5 research outputs found
Topical Classification of Food Safety Publications with a Knowledge Base
The vast body of scientific publications presents an increasing challenge of
finding those that are relevant to a given research question, and making
informed decisions on their basis. This becomes extremely difficult without the
use of automated tools. Here, one possible area for improvement is automatic
classification of publication abstracts according to their topic. This work
introduces a novel, knowledge base-oriented publication classifier. The
proposed method focuses on achieving scalability and easy adaptability to other
domains. Classification speed and accuracy are shown to be satisfactory, in the
very demanding field of food safety. Further development and evaluation of the
method is needed, as the proposed approach shows much potential
Feature Extraction for Polish Language Named Entities Recognition in Intelligent Office Assistant
The purpose of this contribution is to present a feature extractor that was designed as a part of a Named Entity Recognition (NER) system, which is to be used in a Robotic Process Automation application with a self-learning ability. The NER system has a screen of the user interface as its input, and tries to recognize and categorize all the named entities that can be located within this screen. The set of features that can be extracted from the input, is discussed in the article. The local context features appear to be very important in the considered problem. Experiments show that the entities are recognized with a rate that is satisfactory from the business perspective
Multi-Domain Named Entity Recognition for Robotic Process Automation
To make Robotic Process Automation more attractive, it needs to become more ``intelligent''. In this context, a modification of the Form-to-Rule approach, based on identifying data types of form fields, is proposed. Moreover, multi-domain named entity recognition is used, for field value identification. These techniques, used jointly, allow software robots to adapt to interface changes. Experimental results are reported and verify viability of the proposed approach
Ontology Reuse: the Real Test of Ontological Design
Reusing ontologies in practice is still very challenging, especially when
multiple ontologies are (jointly) involved. Moreover, despite recent advances,
the realization of systematic ontology quality assurance remains a difficult
problem. In this work, the quality of thirty biomedical ontologies, and the
Computer Science Ontology are investigated, from the perspective of a practical
use case. Special scrutiny is given to cross-ontology references, which are
vital for combining ontologies. Diverse methods to detect potential issues are
proposed, including natural language processing and network analysis. Moreover,
several suggestions for improving ontologies and their quality assurance
processes are presented. It is argued that while the advancing automatic tools
for ontology quality assurance are crucial for ontology improvement, they will
not solve the problem entirely. It is ontology reuse that is the ultimate
method for continuously verifying and improving ontology quality, as well as
for guiding its future development. Specifically, multiple issues can be found
and fixed primarily through practical and diverse ontology reuse scenarios.Comment: Accepted into SOMET 2022 conferenc
Introducing Federated Learning into Internet of Things ecosystems -- preliminary considerations
Federated learning (FL) was proposed to facilitate the training of models in
a distributed environment. It supports the protection of (local) data privacy
and uses local resources for model training. Until now, the majority of
research has been devoted to "core issues", such as adaptation of machine
learning algorithms to FL, data privacy protection, or dealing with the effects
of uneven data distribution between clients. This contribution is anchored in a
practical use case, where FL is to be actually deployed within an Internet of
Things ecosystem. Hence, somewhat different issues that need to be considered,
beyond popular considerations found in the literature, are identified.
Moreover, an architecture that enables the building of flexible, and adaptable,
FL solutions is introduced.Comment: Conference IEEE 8th World Forum on Internet of Things submissio