We investigate the differentiation of cold and heat syndromes in Traditional Chinese Medicine with a special concern on the issue of data imbalance. Data imbalance occurs frequently in syndrome differentiation. In this study, we use a neural network classifier, fastText, to differentiate cold and heat syndromes, which have skewed distributions in the medical records and in the population. We investigate several sampling techniques to tackle the issued of data imbalance, including oversampling, under-sampling, and bagging. We further set thresholds of probabilities of the classifiers based on an observation on precision recall curves. The performances are evaluated using macro-averaged F1 score. It is disclosed the classifier we used, fastText, is insensitive to imbalanced data.

In recent years, people pay more and more attention to the knowledge of health care and longevity. As an important branch of the Traditional Chinese Medicine (TCM), health preservation is a subject for studying the theories and methods to prevent diseases and to maintain personal health. We aim to provide the general public as well as the TCM practitioners and students with a convenient way to access the knowledge in this area. Information technologies such as knowledge base, information retrieval, and World Wide Web were used for knowledge management, knowledge engineering, and knowledge sharing. We developed a comprehensive knowledge base for the systemic organization of TCM health preservation knowledge, and established an Internet platform that makes the knowledge base available for TCM practitioners as well as ordinary people. This system implements various functions such as knowledge retrieval, knowledge navigation, and graphical browsing. This system provides an ideal platform for the sharing and dissemination of TCM knowledge about health preservation. It increases the availability of the literature and knowledge base in this field and promotes the inheritance and modernization of this cultural heritage.

This research proposed a TCM FS Ontology using FS-associated information retrieved from ancient Chinese medical texts. To standardize the process, we rigorously followed the 7-steps-approach proposed by Standard University. Besides, the establishing of axioms constraining the relationships between concepts may provide reference for the construction of knowledge base in future studies.

Integrative Biology (IB) uses experimental or computational quantitative technologies to characterize biological systems at the molecular, cellular, tissue and population levels. IB typically involves the integration of the data, knowledge and capabilities across disciplinary boundaries in order to solve complex problems. We identify a series of bioinformatics problems posed by interdisciplinary integration: (i) data integration that interconnects structured data across related biomedical domains; (ii) ontology integration that brings jargons, terminologies and taxonomies from various disciplines into a unified network of ontologies; (iii) knowledge integration that integrates disparate knowledge elements from multiple sources; (iv) service integration that build applications out of services provided by different vendors. We argue that IB can benefit significantly from the integration solutions enabled by Semantic Web (SW) technologies. The SW enables scientists to share content beyond the boundaries of applications and websites, resulting into a web of data that is meaningful and understandable to any computers. In this review, we provide insight into how SW technologies can be used to build open, standardized and interoperable solutions for interdisciplinary integration on a global basis. We present a rich set of case studies in system biology, integrative neuroscience, bio-pharmaceutics and translational medicine, to highlight the technical features and benefits of SW applications in IB.

Linked data is a decentralized space of interlinked Resource Description Framework (RDF) graphs that are published, accessed, and manipulated by a multitude of Web agents. Here, we present a multi-agent framework for mining hypothetical semantic relations from linked data, in which the discovery, management, and validation of relations can be carried out independently by different agents. These agents collaborate in relation mining by publishing and exchanging inter-dependent knowledge elements, e.g., hypotheses, evidence, and proofs, giving rise to an evidentiary network that connects and ranks diverse knowledge elements. Simulation results show that the framework is scalable in a multi-agent environment. Real-world applications show that the framework is suitable for interdisciplinary and collaborative relation discovery tasks in social domains.

The Semantic Web technology enables integration of heterogeneous data on the World Wide Web by making the semantics of data explicit through formal ontologies. In this article, we survey the feasibility and state of the art of utilizing the Semantic Web technology to represent, integrate and analyze the knowledge in various biomedical networks. We introduce a new conceptual framework, semantic graph mining, to enable researchers to integrate graph mining with ontology reasoning in network data analysis. Through four case studies, we demonstrate how semantic graph mining can be applied to the analysis of disease-causal genes, Gene Ontology category cross-talks, drug efficacy analysis and herb-drug interactions analysis.

Previous trust mechanisms are mainly focused on reputational models based on explicit trust ratings. However, the large amount of system-provided and user-generated content published on Web is often ignored. These information is very important for providing more accurate, fine-grained and efficient trust management. In this paper, we propose a content-based trust mechanism and introduce trust reasoning functionalities which can infer a user’s interests and expertise. We present a new method to calculate user similarity based on their feedbacks on reviews to reduce data sparseness of User Similarity matrix. We pinpoint several characteristics of trust which have been overlooked previously: trust is field-dependant, similarity-sensitive, personalized and etc. we differ various trust levels between pairs of users to support more accurate and efficient trust calculation. Although we choose Epinions as our targeted application, the mechanism proposed in this paper are also applicable to other e-commerce systems.

Nowadays, social networks play an important role in our lives, in which information and knowledge are exchanged, shared and transformed. With time, large volumes of real-world data have been accumulated capturing diversified application domains. However, heterogeneity and incompleteness of data make social networks perform as 'data isolated islands' separated to each other. In this paper, we propose a generic approach that consists of an ontology-based social network integration approach and a statistic learning method towards the Semantic Web data. In particular, an extended FOAF (Friend-Of-A-Friend) ontology is used as the mediation schema to integrate social networks and a hybrid entity reconciliation method is used to resolve entities of different data sources. We also present an analyzing approach that learns a probabilistic semantic model (PSM) from social data for relationship identification. Empirical results prove that, compared with single numeric or logical methods respectively, our hybrid reconciliation method has obvious improvements on precision and recall during combining LinkedIn.com and DBLP. In addition, PSM framework can reserve richer semantics of semantic data completely during data analysis.

We conduct the first systematical adoption of the Semantic Web solution in the integration, management, and utilization of TCM information and knowledge resources. As the results, the largest TCM Semantic Web ontology is engineered as the uniform knowledge representation mechanism; the ontology-based query and search engine is deployed, mapping legacy and heterogeneous relational databases to the Semantic Web layer for query and search across database boundaries; the first global herb-drug interaction network is mapped through semantic integration, and the semantic graph mining methodology is implemented for discovering and interpreting interesting patterns from this network. The platform and underlying methodology are proved effective in TCM-related drug usage, discovery, and safety analysis.