This paper introduces a technique for mining a collection of user transactions with an Internet search engine to discover clusters of similar queries and similar URLs. The information we exploit is "clickthrough data": each record consists of a user's query to a search engine along

search engine. We define various similarity scores for two given words P and Q, using the page counts for the queries P, Q and P AND Q. Moreover, we propose a novel approach to compute semantic similarity using automatically extracted lexical-syntactic patterns from text snippets. These different

Social bookmarking is a recent phenomenon which has the potential to give us a great deal of data about pages on the web. One major question is whether that data can be used to augment systems like web search. To answer this question, over the past year we have gathered what we believe

discovery linkage itself is not enough, and crawling and indexing of data is necessary. Existing Semantic Web search engines are focused on database-like functionality, compromising on index size, query performance and live updates. We present Sindice, a lookup index over resources crawled on the Semantic

websites in groups. This paper addresses the applications of data mining tool Weka by applying k means clustering to find clusters from huge data sets and find the attributes that govern optimization of search engines. Keywords—Dataset; Websites; Data mining; Weka; k-means I.

, there is a lack of collections which are specially developed for evaluation of the effectiveness of semantically en-hanced text retrieval techniques. In this paper, we propose an approach for the automatic generation of such data-sets, by using search engines query logs and data from human-edited web

Abstract. We present the architecture of an end-to-end semantic search engine that uses a graph data model to enable interactive query answer-ing over structured and interlinked data collected from many disparate sources on the Web. In particular, we study distributed indexing meth-ods for graph

) the availability of a representative corpus of entities that are found on the Web, and (2) the availability of an entity-oriented search infrastructure for experimenting with new retrieval models. In this paper, we introduce the Sindice-2011 data collection which is derived from data collected by the Sindice

We present a system called ALIAS, that is designed to search for duplicate authors from Microsoft Academic Search Engine dataset. Author-ambiguity is a prevalent problem in this dataset, as many authors publish under several variations of their own name, or dif-ferent authors share similar or same

engines. In the atmospheric sciences, there has been a significant growth in public data hosting. However, the ability to index and search has been limited by the metadata provided by the data host. We have developed an infrastructure – Atmospheric Data Discovery System (ADDS) – that provides