Event based text mining for integrated network construction

The scientific literature is a rich and challenging data source for research in systems biology, providing numerous interactions between biological entities. Text mining techniques have been increasingly useful to extract such information from the literature in an automatic way, but up to now the main focus of text mining in the systems biology field has been restricted mostly to the discovery of protein-protein interactions. Here, we take this approach one step further, and use machine learning techniques combined with text mining to extract a much wider variety of interactions between biological entities. Each particular interaction type gives rise to a separate network, represented as a graph, all of which can be subsequently combined to yield a so-called integrated network representation. This provides a much broader view on the biological system as a whole, which can then be used in further investigations to analyse specific properties of the networ

Detecting and Explaining Causes From Text For a Time Series Event

Explaining underlying causes or effects about events is a challenging but valuable task. We define a novel problem of generating explanations of a time series event by (1) searching cause and effect relationships of the time series with textual data and (2) constructing a connecting chain between them to generate an explanation. To detect causal features from text, we propose a novel method based on the Granger causality of time series between features extracted from text such as N-grams, topics, sentiments, and their composition. The generation of the sequence of causal entities requires a commonsense causative knowledge base with efficient reasoning. To ensure good interpretability and appropriate lexical usage we combine symbolic and neural representations, using a neural reasoning algorithm trained on commonsense causal tuples to predict the next cause step. Our quantitative and human analysis show empirical evidence that our method successfully extracts meaningful causality relationships between time series with textual features and generates appropriate explanation between them.Comment: Accepted at EMNLP 201

Community Detection and Growth Potential Prediction from Patent Citation Networks

The scoring of patents is useful for technology management analysis. Therefore, a necessity of developing citation network clustering and prediction of future citations for practical patent scoring arises. In this paper, we propose a community detection method using the Node2vec. And in order to analyze growth potential we compare three ''time series analysis methods'', the Long Short-Term Memory (LSTM), ARIMA model, and Hawkes Process. The results of our experiments, we could find common technical points from those clusters by Node2vec. Furthermore, we found that the prediction accuracy of the ARIMA model was higher than that of other models.Comment: arXiv admin note: text overlap with arXiv:1607.00653 by other author

ASPIE: A Framework for Active Sensing and Processing of Complex Events in the Internet of Manufacturing Things

Rapid perception and processing of critical monitoring events are essential to ensure healthy operation of Internet of Manufacturing Things (IoMT)-based manufacturing processes. In this paper, we proposed a framework (active sensing and processing architecture (ASPIE)) for active sensing and processing of critical events in IoMT-based manufacturing based on the characteristics of IoMT architecture as well as its perception model. A relation model of complex events in manufacturing processes, together with related operators and unified XML-based semantic definitions, are developed to effectively process the complex event big data. A template based processing method for complex events is further introduced to conduct complex event matching using the Apriori frequent item mining algorithm. To evaluate the proposed models and methods, we developed a software platform based on ASPIE for a local chili sauce manufacturing company, which demonstrated the feasibility and effectiveness of the proposed methods for active perception and processing of complex events in IoMT-based manufacturing

On the Evolution of Knowledge Graphs: A Survey and Perspective

Knowledge graphs (KGs) are structured representations of diversified knowledge. They are widely used in various intelligent applications. In this article, we provide a comprehensive survey on the evolution of various types of knowledge graphs (i.e., static KGs, dynamic KGs, temporal KGs, and event KGs) and techniques for knowledge extraction and reasoning. Furthermore, we introduce the practical applications of different types of KGs, including a case study in financial analysis. Finally, we propose our perspective on the future directions of knowledge engineering, including the potential of combining the power of knowledge graphs and large language models (LLMs), and the evolution of knowledge extraction, reasoning, and representation