A concept-based retrieval method for entity-oriented search

Entity-oriented retrieval aims to return a list of relevant entities rather than documents to provide exact answers for user queries. The nature of entity-oriented retrieval requires identifying the semantic intent of user queries, i.e., understanding the semantic role of query terms and determining the semantic categories which indicate the class of target entities. Existing methods are not able to exploit the semantic intent by capturing the semantic relationship between terms in a query and in a document that contains entity related information. To improve the understanding of the semantic intent of user queries, we propose concept-based retrieval method that not only automatically identifies the semantic intent of user queries, i.e., Intent Type and Intent Modifier but introduces concepts represented by Wikipedia articles to user queries. We evaluate our proposed method on entity profile documents annotated by concepts from Wikipedia category and list structure. Empirical analysis reveals that the proposed method outperforms several state-of-the-art approaches.

ACE research vignette 028 : Does it pay for established firms to be entrepreneurial?

This series of research vignettes is aimed at sharing current and interesting research findings from our team of international Entrepreneurship researchers. This vignette, written by Professor Per Davidsson, examines the evidence on the effects of a firm’s level of “entrepreneurial orientation” on business performance, across different contexts.

XML clustering and its application to XML transformation

The continuous growth of the XML data poses a great concern in the area of XML data management. The need for processing large amounts of XML data brings complications to many applications, such as information retrieval, data integration and many others. One way of simplifying this problem is to break the massive amount of data into smaller groups by application of clustering techniques. However, XML clustering is an intricate task that may involve the processing of both the structure and the content of XML data in order to identify similar XML data. This research presents four clustering methods, two methods utilizing the structure of XML documents and the other two utilizing both the structure and the content. The two structural clustering methods have different data models. One is based on a path model and other is based on a tree model. These methods employ rigid similarity measures which aim to identifying corresponding elements between documents with different or similar underlying structure. The two clustering methods that utilize both the structural and content information vary in terms of how the structure and content similarity are combined. One clustering method calculates the document similarity by using a linear weighting combination strategy of structure and content similarities. The content similarity in this clustering method is based on a semantic kernel. The other method calculates the distance between documents by a non-linear combination of the structure and content of XML documents using a semantic kernel. Empirical analysis shows that the structure-only clustering method based on the tree model is more scalable than the structure-only clustering method based on the path model as the tree similarity measure for the tree model does not need to visit the parents of an element many times. Experimental results also show that the clustering methods perform better with the inclusion of the content information on most test document collections. To further the research, the structural clustering method based on tree model is extended and employed in XML transformation. The results from the experiments show that the proposed transformation process is faster than the traditional transformation system that translates and converts the source XML documents sequentially. Also, the schema matching process of XML transformation produces a better matching result in a shorter time.

A novel method for finding similarities between unordered trees using matrix data model

Trees are capable of portraying the semi-structured data which is common in web domain. Finding similarities between trees is mandatory for several applications that deal with semi-structured data. Existing similarity methods examine a pair of trees by comparing through nodes and paths of two trees, and find the similarity between them. However, these methods provide unfavorable results for unordered tree data and result in yielding NP-hard or MAX-SNP hard complexity. In this paper, we present a novel method that encodes a tree with an optimal traversing approach first, and then, utilizes it to model the tree with its equivalent matrix representation for finding similarity between unordered trees efficiently. Empirical analysis shows that the proposed method is able to achieve high accuracy even on the large data sets.

Science of winning soccer : emergent pattern-forming dynamics in association football

Quantitative analysis is increasingly being used in team sports to better understand performance in these stylized, delineated, complex social systems. Here we provide a first step toward understanding the pattern-forming dynamics that emerge from collective offensive and defensive behavior in team sports. We propose a novel method of analysis that captures how teams occupy sub-areas of the field as the ball changes location. We used the method to analyze a game of association football (soccer) based upon a hypothesis that local player numerical dominance is key to defensive stability and offensive opportunity. We found that the teams consistently allocated more players than their opponents in sub-areas of play closer to their own goal. This is consistent with a predominantly defensive strategy intended to prevent yielding even a single goal. We also find differences between the two teams' strategies: while both adopted the same distribution of defensive, midfield, and attacking players (a 4:3:3 system of play), one team was significantly more effective both in maintaining defensive and offensive numerical dominance for defensive stability and offensive opportunity. That team indeed won the match with an advantage of one goal (2 to 1) but the analysis shows the advantage in play was more pervasive than the single goal victory would indicate. Our focus on the local dynamics of team collective behavior is distinct from the traditional focus on individual player capability. It supports a broader view in which specific player abilities contribute within the context of the dynamics of multiplayer team coordination and coaching strategy. By applying this complex system analysis to association football, we can understand how players' and teams' strategies result in successful and unsuccessful relationships between teammates and opponents in the area of play.

Modelling NPO-government relations : Australian case studies

Non-profit organizations (NPOs) are major providers of services in many fields of endeavour, and often receive financial support from government. This article investigates different forms of government/nonprofit funding relationships, with the viewpoint being mainly, though not exclusively, from the perspective of the non-profit agencies. While there are a number of existing typologies of government/NPO relations, these are dated and in need of further empirical analysis and testing. The article advances an empirically derived extension to current models of government/NPO relations. A future research agenda is outlined based on the constructs that underpin typologies, rather than discrete categorization of relationships.

The ranking based constrained document clustering method and its application to social event detection

With the growing size and variety of social media files on the web, it’s becoming critical to efficiently organize them into clusters for further processing. This paper presents a novel scalable constrained document clustering method that harnesses the power of search engines capable of dealing with large text data. Instead of calculating distance between the documents and all of the clusters’ centroids, a neighborhood of best cluster candidates is chosen using a document ranking scheme. To make the method faster and less memory dependable, the in-memory and in-database processing are combined in a semi-incremental manner. This method has been extensively tested in the social event detection application. Empirical analysis shows that the proposed method is efficient both in computation and memory usage while producing notable accuracy.

Assessing the property market impact of stigma removal : high voltage overhead transmission lines removal in Wellington, NZ

This thesis advances the understanding of the impact of stigma on property values. A case study in Wellington, New Zealand, enabled hedonic modelling and an empirical analysis to determine the impact of the stigma from the high voltage transmission line structure and how long the stigma remained after removal. The results reveal a substantial difference between the discount applied to individual properties while the structure is in place, as compared to the overall increase in neighbourhood value once the structure, which created the stigma, is removed.

Tensor-based item recommendation using probabilistic ranking in social tagging systems

A common problem with the use of tensor modeling in generating quality recommendations for large datasets is scalability. In this paper, we propose the Tensor-based Recommendation using Probabilistic Ranking method that generates the reconstructed tensor using block-striped parallel matrix multiplication and then probabilistically calculates the preferences of user to rank the recommended items. Empirical analysis on two real-world datasets shows that the proposed method is scalable for large tensor datasets and is able to outperform the benchmarking methods in terms of accuracy.

Synthesis, characterization and comparative study of thiophene- benzothiadiazole based donor-acceptor-donor (D-A-D) materials

The synthesis and characterization of solution processable donor-acceptor-donor (D-A-D) based conjugated molecules with varying ratios of thiophene as donor (D) and benzothiadiazole as acceptor (A) are reported. Optical, electrochemical, thermal, morphological and organic thin film transistor (OTFT) device properties of these materials were investigated. The thermal and polarized optical microscope analysis indicates that the materials having higher D/A ratios exhibit both liquid crystalline (LC) and OTFT behavior. AFM analysis of the materials having D/A ratios of 3 and 4 (3T1B and 4T1B) show well ordered structures, resulting from strong π-π interchain interactions compared to the other molecules in this study. A XRD patterns for 3T1B and 4T1B thin films also shows high crystalline ordering. Solution processed OTFTs of 3T1B and 4T1B have shown un-optimized charge carrier mobilities of 2 × 10 -2 cm 2 V -1 s -1 and 4 × 10 -3 cm 2 V -1 s -1, respectively on bare Si/SiO 2 substrate.

Corporate credit risk prediction under stochastic volatility and jumps

This paper examines the impact of allowing for stochastic volatility and jumps (SVJ) in a structural model on corporate credit risk prediction. The results from a simulation study verify the better performance of the SVJ model compared with the commonly used Merton model, and three sources are provided to explain the superiority. The empirical analysis on two real samples further ascertains the importance of recognizing the stochastic volatility and jumps by showing that the SVJ model decreases bias in spread prediction from the Merton model, and better explains the time variation in actual CDS spreads. The improvements are found particularly apparent in small firms or when the market is turbulent such as the recent financial crisis.

Financial literacy and superannuation investment decision-making in a choice environment : an exploratory study

This thesis examines superannuation fund members' level of financial literacy and its association with investment choice decisions in the setting of the mandatory superannuation system in Australia. It also investigates a range of contextual and socio-demographic factors in explaining financial literacy and investment choice. The empirical analysis shows that while most survey respondents displayed high levels of self-rated and general financial literacy, fewer scored as well in relation to more advanced literacy regarding superannuation investment options. The findings of this study have important implications for policy-makers and the superannuation industry in educating and engaging with fund members.

Suicide and religion : new evidence on the differences between Protestantism and Catholicism

Suicide has remained a persistent social phenomenon and now accounts for more deaths than motor vehicle accidents. There has been much debate, however, over which religious constructs might best explain the variation in suicide rates. Our empirical analysis reveals that even though theological and social differences between Catholicism and Protestantism have decreased, Catholics are still less likely than Protestants to commit or accept suicide. This difference holds even after we control for such confounding factors as social and religious networks. In addition, although religious networks do mitigate suicides among Protestants, the influence of church attendance is more dominant among Catholics. Our analysis also indicates that alternative concepts such as religious commitment and religiosity strongly reduce suicide acceptance.

Mining discriminative Itemsets in data streams

This paper presents a single pass algorithm for mining discriminative Itemsets in data streams using a novel data structure and the tilted-time window model. Discriminative Itemsets are defined as Itemsets that are frequent in one data stream and their frequency in that stream is much higher than the rest of the streams in the dataset. In order to deal with the data structure size, we propose a pruning process that results in the compact tree structure containing discriminative Itemsets. Empirical analysis shows the sound time and space complexity of the proposed method.

An efficient tagging data interpretation and representation scheme for item recommendation

A tag-based item recommendation method generates an ordered list of items, likely interesting to a particular user, using the users past tagging behaviour. However, the users tagging behaviour varies in different tagging systems. A potential problem in generating quality recommendation is how to build user profiles, that interprets user behaviour to be effectively used, in recommendation models. Generally, the recommendation methods are made to work with specific types of user profiles, and may not work well with different datasets. In this paper, we investigate several tagging data interpretation and representation schemes that can lead to building an effective user profile. We discuss the various benefits a scheme brings to a recommendation method by highlighting the representative features of user tagging behaviours on a specific dataset. Empirical analysis shows that each interpretation scheme forms a distinct data representation which eventually affects the recommendation result. Results on various datasets show that an interpretation scheme should be selected based on the dominant usage in the tagging data (i.e. either higher amount of tags or higher amount of items present). The usage represents the characteristic of user tagging behaviour in the system. The results also demonstrate how the scheme is able to address the cold-start user problem.