Enhancing Navigability:An Algorithm for Constructing Tag Trees

Purpose： This study introduces an algorithm to construct tag trees that can be used as a userfriendly navigation tool for knowledge sharing and retrieval by solving two issues of previous studies, i.e. semantic drift and structural skew.Design/methodology/approach： Inspired by the generality based methods, this study builds tag trees from a co-occurrence tag network and uses the h-degree as a node generality metric. The proposed algorithm is characterized by the following four features：（1） the ancestors should be more representative than the descendants,（2） the semantic meaning along the ancestor-descendant paths needs to be coherent,（3） the children of one parent are collectively exhaustive and mutually exclusive in describing their parent, and（4） tags are roughly evenly distributed to their upper-level parents to avoid structural skew. Findings： The proposed algorithm has been compared with a well-established solution Heymann Tag Tree（HTT）. The experimental results using a social tag dataset showed that the proposed algorithm with its default condition outperformed HTT in precision based on Open Directory Project（ODP） classification. It has been verified that h-degree can be applied as a better node generality metric compared with degree centrality.Research limitations： A thorough investigation into the evaluation methodology is needed, including user studies and a set of metrics for evaluating semantic coherence and navigation performance.Practical implications： The algorithm will benefit the use of digital resources by generating a flexible domain knowledge structure that is easy to navigate. It could be used to manage multiple resource collections even without social annotations since tags can be keywords created by authors or experts, as well as automatically extracted from text.Originality/value： Few previous studies paid attention to the issue of whether the tagging systems are easy to navigate for users. The contributions of this study are twofold：（1） an algorithm was developed to construct tag trees with consideration given to both semanticcoherence and structural balance and（2） the effectiveness of a node generality metric, h-degree, was investigated in a tag co-occurrence network.

Neural Attentional Relation Extraction with Dual Dependency Trees

Relation extraction has been widely used to find semantic relations between entities from plain text.Dependency trees provide deeper semantic information for relation extraction.However,existing dependency tree based models adopt pruning strategies that are too aggressive or conservative,leading to insufficient semantic information or excessive noise in relation extraction models.To overcome this issue,we propose the Neural Attentional Relation Extraction Model with Dual Dependency Trees(called DDT-REM),which takes advantage of both the syntactic dependency tree and the semantic dependency tree to well capture syntactic features and semantic features,respectively.Specifically,we first propose novel representation learning to capture the dependency relations from both syntax and semantics.Second,for the syntactic dependency tree,we propose a local-global attention mechanism to solve semantic deficits.We design an extension of graph convolutional networks(GCNs)to perform relation extraction,which effectively improves the extraction accuracy.We conduct experimental studies based on three real-world datasets.Compared with the traditional methods,our method improves the F 1 scores by 0.3,0.1 and 1.6 on three real-world datasets,respectively.

CCD:An Integrated C Coding and Debugging Tool

CCD is an integrated software tool which is intended to support the coding and debugging for C language. It integrates a hybrid editor,an incremental semantic analyzer,a multi-entry parser,an incremental unpaser and a source-level debugger into a single tool.The integration is realized by sharing common knowledge,among all the components of the system and by task-oriented comhination of the components.Nonlocal attribute grammar is adopted for specifying the common knowledge about the syntax and semantics of C language.The incremental attri bute evaluation is used to implement the semantic analyzer and the unparser to increase system efficiency.CCD keeps the preprocessors and comments most regular to make it practical.

The Implementation Technology of a Model Description and Management Tools of Comprehensive Information

This paper introduces the implementation technology of MDAICI system, a model description and management tool to process comprehensive information in Computer integrated Manufacturing Systems (CIMS). XDMIC combines logic programming with relational database. This paper presents PCRF approach for model description and management-Predicates Calculus to readresent the model and to generate solving programs and Relational Framework to manipulate models and establish a model dictionary. PCF approach implements the division of the model with its solving program and the formalization of the model interpretation, and applies artificial intelligence and database to intelligent modeling and model management.