Distant Supervision for Entity Linking

Entity linking is an indispensable operation of populating knowledge repositories for information extraction. It studies on aligning a textual entity mention to its corresponding disambiguated entry in a knowledge repository. In this paper, we propose a new paradigm named distantly supervised entity linking (DSEL), in the sense that the disambiguated entities that belong to a huge knowledge repository (Freebase) are automatically aligned to the corresponding descriptive webpages (Wiki pages). In this way, a large scale of weakly labeled data can be generated without manual annotation and fed to a classifier for linking more newly discovered entities. Compared with traditional paradigms based on solo knowledge base, DSEL benefits more via jointly leveraging the respective advantages of Freebase and Wikipedia. Specifically, the proposed paradigm facilitates bridging the disambiguated labels (Freebase) of entities and their textual descriptions (Wikipedia) for Web-scale entities. Experiments conducted on a dataset of 140,000 items and 60,000 features achieve a baseline F1-measure of 0.517. Furthermore, we analyze the feature performance and improve the F1-measure to 0.545

Trade Liberalization and Trade Performance of Environmental Goods: Evidence from Asia-Pacific Economic Cooperation Members

In this article, we study the impact of trade liberalization, including reductions in both tariff and nontariff trade barriers, on environmental goods (EGs) exports. Using bilateral trade data from 20 Asia-Pacific Economic Cooperation members, we find that tariff reduction in an exporting country has a larger positive impact on its exports of EGs than tariff reduction in an importing country. Our results also show that a lower nontariff barrier in an importing country increases its imports of EGs. A considerable amount of heterogeneity also exists in subsample results based on countries’ income levels

Large Margin Nearest Neighbor Embedding for Knowledge Representation

Traditional way of storing facts in triplets ({\it head\_entity, relation, tail\_entity}), abbreviated as ({\it h, r, t}), makes the knowledge intuitively displayed and easily acquired by mankind, but hardly computed or even reasoned by AI machines. Inspired by the success in applying {\it Distributed Representations} to AI-related fields, recent studies expect to represent each entity and relation with a unique low-dimensional embedding, which is different from the symbolic and atomic framework of displaying knowledge in triplets. In this way, the knowledge computing and reasoning can be essentially facilitated by means of a simple {\it vector calculation}, i.e. ${\bf h} + {\bf r} \approx {\bf t}$. We thus contribute an effective model to learn better embeddings satisfying the formula by pulling the positive tail entities ${\bf t^{+}}$ to get together and close to {\bf h} + {\bf r} ({\it Nearest Neighbor}), and simultaneously pushing the negatives ${\bf t^{-}}$ away from the positives ${\bf t^{+}}$ via keeping a {\it Large Margin}. We also design a corresponding learning algorithm to efficiently find the optimal solution based on {\it Stochastic Gradient Descent} in iterative fashion. Quantitative experiments illustrate that our approach can achieve the state-of-the-art performance, compared with several latest methods on some benchmark datasets for two classical applications, i.e. {\it Link prediction} and {\it Triplet classification}. Moreover, we analyze the parameter complexities among all the evaluated models, and analytical results indicate that our model needs fewer computational resources on outperforming the other methods.Comment: arXiv admin note: text overlap with arXiv:1503.0815

Experimental study of the effectiveness of air curtains of variable width and injection angle to block fire-induced smoke in a tunnel configuration

Small-scale experiments have been conducted to study the sealing effect of an air curtain for fire-induced smoke confinement in a tunnel configuration. The processed data confirmed the results obtained earlier from blind Computational Fluid Dynamics (CFD) simulations [1] using the Fire Dynamics Simulator (FDS) 6.5.3 [2,3]. Furthermore, the CFD simulations provided complementary information on the detailed flow and temperature fields which are difficult to obtain in experiments with the available techniques. A parametric study is performed, covering a range of air curtain velocities, slot widths, injection angles and total fire heat release rates (HRRs). The momentum ratio R, defined as the ratio of the vertically downward air curtain momentum to the horizontal smoke layer momentum at the position of the air curtain, is confirmed to be a key parameter for the air curtain performance. A ratio R ≈ 10 is recommended for the optimum sealing effect in terms of smoke confinement. In addition, two other important parameters that determine the performance of air curtains for smoke confinement are presented. The first parameter is the dimensionless shape factor AR (AR = Width/Length) that characterizes the dilution effect of the air curtain jet. The second parameter is the injection angle θ that characterizes the horizontal force of the air curtain. The air curtain sealing effectiveness increases with both the increase of slot width (shape factor AR) and injection angle (θ). The air curtain width has a limited influence on the performance of the air curtain whilst the injection angle has a more significant influence on the sealing effectiveness of the air curtain for the scenarios considered in this study. An optimal injection angle of 30° inclined to the fire source is recommended in the engineering design of the air curtain for smoke confinement for situations where the fire location can be pre-determined to be only at one side of an air curtain