No abstract

Although software reuse presents clear advantages for programmer productivit.y and code reliability, it is not practiced enough. One of the reasons for the only moderate success of reuse is the lack of software libr'lries that facilitate the actual locating and understanding of reusable components. This paper dpscrihes II technology for automatically I\Ssembling large softwar~ libraries that promote softwarp rpuse by helping the user locate the components closest to hfO'r/his neeels Softwarp libraries are aut.omatically assemblfO'd from a set. of Ilnorganizpd component.s by using informat.ion rptripval techniques. The constrllction of the library is done in !.wo steps. First, attribut.es ;up alJtornal.ically pxtracted from nat.urlll language docllmpntat.ion by IIsing a npIV inJexing scheme ba~fO'd an t.he nntinns of lexical affinities anci quantity of inforrnllt.ion. Then, a hierarchy for browsing is alJtomat.iclllly generaterl using a clustering techniq1le that draws only nn the informlltion provided by the attribulps. Thanks to the frfO'p-!.pxt inrlexing se-heme, tools following this approach can accept free-style nat'Jral language 'luprips. This tpChllOlogy h1\.S bfO'en impll"ment .. d in the GURU ~ystpm, whirh hrforms l!i% better on a random test set, while being milch le~s pxpensive to build than INFOEXl'LORF.R.

No abstract

Most of the work on XML query and search has stemmed from the publishing and database communities, mostly for the needs of business applications. Recently, the Information Retrieval community began investigating the XML search issue to answer information discovery needs. Following this trend, we present here an approach where information needs can be expressed in an approximate manner as pieces of XML documents or "XML fragments" of the same nature as the documents that are being searched. We present an extension of the vector space model for searching XML collections via XML fragments and ranking results by relevance. We describe how we have extended a fulltext search engine to comply with this model. The value of the proposed method is demonstrated by the relative high precision of our system, which was among the top performers in the recent INEX workshop. Our results indicate that certain queries are more appropriate than others for the extended vector space model. Specifically, queries with relatively specific contexts but vague information needs are best situated to reap the benefit of this model. Finally our results show that one method may not fit all types of queries and that it could be worthwhile to use different solutions for different applications.

The ability to aggregate huge volumes of queries over a large population of users allows search engines to build precise models for a variety of query-assistance features such as query recommendation, correction, etc. Yet, no matter how much data is aggregated, the long-tail distribution implies that a large fraction of queries are rare. As a result, most query assistance services perform poorly or are not even triggered on long-tail queries. We propose a method to extend the reach of query assistance techniques (and in particular query recommendation) to long-tail queries by reasoning about rules between query templates rather than individual query transitions, as currently done in query-flow graph models. As a simple example, if we recognize that 'Montezuma' is a city in the rare query "Montezuma surf" and if the rule ' surf → beach' has been observed, we are able to offer "Montezuma beach" as a recommendation, even if the two queries were never observed in a same session. We conducted experiments to validate our hypothesis, first via traditional small-scale editorial assessments but more interestingly via a novel automated large scale evaluation methodology. Our experiments show that general coverage can be relatively increased by 24% using templates without penalizing quality. Furthermore, for 36% of the 95M queries in our query flow graph, which have no out edges and thus could not be served recommendations, we can now offer at least one recommendation in 98% of the cases.