Case-Based Sample Generation using Multi-Armed Bandits
A central problem in knowledge-based tasks is to provide a collection of reusable knowledge samples extracted from a textual corpus. Often, such corpora are structured into different documents or topics, respectively. The samples need to be proven for usability and adapted by a domain expert requiring a certain processing time for each sample taken. The goal is to achieve an optimal retrieval and adaptation success meeting the time budget of the domain expert. In this work, we formulate this task as a constrained multi-armed bandit model. We combine it with the model of a configurable data-driven case-based learning agent. A case study evaluates the theoretical considerations in a scenario of regulatory knowledge acquisition. Therefore, a data set is constructed out of a corpus of nuclear safety documents. We use the model to optimize the evaluation process of sample generation of adaptational knowledge. The corresponding knowledge graph has been created in an information extraction step by automatically identifying semantic concepts and their relations.
ICCBR International Conference on Case-Based Reasoning Aberdeen, 2023
An Active Annotation Support System for Regulatory Documents
Manual document annotation is a resource intense task. The costs of annotation can be lowered by supporting the manual annotation with pre-processing of the available corpus and active in-process support of annotating users. To integrate different components into a coherent active annotation support system the XML Metadata Interchange standard can be used to exchange objects on the base of a metameta data model. Further, to integrate an existing knowledge graph into an annotation support system the RDF query language SPARQL can be used as an interface to analyze existent documents and declare new knowledge. In this manner the presented efforts contribute to structure and standardize the process of manual knowledge acquisition from regulatory documents.
LWDA Learning Knowledge Data Analysis Hildesheim, 2022
A Feasibility Study for the Semantification of Cyclic Compliance Knowledge Transfer between Multinational Organizations
In a variety of scenarios the transfer of knowledge between different organizations is aspired. There exist theoretical concepts that describe cyclic processes of knowledge management and knowledge transfer. In this work a conceptual symbiosis of both is presented. A semantic description of the knowledge transfer
process is suggested extending accepted standards. A strategic concept of identifying characteristics to assess the chances of a successful knowledge exchange is presented. Theoretical concepts are undermined by a case study about the transfer of compliance knowledge between two international organizations for the safe and compliant handling of employees dogs at the workplace with the intention of improving the overall working atmosphere.
LWDA Learning Knowledge Data Analysis Hildesheim, 2022
Rule-based Semantic Relation Extraction in Regulatory Documents
Regulatory documents are present in many domains of daily living, technical, business, and political context. The knowledge underlying such documents is most often structured using semantic concepts from narrow to broad scope. Those concepts are immanent to the text making up a document. Narrow semantic concepts are described by some words or sentences. Semantic concepts of a broader sense are more complex in their textual representation. This work gives examples of textual characteristics of semantic concepts in the domain of nuclear safety, and that of public events. It shows a rule-based approach for the handling of these concepts and extracting the relations between them.
LWDA Learning Knowledge Data Analysis Munich (virtually), 2021
Construction of a Corpus for the Evaluation of Textual Case-based Reasoning Architectures
Regulatory documents denote an interesting application domain for case-based knowledge management. These documents enumerate
situations with conditions, that are often dangerous for human and environment and they give advice, rules, and instructions for prevention
or handling. That type of documents is eminent in many domains and provides valuable experience knowledge which makes it a remarkable application
and research domain for (textual) case-based reasoning. In this paper, an initial case-based representation of regulatory documents is introduced.
We report on the construction of an open corpus of regulatory documents in the domain of nuclear safety regulations.
LWDA Learning Knowledge Data Analysis Bonn (virtually), 2020
Case-Based Generation of Regulatory Documents and their Semantic Relatedness
Regulatory documents are required or provided by authorities in many domains. They commonly point out relevant incidents for specic scenarios. For those they have to present suitable preventive and reactive measures. We introduce an approach to connect a case-based description of the incidents structure with a pre-calculated word embed ding and describe how to adapt this word embedding to dierent context. This paper shows how to use case-based methods to retrieve, adapt, and reuse incidents descriptions. Subsequently they are used to generate new regulatory documents via case-based reasoning.
FICC Future of Information and Communication Conference San Francisco, 2020
Case-Based Retrieval and Adaptation of Regulatory Documents and their Context
Regulatory documents are required or provided by authorities in many domains. They commonly point out relevant incidents for specic scenarios. For those they have to present suitable preventive and reactive measures. We introduce an approach to connect a case-based description of the incidents structure with a case- based description of the according context. This paper shows how to use case-based methods to retrieve, adapt, and reuse incidents descriptions. Subsequently they are used to generate new regulatory documents via case-based reasoning. Case-based reasoning Experience Management Knowledge Management SKOS Semantic Relatedness Natural Language Generation.
LWDA Learning Knowledge Data Analysis Berlin, 2019
Textual Case-based Adaptation using Semantic Relatedness – A Case Study in the Domain of Security Documents
In previous eorts graph-based and textual knowledge representations were combined for the usage in case-based reasoning. This work proposes rst steps for this combination in the domain of security documents and similar document classes. We present an approach pre-processing documents for textual case-based reasoning by adapting methods of natural language processing.We propose a method improving
a case-based hierarchical similarity assessment for retrieval by introducing
the concept of vector space embeddings and semantic relatedness of
words and phrases.
KM Knowledge Management Conference Potsdam, 2019
The SECCO Ontology for the Retrieval and Generation of Security Concepts
Due to the development of the global security situation, the existence and implementation of security concepts became an important aspect of public events. The denition and writing of a security concept demands domain knowledge and experience. This paper describes an approach for the automated retrieval and generation of security concept templates based on reliable examples. We use ontologies for the conceptualization of textual security concepts, and we employ case-based reasoning for the retrieval and generation of new security concepts.
ICCBR International Conference on Case-based Reasoning Stockholm, 2018