A review of ontology based query expansion

This paper examines the meaning of context in relation to ontology based query expansion and contains a review of query expansion approaches. The various query expansion approaches include relevance feedback, corpus dependent knowledge models and corpus independent knowledge models. Case studies detailing query expansion using domain-specific and domain-independent ontologies are also included. The penultimate section attempts to synthesise the information obtained from the review and provide success factors in using an ontology for query expansion. Finally the area of further research in applying context from an ontology to query expansion within a newswire domain is described.     

Liquid–Liquid Extraction and Solid Phase Extraction for Urinary Organic Acids: A Comparative Study from a Resource Constraint Setting

Pre analytical process of extraction for accurate detection of organic acids is a crucial step in diagnosis of organic acidemias by GCMS analysis. This process is accomplished either by solid phase extraction (SPE) or by liquid–liquid extraction (LLE). Both extraction procedures are used in different metabolic laboratories all over the world. In this study we compared these two extraction procedures in respect of precision, accuracy, percent recovery of metabolites, number of metabolites isolated, time and cost in a resource constraint setup. We observed that the mean recovery from SPE was 84.1 % and by LLE it was 77.4 % (p value <0.05). Moreover, the average number of metabolites isolated by SPE and LLE was 161.8 ± 18.6 and 140.1 ± 20.4 respectively. The processing cost of LLE was economical. In a cost constraint setting using LLE may be the practical option if used for organic acid analysis.     

When do incumbents learn from entrepreneurial ventures?: Corporate venture capital and investing firm innovation rates

In this paper, we focus on the potential innovative benefits to corporate venture capital (CVC), i.e. equity investments in entrepreneurial ventures by incumbent firms. We propose that corporate venture capital programs may be instrumental in harvesting innovations from entrepreneurial ventures and thus an important part of a firm's overall innovation strategy. We hypothesize that these programs are especially effective in weak intellectual property (IP) regimes and when the firm has sufficient absorptive capacity. We analyze a large panel of public firms over a 20-year period and find that increases in corporate venture capital investments are associated with subsequent increases in firm patenting.     

Secondary immunization inhibits drug induced apoptosis<Emphasis Type="Italic">in vivo</Emphasis>

Immunization with a proper dose of an antigenic stimulus leads to cell proliferation and antibody response of circulating lymphocytes. We have previously observed that Secondary immunized spleenocytes resist ceramide-mediated apoptosisin vitro. Our present study is aimed at investigating thein vivo effect of immunization on apoptosis. Mice were subjected to either Primary or Secondary dose with Tetanus Toxoid. Unimmunized spleenocytes served as controls. Unimmunized, Primary and Secondary immunized mice were later exposed to chemotherapeutic drugs such as Etoposide/Methotrexate/Vincristine to induce apoptosis. Apoptosis was studied by using the Feulgen reaction on 5μ thin parafin sections of spleen. It was observed that Secondary immunized mice showed a lower percentage of apoptosis as compared to Primary or Unimmunized mice that was subjected to either of the chemotherapeutic drugs. It was thus concluded that Secondary immunization inhibits the process of chemotherapeutic drug induced apoptosis in vivo.     

Neutralization theory and online software piracy: An empirical analysis

Accompanying the explosive growth of information technology is the increasing frequency of antisocial and criminal behavior on the Internet. Online software piracy is one such behavior, and this study approaches the phenomenon through the theoretical framework of neutralization theory. The suitability and applicability of nine techniques of neutralization in determining the act is tested via logistic regression analyses on cross-sectional data collected from a sample of university students in the United States. Generally speaking, neutralization was found to be weakly related to experience with online software piracy; other elements which appear more salient are suggested and discussed in conclusion.     

(Residual) Public Music Radio in the Streaming Era: Listening for Diversity and Frequency on CBC Music

The aim of this article is to assess whether and how the CBC’s music radio programming reflects the corporation’s mandate of showcasing a diversity of music and culture. I argue that a program logic that privileges an international corporate music industry strategy persists (particularly during the peak morning and afternoon drive time programs) and limits the capacity for public radio to imagine and project alternative musical trajectories. My aspiration is to imagine CBC Music’s over-the-air radio as a residual broadcast service, one that presents novel opportunities at a time when digital and online streaming music is dominant.     

A new unsupervised method for document clustering by using WordNet lexical and conceptual relations

Text document clustering provides an effective and intuitive navigation mechanism to organize a large amount of retrieval results by grouping documents in a small number of meaningful classes. Many well-known methods of text clustering make use of a long list of words as vector space which is often unsatisfactory for a couple of reasons: first, it keeps the dimensionality of the data very high, and second, it ignores important relationships between terms like synonyms or antonyms. Our unsupervised method solves both problems by using ANNIE and WordNet lexical categories and WordNet ontology in order to create a well structured document vector space whose low dimensionality allows common clustering algorithms to perform well. For the clustering step we have chosen the bisecting k-means and the Multipole tree, a modified version of the Antipole tree data structure for, respectively, their accuracy and speed.

Extending WHIRL with background knowledge for improved text classification

Intelligent use of the many diverse forms of data available on the Internet requires new tools for managing and manipulating heterogeneous forms of information. This paper uses WHIRL, an extension of relational databases that can manipulate textual data using statistical similarity measures developed by the information retrieval community. We show that although WHIRL is designed for more general similarity-based reasoning tasks, it is competitive with mature systems designed explicitly for inductive classification. In particular, WHIRL is well suited for combining different sources of knowledge in the classification process. We show on a diverse set of tasks that the use of appropriate sets of unlabeled background knowledge often decreases error rates, particularly if the number of examples or the size of the strings in the training set is small. This is especially useful when labeling text is a labor-intensive job and when there is a large amount of information available about a particular problem on the World Wide Web.

Result merging methods in distributed information retrieval with overlapping databases

In distributed information retrieval systems, document overlaps occur frequently among different component databases. This paper presents an experimental investigation and evaluation of a group of result merging methods including the shadow document method and the multi-evidence method in the environment of overlapping databases. We assume, with the exception of resultant document lists (either with rankings or scores), no extra information about retrieval servers and text databases is available, which is the usual case for many applications on the Internet and the Web. The experimental results show that the shadow document method and the multi-evidence method are the two best methods when overlap is high, while Round-robin is the best for low overlap. The experiments also show that [0,1] linear normalization is a better option than linear regression normalization for result merging in a heterogeneous environment.