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Showing papers presented at "International Joint Conference on Knowledge Discovery, Knowledge Engineering and Knowledge Management in 2018"


Proceedings ArticleDOI
01 Jan 2018

45 citations



Proceedings Article
04 Aug 2018
TL;DR: This study uses intensive interviewing to explore critical success factors of technology parks and identified a culture of risk-taking “entrepreneurism”, an autonomous park management, an enabling environment, a critical mass of companies, the presence of internationally renowned innovative companies, and finally a shared vision among the technology park stakeholders.
Abstract: Given the potential importance of technology parks, their complexity in terms of the scope of required investment and the growing interest of governments to use them as tools for creating sustainable development there is a pressing need for a better understanding of the critical success factors of these entities. However, Briggs and watt (2001) argued that the goal of many technology parks and the factors driving innovation success are still a mystery. It is also argued that the problem with analyzing technology parks is that recent studies analyze “the most celebrated case studies… to ‘explain’ their success”. This study uses intensive interviewing to explore critical success factors of technology parks. The study identified the following factors: a culture of risk-taking “entrepreneurism”, an autonomous park management, an enabling environment, a critical mass of companies, the presence of internationally renowned innovative companies, and finally a shared vision among the technology park stakeholders.

18 citations


Proceedings ArticleDOI
01 Jan 2018
TL;DR: This work shows that MemDNN based classifiers improve the state-of-the-art on Amazon Reviews corpus with reference to document-level cross-domain sentiment classification, and DNC outperforms previous approaches in the analysis of a very large in-domain configuration in both binary and fine-grained document sentiment classification.
Abstract: Cross-domain sentiment classifiers aim to predict the polarity, namely the sentiment orientation of target text documents, by reusing a knowledge model learned from a different source domain. Distinct domains are typically heterogeneous in language, so that transfer learning techniques are advisable to support knowledge transfer from source to target. Distributed word representations are able to capture hidden word relationships without supervision, even across domains. Deep neural networks with memory (MemDNN) have recently achieved the state-of-the-art performance in several NLP tasks, including cross-domain sentiment classification of large-scale data. The contribution of this work is the massive experimentations of novel outstanding MemDNN architectures, such as Gated Recurrent Unit (GRU) and Differentiable Neural Computer (DNC) both in cross-domain and in-domain sentiment classification by using the GloVe word embeddings. As far as we know, only GRU neural networks have been applied in cross-domain sentiment classification. Sentiment classifiers based on these deep learning architectures are also assessed from the viewpoint of scalability and accuracy by gradually increasing the training set size, and showing also the effect of fine-tuning, an explicit transfer learning mechanism, on cross-domain tasks. This work shows that MemDNN based classifiers improve the state-of-the-art on Amazon Reviews corpus with reference to document-level cross-domain sentiment classification. On the same corpus, DNC outperforms previous approaches in the analysis of a very large in-domain configuration in both binary and fine-grained document sentiment classification. Finally, DNC achieves accuracy comparable with the state-of-the-art approaches on the Stanford Sentiment Treebank dataset in both binary and fine-grained single-sentence sentiment classification.

17 citations




Proceedings ArticleDOI
18 Sep 2018
TL;DR: This work proposes an approach to generate automatically use cases of autonomous vehicle for highway based on a three layers hierarchy, which exploits static and mobile concepts defined in the context of three ontologies: highway, weather and vehicle.
Abstract: Autonomous vehicles perceive the environment with different kinds of sensors (camera, radar, lidar...). They must evolve in an unpredictable environment and a wide context of dynamic execution, with strong interactions. In order to generate the safety of the autonomous vehicle, its occupants and the others road users, it is necessary to validate the decisions of the algorithms for all the situations that will be met. These situations are described and generated as different use cases of automated vehicles. In this work, we propose an approach to generate automatically use cases of autonomous vehicle for highway. This approach is based on a three layers hierarchy, which exploits static and mobile concepts we have defined in the context of three ontologies: highway, weather and vehicle. The highway ontology and the weather ontology conceptualize the environment in which evolves the autonomous vehicle, and the vehicle ontology consists of the vehicle devices and the control actions. To apply our approach, we consider a running example about the insertion of a vehicle by the right entrance lane of a highway.

14 citations



Proceedings Article
14 Aug 2018
TL;DR: An extended data warehouse approach is defined that integrates process-related data and operational business data that is used as the underlying data source for extended OLAP and data mining analysis techniques for a comprehensive business process optimization.
Abstract: Efficient adaption of a company’s business and its business processes to a changing environment is a crucial ability to survive in today’s dynamic world. For optimizing business processes, a profound analysis of all relevant business data in the company is necessary. We define an extended data warehouse approach that integrates process-related data and operational business data. This extended data warehouse is used as the underlying data source for extended OLAP and data mining analysis techniques for a comprehensive business process optimization.

11 citations


Proceedings ArticleDOI
01 Jan 2018

10 citations


Proceedings Article
28 Jul 2018
TL;DR: An approach for facilitating the authoring process for Technology Enhanced Learning by providing service oriented access to learning materials based on their content is discussed, based on Semantic Web Service technologies.
Abstract: The paper discusses an approach for facilitating the authoring process for Technology Enhanced Learning by providing service oriented access to learning materials based on their content. This service-oriented approach is based on Semantic Web Service technologies. It reflects current work in the frame of an ongoing Bulgarian research project SINUS “Semantic Technologies for Web Services and Technology




Proceedings ArticleDOI
20 Sep 2018
TL;DR: The proposed method is an ontological, developer-centred approach aiding a software developer in decision making and interoperable information sharing through the use of the ODYSSEY ontology the authors developed for the software development life cycle (SDLC) domain.
Abstract: With the omnipresence of softwares in our Society from Information Technology (IT) services to autonomous agents, their systematic and efficient development is crucial for software developers. Hence, in this paper, we present an approach to assist intelligent agents (IA), whatever human beings or artificial systems, in theirs task to develop and configure softwares. The proposed method is an ontological, developer-centred approach aiding a software developer in decision making and interoperable information sharing through the use of the ODYSSEY ontology we developed for the software development life cycle (SDLC) domain. This ODYSSEY ontology has been designed following the Enterprise Ontology (EO) methodology and coded in Descriptive Logic (DL). Its implementation in OWL has been evaluated for case studies, showing promising results.

Proceedings Article
06 Aug 2018
TL;DR: It is argued that there are logical relationships between the fields of Knowledge Management and Records Management, and the recognition of such relationships will benefit the development of both fields.
Abstract: This paper argues that there are logical relationships between the fields of Knowledge Management and Records Management, and the recognition of such relationships will benefit the development of both fields. It bases these arguments on the nature of records and Records Management as well as the findings of the


Proceedings ArticleDOI
23 Jun 2018
TL;DR: This paper presents the Organizational learning and its specifications in SMEs, and presents TOVE and Enterprise projects from which a semantic model specially dedicated to SMEs is defined from which the choice of the MEMORAe organizational memory platform is explained.
Abstract: Many efforts have been made in the last two decades to manage knowledge in organizations, especially tacit knowledge which is difficult to transfer to others as contrary to explicit knowledge. Organizational learning plays a great role in capitalizing such expertise in organizations. Large enterprises can spend high budgets on the organizational learning process which is not the case in Small and Medium-sized Enterprises (SMEs) where organizational learning is not supported due to the missing of standardized and codified technical supports. So there is a special need for SMEs to organize their knowledge and to facilitate the access of information. In this paper, we present the Organizational learning and its specifications in SMEs. We also present TOVE and Enterprise projects from which we defined a semantic model specially dedicated to SMEs. We explain the choice of the MEMORAe organizational memory platform to manage knowledge in SMEs.



Proceedings Article
18 Jul 2018
TL;DR: In the long-term research, a new management and leadership methodology is created, which can be used for strategic management purposes to manage change and to lead the company resources efficiently and effectively towards the new future.
Abstract: In our long-term research we have created a new management and leadership methodology, which can be used for strategic management purposes to manage change and to lead the company resources efficiently and effectively towards the new future. Our research methods are based on management and leadership ontologies, with which we can capture the current and future views of personnel for use in strategy making and strategic management. The evidence we have obtained originates from our research with our ontologybased research instruments and test runs with fuzzy logic based computer applications.


Proceedings ArticleDOI
01 Jan 2018
TL;DR: This paper introduces an approach to reason over contextual knowledge in RDF, while committing to the semantics of a contextual description logic, and defines an OWLC profile for contextual reasoning, similar to OWL 2 RL.
Abstract: Dealing with context-sensitive information is a crucial aspect in the management of semantic web data. Despite the importance of this topic, there is so far no accepted consensus regarding the precise way of encoding and even more reasoning on contextual knowledge. In this paper, we introduce an approach to reason over contextual knowledge in RDF, while committing to the semantics of a contextual description logic. The lines of this paper are many folds. First, we present an extension of OWL 2 DL for contexts, that we call {OWL 2 DLC. It is a two-dimensional web ontology language with one dimension for contextualized object knowledge and one dimension for contexts. Second, we define an OWLC profile for contextual reasoning, similar to OWL 2 RL. And finally, we demonstrate that the model can be practically implemented using existing semantic web technologies, especially using SPIN rules.


Proceedings Article
25 Aug 2018
TL;DR: In this paper, a methodology for building an integrated model for information and knowledge management, based on the identification of strategic information assets (IA), is presented and discussed, which includes the collection of best practices and benchmarks and the analysis of internal documentation, including the organization mission and vision statements.
Abstract: This paper aims to present and discuss a methodology for building an integrated model for information and knowledge management, based on the identification of strategic information assets (IA). It begins with the collection of best practices and benchmarks and the analysis of internal documentation, including the organization mission and vision statements, to then identify strategic information assets that give business support. During field visits and structured interviews for collecting information, information assets are analyzed and partial formal models are then produced. Those models are gradually consolidated into an integrated model. The adopted modelling techniques include: definition of business requirements; development of business use cases and an information model; the representation of information flows; and the identification of knowledge, skills and professionals. The ontology model is used to clarify concepts definitions in this domain. The methodology includes current situation analysis (as is), identification of gaps and the proposal of improvements, which are all reflected in the desired situation models (to be). Project innovation lies, mainly, in the use of IAs and the combination of complex elements to build a unique model that integrates information management and knowledge management components in the same framework.

Proceedings ArticleDOI
01 Jan 2018
TL;DR: A new deflationistic interpretation of the essential account of ontological dependence is proposed to derive a new largescale domains hierarchy from the Wikipedia category system, and it is going to be used to provide BabelNet and DBpedia with fine-grained domain annotations.
Abstract: We analyse violations of the transitivity principle of the Wikipedia category system, i.e. the situations where articles from a subcategory doesn’t logically belong to its parent category. The causes of the violation have been analysed on the base of ontological modelling methodologies such as OntoClean. We propose a new approach to automatically eliminating the violations. This approach is based on analysis of the relation of ontological dependence between categories. As a theoretical foundation of such analysis we propose a new deflationistic interpretation of the essential account of ontological dependence. The proof of concept has been evaluated on the category C:Mathematics. We are going to apply the proposed approach to derive a new largescale domains hierarchy from the Wikipedia category system, and use it to provide BabelNet and DBpedia with fine-grained domain annotations.