Intelligent Semantic Web Search Engines: A Brief Survey <s> Sara Cohen Jonathan <s> wshih@cs.nthu.edu.tw ABSTRACT Although extracting information from tables is essential for Internet information agents, most tables are designed for human eyes and their layout and semantic meanings are not well defined. In practice, encoding the layout of each information source is impossible. This work presents a novel semantic search approach capable of extracting information from general tables. Semantic ontology allows our agents to read tables in the same knowledge domain with different layouts. In addition, a system of layout syntax and a set of transformation rules are defined to transform tables into databases without losing their semantic meanings. <s> BIB001 </s> Intelligent Semantic Web Search Engines: A Brief Survey <s> Sara Cohen Jonathan <s> We describe Eureka, a file system search engine that takes into account the inherent relationships among files in order to improve the rankings of search results. The key idea is to automatically infer semantic links within the file system, and use the structure of the links to determine the importance of different files and essentially bias the result rankings. We discuss the inference of semantic links and describe the design of the Eureka search engine. <s> BIB002 </s> Intelligent Semantic Web Search Engines: A Brief Survey <s> Sara Cohen Jonathan <s> We present Avatar Semantic Search, a prototype search engine that exploits annotations in the context of classical keyword search. The process of annotations is accomplished offline by using high-precision information extraction techniques to extract facts, con-cepts, and relationships from text. These facts and concepts are represented and indexed in a structured data store. At runtime, keyword queries are interpreted in the context of these extracted facts and converted into one or more precise queries over the structured store. In this demonstration we describe the overall architecture of the Avatar Semantic Search engine. We also demonstrate the superiority of the AVATAR approach over traditional keyword search engines using Enron email data set and a blog corpus. <s> BIB003
Mamou et al presented a semantic search engine for XML (XSEarch)  .It has a simple query language, suitable for a naïve user. It returns semantically related document fragments that satisfy the user's query. Query answers are ranked using extended information-retrieval techniques and are generated in an order similar to the ranking. Advanced indexing techniques were developed to facilitate efficient implementation of XSEarch. The performance of the different techniques as well as the recall and the precision were measured experimentally. These experiments indicate that XSEarch is efficient, scalable and ranks quality results highly. Bhagwat and Polyzotis propose a Semantic-based file system search engine-Eureka, which uses an inference model to build the links between files and a File Rank metric to rank the files according to their semantic importance BIB002 . Eureka has two main parts: a) crawler which extracts file from file system and generates two kinds of indices: keywords' indices that record the keywords from crawled files, and rank index that records the File Rank metrics of the files; b) when search terms are entered, the query engine will match the search terms with keywords' indices, and determine the matched file sets and their ranking order by an information retrievalbased metrics and File Rank metrics. Wang et al. project a semantic search methodology to retrieve information from normal tables, which has three main steps: identifying semantic relationships between table cells; converting tables into data in the form of database; retrieving objective data by query languages BIB001 . The research objective defined by the authors is how to use a given BIB003 . Avatar has two main functions: a) extraction and representation -by means of UIMA framework, which is a workflow consisting of a chain of annotators extracted from documents and stored in the annotation store; b) interpretation -a process of automatically transforming a keyword search to several precise searches. Avatar consists of two main parts: semantic optimizer and user interaction engine. When a query is entered into the former, it will output a list of ranked interpretations for the query; then the topranked interpretations are passed to the latter, which will display the interpretations and the retrieved documents from the interpretations.