Summary Patent is a very important and valuable type of scientific and technical big data. This paper presents how to mine patent text to obtain valuable information/knowledge from large‐scale candidates obtained from these patents based on massive patent texts. We firstly propose a patent term extraction method using co‐occurrence in the abstract and first‐claim sections of patent records. There are three steps: (1) we extract candidate strings according to our definition of a term; (2) we propose an assumption to verify whether a candidate string is a qualified term or not by using the co‐occurrence of terms in the abstract and first claim; and (3) we use term frequency–inverse document frequencyAUTHOR: TF‐IDF has been defined as “term frequency–inverse document frequency”. Please check if correct. or mutual information to rank and select candidate terms. Secondly, we propose a new method to obtain valuable long tail term from patents. To fulfill the purpose, (1) we firstly build long tail term–common term pair as candidate set; (2) then we evaluate each candidate pair's value; and finally, (3) to demonstrate our method, we give an example on our result. This study provides a new perspective in extracting terms from free texts of patent records and also proposes a new method to obtain valuable long term to aid information analysis with massive patent texts. Copyright © 2016 John Wiley & Sons, Ltd.

Abstract Knowing semantic links among resources is the basis of realizing machine intelligence over large‐scale resources. Discovering semantic links among resources with limited human interference is a challenge issue. This paper proposes an approach to automatically discovering and predicting semantic links in a document set based on a model of document semantic link network (SLN). The approach has the following advantages: it supports probabilistic relational reasoning; SLNs and the relevant rules automatically evolve; and, it can adapt to the update of the adopted techniques. The approach can support cyber space applications, such as documentation recommendation and relational queries, on large documents. Copyright © 2010 John Wiley & Sons, Ltd.

Abstract Network structure analysis plays an important role in characterizing complex systems. Different from previous network centrality measures, this article proposes the topological centrality measure reflecting the topological positions of nodes and edges as well as influence between nodes and edges in general network. Experiments on different networks show distinguished features of the topological centrality by comparing with the degree centrality, closeness centrality, betweenness centrality, information centrality, and PageRank. The topological centrality measure is then applied to discover communities and to construct the backbone network. Its characteristics and significance is further shown in e‐Science applications.

Abstract The semantic link network (SLN) is the extension of the hyperlink Web by attaching semantics to hyperlinks. It is an approach to construct a semantic overlay on Web resources. The specification of the semantics of the SLN model is an essential issue of SLN application. This paper proposes a modeling language for SLN consisting of an algebraic definition for SLN, a SLN metamodel and a Unified Modeling Language (UML) profile for SLN. The SLN metamodel specifies the primitives of the modeling language. The UML profile for SLN defines the specific syntax on SLN to make the modeling language understandable and usable. The development of the SLN builder implementing this language and the graphical SLN browser is introduced. This work is a part of the SLN model. Copyright © 2007 John Wiley & Sons, Ltd.