Utilizing Knowledge Graphs for Text-Centric Information Retrieval

Dietz, Laura; Kotov, Alexander; Meij, Edgar

doi:10.1145/3209978.3210187

Cited by 62 publications

(18 citation statements)

References 43 publications

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“…Their semantic structure was used alongside text mining in order to enrich a graph with extracted textual data. The result of this combination is also addressed by Dietz et al (2018), who highlighted in their summary on the utilization of knowledge graphs with text mining, the rising role of knowledge graphs in text-centric retrieval, especially for search system applications [64]. This further highlights the interaction between knowledge graphs and natural language processing (NLP).…”

Section: Semantic Knowledge Representation In Smart Factoriesmentioning

confidence: 95%

Knowledge Integration in Smart Factories

et al. 2021

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Knowledge integration is well explained by the human–organization–technology (HOT) approach known from knowledge management. This approach contains the horizontal and vertical interaction and communication between employees, human-to-machine, but also machine-to-machine. Different organizational structures and processes are supported with the help of appropriate technologies and suitable data processing and integration techniques. In a Smart Factory, manufacturing systems act largely autonomously on the basis of continuously collected data. The technical design concerns the networking of machines, their connectivity and the interaction between human and machine as well as machine-to-machine. Within a Smart Factory, machines can be considered as intelligent manufacturing systems. Such manufacturing systems can autonomously adapt to events through the ability to intelligently analyze data and act as adaptive manufacturing systems that consider changes in production, the supply chain and customer requirements. Inter-connected physical devices, sensors, actuators, and controllers form the building block of the Smart Factory, which is called the Internet of Things (IoT). IoT uses different data processing solutions, such as cloud computing, fog computing, or edge computing, to fuse and process data. This is accomplished in an integrated and cross-device manner.

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Section: Semantic Knowledge Representation In Smart Factoriesmentioning

confidence: 95%

Knowledge Integration in Smart Factories

et al. 2021

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“…During recent years, Knowledge Graph Embedding (KGE) has prevailed in the field of huge structured knowledge interlinking [22], and its effectiveness has been shown in many different scenarios such as search engine [23][24][25], recommendation system [26][27][28][29][30][31][32], question answering [33][34][35], video understanding [36], conversational AI [37,38] and explainable AI [25,31,39].…”

Section: Related Workmentioning

confidence: 99%

Efficient Non-Sampling Knowledge Graph Embedding

et al. 2021

Proceedings of the Web Conference 2021

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Knowledge Graph (KG) is a flexible structure that is able to describe the complex relationship between data entities. Currently, most KG embedding models are trained based on negative sampling, i.e., the model aims to maximize some similarity of the connected entities in the KG, while minimizing the similarity of the sampled disconnected entities. Negative sampling helps to reduce the time complexity of model learning by only considering a subset of negative instances, which may fail to deliver stable model performance due to the uncertainty in the sampling procedure. To avoid such deficiency, we propose a new framework for KG embedding-Efficient Non-Sampling Knowledge Graph Embedding (NS-KGE). The basic idea is to consider all of the negative instances in the KG for model learning, and thus to avoid negative sampling. The framework can be applied to square-loss based knowledge graph embedding models or models whose loss can be converted to a square loss. A natural side-effect of this non-sampling strategy is the increased computational complexity of model learning. To solve the problem, we leverage mathematical derivations to reduce the complexity of non-sampling loss function, which eventually provides us both better efficiency and better accuracy in KG embedding compared with existing models. Experiments on benchmark datasets show that our NS-KGE framework can achieve a better performance on efficiency and accuracy over traditional negative sampling based models, and that the framework is applicable to a large class of knowledge graph embedding models. CCS CONCEPTS• Computing methodologies → Knowledge representation and reasoning; Learning latent representations; Neural networks.

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“…An example is the work of Subasic et al [8], where the solution to adapt the knowledge graph to the domain terminology was to build the graph on two levels: a static level, corresponding to predefined databases, and a dynamic level, that corresponds to the special terminology. Knowledge graphs have emerged as a graphical representation of a knowledge base; and got integrated with multiple technologies and applications [9]. Among the tasks that knowledge graphs have been involved in, enhancing search and matching is a promising field, especially with the advances in search engines and semantic web.…”

Section: Background and Related Workmentioning

confidence: 99%

Explainable Graph-Based Search for Lessons-Learned Documents in the Semiconductor Industry

Abu-Rasheed

Weber

Zenkert

et al. 2021

Lecture Notes in Networks and Systems

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Industrial processes produce a considerable volume of data and thus information. Whether it is structured sensory data or semi-to unstructured textual data, the knowledge that can be derived from it is critical to the sustainable development of the industrial process. A key challenge of this sustainability is the intelligent management of the generated data, as well as the knowledge extracted from it, in order to utilize this knowledge for improving future procedures. This challenge is a result of the tailored documentation methods and domain-specific requirements, which include the need for quick visibility of the documented knowledge. In this paper, we utilize the expert knowledge documented in chip-design failure reports in supporting user access to information that is relevant to a current chip design. Unstructured, free, textual data in previous failure documentations provides a valuable source of lessons-learned, which expert designengineers have experienced, solved and documented. To achieve a sustainable utilization of knowledge within the company, not only the inherent knowledge has to be mined from unstructured textual data, but also the relations between the lessons-learned, uncovering potentially unknown links. In this research, a knowledge graph is constructed, in order to represent and use the interconnections between reported design failures. A search engine is developed and applied onto the graph to answer queries. In contrast to mere keyword-based searching, the searchability of the knowledge graph offers enhanced search results beyond direct matches and acts as a mean for generating explainable results and result recommendations. Results are provided to the design engineer through an interactive search interface, in which, the feedback from the user is used to further optimize relations for future iterations of the knowledge graph.

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Utilizing Knowledge Graphs for Text-Centric Information Retrieval

Cited by 62 publications

References 43 publications

Knowledge Integration in Smart Factories

Knowledge Integration in Smart Factories

Efficient Non-Sampling Knowledge Graph Embedding

Explainable Graph-Based Search for Lessons-Learned Documents in the Semiconductor Industry

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