424,404 research outputs found
End-to-End Software Construction using ChatGPT: An Experience Report
In this paper, we explore the application of Large Language Models (LLMs) in
the particular context of end-to-end software construction, i.e., in contexts
where software developers have a set of requirements and have to design,
implement, test, and validate a new software system. Particularly, we report an
experiment where we asked three software developers to use ChatGPT to fully
implement a Web-based application using mainstream software architectures and
technologies. After that, we compare the apps produced by ChatGPT with a
reference implementation that we manually implemented for our research. As a
result, we document four categories of prompts that can be used by developers
in similar contexts, including initialization prompts, feature requests,
bug-fixing, and layout prompts. Additionally, we discuss the advantages and
disadvantages of two prompt construction approaches: top-down (where we start
with a high-level description of the target software, typically in the form of
user stories) and bottom-up (where we request the construction of the system
feature by feature)
Interchanging lexical resources on the Semantic Web
Lexica and terminology databases play a vital role in many NLP applications, but currently most such resources are published in application-specific formats, or with custom access interfaces, leading to the problem that much of this data is in ââdata silosââ and hence difficult to access. The Semantic Web and in particular the Linked Data initiative provide effective solutions to this problem, as well as possibilities for data reuse by inter-lexicon linking, and incorporation of data categories by dereferencable URIs. The Semantic Web focuses on the use of ontologies to describe semantics on the Web, but currently there is no standard for providing complex lexical information for such ontologies and for describing the relationship between the lexicon and the ontology. We present our model, lemon, which aims to address these gap
A characteristics framework for Semantic Information Systems Standards
Semantic Information Systems (IS) Standards play a critical role in the development of the networked economy. While their importance is undoubted by all stakeholdersâsuch as businesses, policy makers, researchers, developersâthe current state of research leaves a number of questions unaddressed. Terminological confusion exists around the notions of âbusiness semanticsâ, âbusiness-to-business interoperabilityâ, and âinteroperability standardsâ amongst others. And, moreover, a comprehensive understanding about the characteristics of Semantic IS Standards is missing. The paper addresses this gap in literature by developing a characteristics framework for Semantic IS Standards. Two case studies are used to check the applicability of the framework in a âreal-lifeâ context. The framework lays the foundation for future research in an important field of the IS discipline and supports practitioners in their efforts to analyze, compare, and evaluate Semantic IS Standard
Named Entity Extraction and Disambiguation: The Reinforcement Effect.
Named entity extraction and disambiguation have received much attention in recent years. Typical fields addressing these topics are information retrieval, natural language processing, and semantic web. Although these topics are highly dependent, almost no existing works examine this dependency. It is the aim of this paper to examine the dependency and show how one affects the other, and vice versa. We conducted experiments with a set of descriptions of holiday homes with the aim to extract and disambiguate toponyms as a representative example of named entities. We experimented with three approaches for disambiguation with the purpose to infer the country of the holiday home. We examined how the effectiveness of extraction influences the effectiveness of disambiguation, and reciprocally, how filtering out ambiguous names (an activity that depends on the disambiguation process) improves the effectiveness of extraction. Since this, in turn, may improve the effectiveness of disambiguation again, it shows that extraction and disambiguation may reinforce each other.\u
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