34 research outputs found
Digital Availability of Product Information for Collaborative Engineering of Spacecraft
In this paper, we introduce a system to collect product information from
manufacturers and make it available in tools that are used for concurrent
design of spacecraft. The planning of a spacecraft needs experts from different
disciplines, like propulsion, power, and thermal. Since these different
disciplines rely on each other there is a high need for communication between
them, which is often realized by a Model-Based Systems Engineering (MBSE)
process and corresponding tools. We show by comparison that the product
information provided by manufacturers often does not match the information
needed by MBSE tools on a syntactic or semantic level. The information from
manufacturers is also currently not available in machine-readable formats.
Afterwards, we present a prototype of a system that makes product information
from manufacturers directly available in MBSE tools, in a machine-readable way.Comment: accepted at CDVE201
The Legal Argument Reasoning Task in Civil Procedure
We present a new NLP task and dataset from the domain of the U.S. civil
procedure. Each instance of the dataset consists of a general introduction to
the case, a particular question, and a possible solution argument, accompanied
by a detailed analysis of why the argument applies in that case. Since the
dataset is based on a book aimed at law students, we believe that it represents
a truly complex task for benchmarking modern legal language models. Our
baseline evaluation shows that fine-tuning a legal transformer provides some
advantage over random baseline models, but our analysis reveals that the actual
ability to infer legal arguments remains a challenging open research question.Comment: Camera ready, to appear at the Natural Legal Language Processing
Workshop 2022 co-located with EMNL
A Novel Approach to Ontology Management
The term ontology is defined as the explicit specification of a conceptualization. While much of the prior research has focused on technical aspects of ontology management, little attention has been paid to the investigation of issues that limit the widespread use of ontologies and the evaluation of the effectiveness of ontologies in improving task performance. This dissertation addresses this void through the development of approaches to ontology creation, refinement, and evaluation. This study follows a multi-paper model focusing on ontology creation, refinement, and its evaluation. The first study develops and evaluates a method for ontology creation using knowledge available on the Web. The second study develops a methodology for ontology refinement through pruning and empirically evaluates the effectiveness of this method. The third study investigates the impact of an ontology in use case modeling, which is a complex, knowledge intensive organizational task in the context of IS development. The three studies follow the design science research approach, and each builds and evaluates IT artifacts. These studies contribute to knowledge by developing solutions to three important issues in the effective development and use of ontologies
Role of Semantic web in the changing context of Enterprise Collaboration
In order to compete with the global giants, enterprises are concentrating on
their core competencies and collaborating with organizations that compliment their
skills and core activities. The current trend is to develop temporary alliances of
independent enterprises, in which companies can come together to share skills, core
competencies and resources. However, knowledge sharing and communication
among multidiscipline companies is a complex and challenging problem. In a
collaborative environment, the meaning of knowledge is drastically affected by the
context in which it is viewed and interpreted; thus necessitating the treatment of
structure as well as semantics of the data stored in enterprise repositories. Keeping
the present market and technological scenario in mind, this research aims to propose
tools and techniques that can enable companies to assimilate distributed information
resources and achieve their business goals
Garbage In, Garbage Out? Do Machine Learning Application Papers in Social Computing Report Where Human-Labeled Training Data Comes From?
Many machine learning projects for new application areas involve teams of
humans who label data for a particular purpose, from hiring crowdworkers to the
paper's authors labeling the data themselves. Such a task is quite similar to
(or a form of) structured content analysis, which is a longstanding methodology
in the social sciences and humanities, with many established best practices. In
this paper, we investigate to what extent a sample of machine learning
application papers in social computing --- specifically papers from ArXiv and
traditional publications performing an ML classification task on Twitter data
--- give specific details about whether such best practices were followed. Our
team conducted multiple rounds of structured content analysis of each paper,
making determinations such as: Does the paper report who the labelers were,
what their qualifications were, whether they independently labeled the same
items, whether inter-rater reliability metrics were disclosed, what level of
training and/or instructions were given to labelers, whether compensation for
crowdworkers is disclosed, and if the training data is publicly available. We
find a wide divergence in whether such practices were followed and documented.
Much of machine learning research and education focuses on what is done once a
"gold standard" of training data is available, but we discuss issues around the
equally-important aspect of whether such data is reliable in the first place.Comment: 18 pages, includes appendi
Proceedings of the 1st Doctoral Consortium at the European Conference on Artificial Intelligence (DC-ECAI 2020)
1st Doctoral Consortium at the European Conference on
Artificial Intelligence (DC-ECAI 2020), 29-30 August, 2020
Santiago de Compostela, SpainThe DC-ECAI 2020 provides a unique opportunity for PhD students, who are close to finishing their doctorate research, to interact with experienced researchers in the field. Senior members of the community are assigned as mentors for each group of students based on the student鈥檚 research or similarity of research interests. The DC-ECAI 2020, which is held virtually this year, allows students from all over the world to present their research and discuss their ongoing research and career plans with their mentor, to do networking with other participants, and to receive training and mentoring about career planning and career option