12 research outputs found
Cross-language Ontology Learning: Incorporating and Exploiting Cross-language Data in the Ontology Learning Process
Hans Hjelm. Cross-language Ontology Learning:
Incorporating and Exploiting Cross-language Data in the Ontology Learning Process.
NEALT Monograph Series, Vol. 1 (2009), 159 pages.
© 2009 Hans Hjelm.
Published by
Northern European Association for Language
Technology (NEALT)
http://omilia.uio.no/nealt .
Electronically published at
Tartu University Library (Estonia)
http://hdl.handle.net/10062/10126
Eri meetodeid wordnet-tüüpi sõnastiku kontrolliks Eesti Wordneti näitel
https://www.ester.ee/record=b5358502*es
Model-based Specification and Analysis of Natural Language Requirements in the Financial Domain
Software requirements form an important part of the software development process. In many software projects conducted by companies in the financial sector, analysts specify software requirements using a combination of models and natural language (NL). Neither models nor NL requirements provide a complete picture of the information in the software system, and NL is highly prone to quality issues, such as vagueness, ambiguity, and incompleteness. Poorly written requirements are difficult to communicate and reduce the opportunity to process requirements automatically, particularly the automation of tedious and error-prone tasks, such as deriving acceptance criteria (AC). AC are conditions that a system must meet to be consistent with its requirements and be accepted by its stakeholders. AC are derived by developers and testers from requirement models. To obtain a precise AC, it is necessary to reconcile the information content in NL requirements and the requirement models.
In collaboration with an industrial partner from the financial domain, we first systematically developed and evaluated a controlled natural language (CNL) named Rimay to help analysts write functional requirements. We then proposed an approach that detects common syntactic and semantic errors in NL requirements. Our approach suggests Rimay patterns to fix errors and convert NL requirements into Rimay requirements. Based on our results, we propose a semiautomated approach that reconciles the content in the NL requirements with that in the requirement models. Our approach helps modelers enrich their models with information extracted from NL requirements. Finally, an existing test-specification derivation technique was applied to the enriched model to generate AC.
The first contribution of this dissertation is a qualitative methodology that can be used to systematically define a CNL for specifying functional requirements. This methodology was used to create Rimay, a CNL grammar, to specify functional requirements. This CNL was derived after an extensive qualitative analysis of a large number of industrial requirements and by following a systematic process using lexical resources. An empirical evaluation of our CNL (Rimay) in a realistic setting through an industrial case study demonstrated that 88% of the requirements used in our empirical evaluation were successfully rephrased using Rimay.
The second contribution of this dissertation is an automated approach that detects syntactic and semantic errors in unstructured NL requirements. We refer to these errors as smells. To this end, we first proposed a set of 10 common smells found in the NL requirements of financial applications. We then derived a set of 10 Rimay patterns as a suggestion to fix the smells. Finally, we developed an automatic approach that analyzes the syntax and semantics of NL requirements to detect any present smells and then suggests a Rimay pattern to fix the smell. We evaluated our approach using an industrial case study that obtained promising results for detecting smells in NL requirements (precision 88%) and for suggesting Rimay patterns (precision 89%).
The last contribution of this dissertation was prompted by the observation that a reconciliation of the information content in the NL requirements and the associated models is necessary to obtain precise AC. To achieve this, we define a set of 13 information extraction rules that automatically extract AC-related information from NL requirements written in Rimay. Next, we propose a systematic method that generates recommendations for model enrichment based on the information extracted from the 13 extraction rules. Using a real case study from the financial domain, we evaluated the usefulness of the AC-related model enrichments recommended by our approach. The domain experts found that 89% of the recommended enrichments were relevant to AC, but absent from the original model (precision of 89%)
Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan languages
Proceedings of the Seventh International Conference Formal Approaches to South Slavic and Balkan Languages publishes 17 papers that were presented at the conference organised in Dubrovnik, Croatia, 4-6 Octobre 2010
Visual Analytics for the Exploratory Analysis and Labeling of Cultural Data
Cultural data can come in various forms and modalities, such as text traditions, artworks, music, crafted objects, or even as intangible heritage such as biographies of people, performing arts, cultural customs and rites.
The assignment of metadata to such cultural heritage objects is an important task that people working in galleries, libraries, archives, and museums (GLAM) do on a daily basis.
These rich metadata collections are used to categorize, structure, and study collections, but can also be used to apply computational methods.
Such computational methods are in the focus of Computational and Digital Humanities projects and research.
For the longest time, the digital humanities community has focused on textual corpora, including text mining, and other natural language processing techniques.
Although some disciplines of the humanities, such as art history and archaeology have a long history of using visualizations.
In recent years, the digital humanities community has started to shift the focus to include other modalities, such as audio-visual data.
In turn, methods in machine learning and computer vision have been proposed for the specificities of such corpora.
Over the last decade, the visualization community has engaged in several collaborations with the digital humanities, often with a focus on exploratory or comparative analysis of the data at hand.
This includes both methods and systems that support classical Close Reading of the material and Distant Reading methods that give an overview of larger collections, as well as methods in between, such as Meso Reading.
Furthermore, a wider application of machine learning methods can be observed on cultural heritage collections.
But they are rarely applied together with visualizations to allow for further perspectives on the collections in a visual analytics or human-in-the-loop setting.
Visual analytics can help in the decision-making process by guiding domain experts through the collection of interest.
However, state-of-the-art supervised machine learning methods are often not applicable to the collection of interest due to missing ground truth.
One form of ground truth are class labels, e.g., of entities depicted in an image collection, assigned to the individual images.
Labeling all objects in a collection is an arduous task when performed manually, because cultural heritage collections contain a wide variety of different objects with plenty of details.
A problem that arises with these collections curated in different institutions is that not always a specific standard is followed, so the vocabulary used can drift apart from another, making it difficult to combine the data from these institutions for large-scale analysis.
This thesis presents a series of projects that combine machine learning methods with interactive visualizations for the exploratory analysis and labeling of cultural data.
First, we define cultural data with regard to heritage and contemporary data, then we look at the state-of-the-art of existing visualization, computer vision, and visual analytics methods and projects focusing on cultural data collections.
After this, we present the problems addressed in this thesis and their solutions, starting with a series of visualizations to explore different facets of rap lyrics and rap artists with a focus on text reuse.
Next, we engage in a more complex case of text reuse, the collation of medieval vernacular text editions.
For this, a human-in-the-loop process is presented that applies word embeddings and interactive visualizations to perform textual alignments on under-resourced languages supported by labeling of the relations between lines and the relations between words.
We then switch the focus from textual data to another modality of cultural data by presenting a Virtual Museum that combines interactive visualizations and computer vision in order to explore a collection of artworks.
With the lessons learned from the previous projects, we engage in the labeling and analysis of medieval illuminated manuscripts and so combine some of the machine learning methods and visualizations that were used for textual data with computer vision methods.
Finally, we give reflections on the interdisciplinary projects and the lessons learned, before we discuss existing challenges when working with cultural heritage data from the computer science perspective to outline potential research directions for machine learning and visual analytics of cultural heritage data
Innovations for Requirements Analysis, From Stakeholders' Needs to Formal Designs
14th MontereyWorkshop 2007
Monterey, CA, USA, September 10-13, 2007
Revised Selected PapersWe are pleased to present the proceedings of the 14thMontereyWorkshop, which
took place September 10–13, 2007 in Monterey, CA, USA. In this preface, we give
the reader an overview of what took place at the workshop and introduce the
contributions in this Lecture Notes in Computer Science volume. A complete
introduction to the theme of the workshop, as well as to the history of the
Monterey Workshop series, can be found in Luqi and Kordon’s “Advances in
Requirements Engineering: Bridging the Gap between Stakeholders’ Needs and
Formal Designs” in this volume. This paper also contains the case study that
many participants used as a problem to frame their analyses, and a summary of
the workshop’s results
Emerging Informatics
The book on emerging informatics brings together the new concepts and applications that will help define and outline problem solving methods and features in designing business and human systems. It covers international aspects of information systems design in which many relevant technologies are introduced for the welfare of human and business systems. This initiative can be viewed as an emergent area of informatics that helps better conceptualise and design new world-class solutions. The book provides four flexible sections that accommodate total of fourteen chapters. The section specifies learning contexts in emerging fields. Each chapter presents a clear basis through the problem conception and its applicable technological solutions. I hope this will help further exploration of knowledge in the informatics discipline
LWA 2013. Lernen, Wissen & Adaptivität ; Workshop Proceedings Bamberg, 7.-9. October 2013
LWA Workshop Proceedings: LWA stands for "Lernen, Wissen, Adaption" (Learning, Knowledge, Adaptation). It is the joint forum of four special interest groups of the German Computer Science Society (GI). Following the tradition of the last years, LWA provides a joint forum for experienced and for young researchers, to bring insights to recent trends, technologies and applications, and to promote interaction among the SIGs
Multi-Agent Systems
A multi-agent system (MAS) is a system composed of multiple interacting intelligent agents. Multi-agent systems can be used to solve problems which are difficult or impossible for an individual agent or monolithic system to solve. Agent systems are open and extensible systems that allow for the deployment of autonomous and proactive software components. Multi-agent systems have been brought up and used in several application domains