scholars. Preliminary results come from a case study of the works of one prolific New Testament scholar whose works are available in digital form. We see clearly the distance between the theological positions, sociological interpretive positions, and methodological positions, indicating three interacting intellectual poles in this scholar’s writing

Mining Concepts from Wikipedia for Ontology Construction

An ontology is a structured knowledgebase of concepts organized by relations among them. But concepts are usually mixed with their instances in the corpora for knowledge extraction. Concepts and their corresponding instances share similar features and are difficult to distinguish. In this paper, a novel approach is proposed to comprehensively obtain concepts with the help of definition sentences and Category Labels in Wikipedia pages. N-gram statistics and other NLP knowledge are used to help extracting appropriate concepts. The proposed method identified nearly 50,000 concepts from about 700,000 Wiki pages. The precision reaching 78.5% makes it an effective approach to mine concepts from Wikipedia for ontology construction.Department of Computin

WATERFALL METHOD OF WEB-BASED SYSTEM TO DEVELOP WAREHOUSE PACKING EFFECTIVELY

This study aims to develop an efficient Warehouse Packing System for PT Luna Technology Group in order to improve operational efficiency and minimize packing errors in logistics processes. The research adopts the Waterfall Method, employing a systematic approach that encompasses requirements analysis, system design, implementation, and testing. Data was collected through qualitative observational methods, including interviews and direct observations of the existing packing processes. The findings demonstrate that the implementation of a web-based packing system significantly reduces packing times, mitigates errors in item selection, and lowers operational costs compared to the previous manual methods. The research concludes that embracing a structured software development methodology not only enhances the packing process, but also positions PT Luna Technology Group as a competitive player in the logistics industry, underscoring the need for continuous technological advancements in warehouse management

Word Sense Disambiguation for Ontology Learning

Ontology learning aims to automatically extract ontological concepts and relationships from related text repositories and is expected to be more efficient and scalable than manual ontology development. One of the challenging issues associated with ontology learning is word sense disambiguation (WSD). Most WSD research employs resources such as WordNet, text corpora, or a hybrid approach. Motivated by the large volume and richness of user-generated content in social media, this research explores the role of social media in ontology learning. Specifically, our approach exploits social media as a dynamic context rich data source for WSD. This paper presents a method and preliminary evidence for the efficacy of our proposed method for WSD. The research is in progress toward conducting a formal evaluation of the social media based method for WSD, and plans to incorporate the WSD routine into an ontology learning system in the future