01387nas a2200193   4500008004100000245008000041210006900121260000900190300001000199490000700209520075600216653002300972653001900995653001501014100001501029700001501044700001701059856011701076       2012                        eng d00aA retrieval method based on language model considering neighboring contents0 aretrieval method based on language model considering neighboring  c2012  a1 - 90 v103 aThe World Wide Web (WWW) has a massive number of Web pages, so that it is difficult for users to get useful information. In recent years, however, it is said that the probabilistic language model can help to improve retrieval accuracies of some kinds of search engines. The probabilistic language model has statistical background and can adapt previous text information retrieval model. However, we cannot directly adapt the probabilistic language model to Web search engines, because data on the Web has a hyperlink environment while the probabilistic language model can only text information on it. In this paper, we propose a novel approach for searching Web pages considering a Web page as well as its neighboring oneson the hyperlink environment.10aHyperlink Analysis10aLanguage Model10aWeb search1 aTamura, K.1 aHatano, K.1 aYadohisa, H.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-84857609599&partnerID=40&md5=78bc4a283c5231cf36f479f17c8ab0e501819nas a2200217   4500008004100000245006000041210006000101260000900161300001400170490000600184520115700190653001801347653001301365653002801378653001601406653001501422100001601437700001601453700001501469856011701484       2010                        eng d00aMining the blogosphere to generate cuisine hotspot maps0 aMining the blogosphere to generate cuisine hotspot maps  c2010  a396 - 4010 v83 aChoosing a restaurant is one of the most frequent decisions faced in modern daily life; however, it is difficult for consumers to choose between food/restaurant by reading large amounts of reviews. This study attempts to generate cuisine hotspot maps through blog content mining to help consumers make restaurant decisions by specialties. The main obstacle in doing this involves recognizing and extracting restaurants and essentialrestaurant information (i.e., restaurant dishes) in unstructured content. In contrast to traditional Named Entity Recognition (NER) targets, dish name is a promising target that received little attention in previous studies. This study develops methods for recognizing and extracting restaurant names and dish names from review postsin the blogosphere and achieves satisfactory performance. Based on the method, we processed more than 12,000 Chinese blog posts and generated a cuisine hotspot map. The map shows the most popular dishes of restaurants in a map-view to help consumers make restaurant decisions. A prototype of cuisine hotspot map, named CuisineGuide, is implemented and available as an iPhone application.10aBlog analysis10aHotspots10aName entity recognition10aWeb content10aWeb search1 aShih, C.-C.1 aPeng, T.-C.1 aLai, W.-S.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-79960266928&partnerID=40&md5=4f85ee81adec856534db4c96673f0dbe01809nas a2200217   4500008004100000245008200041210006900123260000900192300001400201490000600215520109000221653002601311653003401337653002001371653001501391100001501406700001401421700001501435700002401450856011701474       2007                        eng d00aPre-processing and Path Normalization of a web graph used as a social network0 aPreprocessing and Path Normalization of a web graph used as a so  c2007  a262 - 2760 v53 aMany efforts have been made for compression of web graphs. Most of these methods are suitable for search engines and are centered around encoding links and URLs efficiently. The purpose is to handle a large set of web pages in the main memory against any web based search. The authors of the present paper are interested in studying web graph as a social network and to develop a data model for it. So, suitable compression techniques, which are space efficient for disk based storage, are required. This paper has provided a two level compression technique. In the first level, the structural properties of a graph are studied and strongly connected components are fused to reduce the original graph to a DAG. Paths on this DAG are then stored efficiently using a Path Normalization technique. Space complexity expressions indicate the efficiency of the method. Relevant operators required for accessing the original graph through the compressed representations have also been discussed. Important earlier works have been referred to indicate the requirements of the present approach.10aCompression technique10aPath normailisation technique10aSocial software10aWeb search1 aTeja, C.B.1 aMitra, S.1 aBagchi, A.1 aBandyopadhyay, A.K.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-57949092373&partnerID=40&md5=08a3428b63ad14c630820944a0e9c22201502nas a2200205   4500008004100000245008600041210006900127260000900196300001400205490000600219520084000225653002601065653001101091653001301102653001901115653001501134100001501149700001501164856011701179       2006                        eng d00aOn the use of ontologies for an optimal representation and exploration of the web0 ause of ontologies for an optimal representation and exploration   c2006  a159 - 1680 v43 aThe use and definition of ontology for the representation and the exploration of knowledge are critical issues for approaches dealing with information retrieval. In this paper, we propose a new ontology-based approach for improving the quality, in terms of relevance, of the results obtained when searching documents on the Internet. This is done by a coherent integration of ontologies, Web data and query languages. We propose new data structures built upon ontologies: the WPGraph and the W 3Graph which allow Web data to be modelled. We also discuss the use of ontologies for an efficient exploration of the knowledge contained in our conceptual structures using ASK, a specific query language introduced in this paper. An experimental validation of our approach is proposed through a prototype supporting our innovative framework.10aConceptual structures10aGraphs10aOntology10aQuery language10aWeb search1 aGuelfi, N.1 aPruski, C.  uhttp://www.scopus.com/inward/record.url?eid=2-s2.0-33749389716&partnerID=40&md5=2881871777661cd78d3507f448bedc32