An Introduction to a Meta-meta-search Engine

Imagine that all the information in the entire world written in every known language, and every graphic image, video clip, or photograph copied digitally was available at your fingertips. This vast amount of data could then be reduced to digital data packets and stored in miniscule form on computer hard drives that are all connected by several other centrally located larger machines, called servers. However, searching for data in a vast system of inter-connected computers is virtually impossible using human faculties and is a far more intricate process than perusing book titles using the library\u27s Dewey Decimal System. In order to find five or six pieces of information out of a global network of servers, individuals can explore the advantages of meta-search engines, which understand the language of each computer on the network and can quickly access global databases to respond to user inquiries, based on certain keywords or phrases. The advantages of meta-search engines are that they are able to talk to other search engines, which contain relevant data. The language that they speak is HTML, or hypertext markup language, a set of electronic codes that enables computers to read, translate, transmit, and store data accessible to the entire world. Every Web page is written in HTML using meta tags, which are directives to client computers describing the kind of document stored. By reading meta tags, search engines are able to electronically skim through vast databases to select data that match a user\u27s inquiry. However, the existing meta-search engines are still facing issues in providing accurate results that match user queries due to the extremely fast growth and the complexity of information that is stored in the Web server. This thesis proposes a new algorithm that will re-rank the Web search results from some of the best existing meta-search engines. This algorithm can be implemented to form a meta-meta-search engine. As a result, the new search engine will have the capability of listing a more reliable rank list with higher accuracy in comparison to the existing search engines and meta-search engines

Automatic text summarization in digital libraries

xiii, 142 leaves ; 28 cm.A digital library is a collection of services and information objects for storing, accessing, and retrieving digital objects. Automatic text summarization presents salient information in a condensed form suitable for user needs. This thesis amalgamates digital libraries and automatic text summarization by extending the Greenstone Digital Library software suite to include the University of Lethbridge Summarizer. The tool generates summaries, nouns, and non phrases for use as metadata for searching and browsing digital collections. Digital collections of newspapers, PDFs, and eBooks were created with summary metadata. PDF documents were processed the fastest at 1.8 MB/hr, followed by the newspapers at 1.3 MB/hr, with eBooks being the slowest at 0.9 MV/hr. Qualitative analysis on four genres: newspaper, M.Sc. thesis, novel, and poetry, revealed narrative newspapers were most suitable for automatically generated summarization. The other genres suffered from incoherence and information loss. Overall, summaries for digital collections are suitable when used with newspaper documents and unsuitable for other genres

Learning Ontology Relations by Combining Corpus-Based Techniques and Reasoning on Data from Semantic Web Sources

The manual construction of formal domain conceptualizations (ontologies) is labor-intensive. Ontology learning, by contrast, provides (semi-)automatic ontology generation from input data such as domain text. This thesis proposes a novel approach for learning labels of non-taxonomic ontology relations. It combines corpus-based techniques with reasoning on Semantic Web data. Corpus-based methods apply vector space similarity of verbs co-occurring with labeled and unlabeled relations to calculate relation label suggestions from a set of candidates. A meta ontology in combination with Semantic Web sources such as DBpedia and OpenCyc allows reasoning to improve the suggested labels. An extensive formal evaluation demonstrates the superior accuracy of the presented hybrid approach

Arkansas Soybean Research Studies 2014

Arkansas is the leading soybean-producing state in the mid-southern United States. Arkansas ranked 10th in soybean production in 2015 when compared to the other soybean-producing states in the U.S. The state represents 4.0% of the total U.S. soybean production and 3.7% of the total acres planted to soybean in 2015. The 2015 state soybean average was 49 bushels per acres, 0.5 bushel per acres less than the state record soybean yield set in 2014 (Table 1). The top five soybean-producing counties in 2015 were Mississippi, Desha, Poinsett, Phillips, and Arkansas Counties. These five counties accounted for 35% of soybean production in Arkansas in 2015