A Semantic Collaboration Method Based on Uniform Knowledge Graph

The Semantic Internet of Things is the extension of the Internet of Things and the Semantic Web, which aims to build an interoperable collaborative system to solve the heterogeneous problems in the Internet of Things. However, the Semantic Internet of Things has the characteristics of both the Internet of Things and the Semantic Web environment, and the corresponding semantic data presents many new data features. In this study, we analyze the characteristics of semantic data and propose the concept of a uniform knowledge graph, allowing us to be applied to the environment of the Semantic Internet of Things better. Here, we design a semantic collaboration method based on a uniform knowledge graph. It can take the uniform knowledge graph as the form of knowledge organization and representation, and provide a useful data basis for semantic collaboration by constructing semantic links to complete semantic relation between different data sets, to achieve the semantic collaboration in the Semantic Internet of Things. Our experiments show that the proposed method can analyze and understand the semantics of user requirements better and provide more satisfactory outcomes

Large language models converge toward human-like concept organization

Large language models show human-like performance in knowledge extraction, reasoning and dialogue, but it remains controversial whether this performance is best explained by memorization and pattern matching, or whether it reflects human-like inferential semantics and world knowledge. Knowledge bases such as WikiData provide large-scale, high-quality representations of inferential semantics and world knowledge. We show that large language models learn to organize concepts in ways that are strikingly similar to how concepts are organized in such knowledge bases. Knowledge bases model collective, institutional knowledge, and large language models seem to induce such knowledge from raw text. We show that bigger and better models exhibit more human-like concept organization, across four families of language models and three knowledge graph embeddings

Pharmaceuticals and life sciences: Role of competitive intelligence in innovation

This chapter goal is to analyze the concepts of innovation, knowledge and competitive intelligence (CI). Besides these concepts, the focus will be on the role of innovation profiles defined by Sousa (2009, 2013). The innovation profiles include the creation, capture, organization, and integration of knowledge into the innovation process. The CI variable will be analyzed demonstrating the potential for creating a context of competition for companies. A case study is presented about the pharmaceutical (pharma) industry with the application of the concepts of competitive intelligence, knowledge, and innovation to a real context. Keywords: Competitive Intelligence, Innovation, Organization, Knowledge, Knowledge Profiles, Pharma industry.info:eu-repo/semantics/acceptedVersio

A Two-Tiered Approach for Organizing Slots in Large, Frame-Structured Knowledge Bases

Slots represent semantic relations and play a major role in frame-based representation systems; they not only act as an "instruction set" for knowledge entry but also support most forms of nterferencing. Thus, the organization of slots merits a systematic study in its own righL A taxonomic approach formalizes the organization of the slots and provides a principled intelpretation for their semantics. We discuss the organization of slots from three different perspectives---relation-element, slot-use, and slot-argument-and propose that all three are useful in providing interpretations for slots. We argue that each individual view, by itself, does not offer sufficient semantics for slot organization. On the other hand, forcing all perspectives together destroys the clarity of a principled taxonomy. Therefore, we propose using two taxonomic views: A first taxonomy for slots is based on the relation-element and slot-use views; this taxonomy is domain-independent and promotes knowledge reuse. A second taxonomy, based on a slot-argument view, is domain- dependent and parallels the nonslot taxonomy

An ontological approach for recovering legacy business content

Legacy Information Systems (LIS) pose a challenge for many organizations. On one hand, LIS are viewed as aging systems needing replacement; on the other hand, years of accumulated business knowledge have made these systems mission-critical. Current approaches however are often criticized for being overtly dependent on technology and ignoring the business knowledge which resides within LIS. In this light, this paper proposes a means of capturing the business knowledge in a technology agnostic manner and transforming it in a way that reaps the benefits of clear semantic expression - this transformation is achieved via the careful use of ontology. The approach called Content Sophistication (CS) aims to provide a model of the business that more closely adheres to the semantics and relationships of objects existing in the real world. The approach is illustrated via an example taken from a case study concerning the renovation of a large financial system and the outcome of the approach results in technology agnostic models that show improvements along several dimensions

Knowledge management maturity contributes to project-based companies in an open innovation era

Knowledge is a crucial asset for any organization nowadays. Despite the temporary nature of projects, proper knowledge management can improve outcomes and benefit future endeavors. However, an effective knowledge management system has to be tailored to each organization. Therefore, it is extremely important to assess the stage of knowledge management (KM) maturity of an organization. The present study analyzed the general maturity level of European project-based organizations. The analysis was performed to understand the maturity of the knowledge management cycle’s phases and it was possible to distinguish which phases were more developed and what are the main steps to create effective organizational learning in a project-based organization. Overall, European-based project-oriented organizations are halfway through the implementation of KM systems. While the need for a proper system has already been acknowledged, the infrastructure to support it still needs to be developed so proper measures can be put in practice.info:eu-repo/semantics/publishedVersio

Expressing the tacit knowledge of a digital library system as linked data

Library organizations have enthusiastically undertaken semantic web initiatives and in particular the data publishing as linked data. Nevertheless, different surveys report the experimental nature of initiatives and the consumer difficulty in re-using data. These barriers are a hindrance for using linked datasets, as an infrastructure that enhances the library and related information services. This paper presents an approach for encoding, as a Linked Vocabulary, the "tacit" knowledge of the information system that manages the data source. The objective is the improvement of the interpretation process of the linked data meaning of published datasets. We analyzed a digital library system, as a case study, for prototyping the "semantic data management" method, where data and its knowledge are natively managed, taking into account the linked data pillars. The ultimate objective of the semantic data management is to curate the correct consumers' interpretation of data, and to facilitate the proper re-use. The prototype defines the ontological entities representing the knowledge, of the digital library system, that is not stored in the data source, nor in the existing ontologies related to the system's semantics. Thus we present the local ontology and its matching with existing ontologies, Preservation Metadata Implementation Strategies (PREMIS) and Metadata Objects Description Schema (MODS), and we discuss linked data triples prototyped from the legacy relational database, by using the local ontology. We show how the semantic data management, can deal with the inconsistency of system data, and we conclude that a specific change in the system developer mindset, it is necessary for extracting and "codifying" the tacit knowledge, which is necessary to improve the data interpretation process