A. Motro. SEAVE: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.  Home/Search   Document Not in Database   Summary   Related Articles   Check

....tuples of a constraint database. However, matchmaking is different in that it employs a semistructured data model, and ClassAds are consumed during the matching process. Most of the work relevant to ClassAd analysis is in literature on databases, particularly on cooperative query answering. In [8] a mechanism called SEAVE is presented for extracting and verifying presuppositions from queries. This mechanism identifies queries which result in null answers, then finds more general queries by weakening or deleting query subexpressions. The result is a set of maximally general erroneous ....

A. Motro. SEAVE: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.

....relaxation for XML model more important than that for the relational model, as discussed in Chapter 1.1. 3.2 Related Work Query relaxation and its related techniques (e.g. cooperative information systems, query expansion, etc. have been extensively investigated in both IR and DB areas (e.g. [Kap82, Mot84, Mot86, Gal88, Mot90, CLC91, CYC94, CCH94, CYC96, CCH96, God97, CG99]) In this Section, we briefly review those related works. When a query fails, it is more cooperative to identify the causes of failure, rather than just to report the empty answer set [God97] Information system with such capability is known as Cooperative Information System. Kaplan [Kap82] is ....

....to observe the relevance of false presuppositions to databases 22 and studied a method to find the minimal failing sub queries. Also, he introduced the notion of generalizing a failing query into a successful query by removing some of the failing sub queries from the original query. Motro [Mot84, Mot86, Mot90] extended the Kaplan s notion of query generalization into the case where a degree of query condition is relaxed . Thus, Kaplan s query generalization where some sub queries may be removed can be viewed as a special case of Motro s extended framework. CoBase system [CLC91, CCH94, CYC96, CCH96] ....

A. Motro. "SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems". ACM Trans. on Information Systems (TOIS), 4(4):312--330, Oct. 1986.

....to use. All three of these cooperative behaviors for better response to failing database queries require search for a minimal subquery. It is this search for minimals that is the focus of this paper. There has been much research on this topic, in particular on finding minimal failing subqueries [9, 27, 29, 30, 31, 37, 38]. This work has not formally addressed the complexity of finding MFSs to queries. The implicit assumption has been that it may cost exponential time in worst case even to find one MFS. Much work has been devoted to heuristics and other means to reduce the search time to find MFSs, with the hopes ....

....boolean queries for library searches. Their system also reports the number of matches found for each subquery and displays the results graphically. The MFSs then are just the subqueries which had zero matches. This is intended to help the user to choose among the subqueries to pursue. Motro [37, 38] extended on the notion of false presuppositions. Instead of considering only the subqueries of a query, as defined above, he considers certain generalizations of the query as well which are logical presuppositions to the query. The query generalizations are obtained by relaxing to a degree some ....

[Article contains additional citation context not shown here]

A. Motro. SEAVE: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.

....conflict with a constraint. It also includes extra information. We have developed an algorithm for gleaning from the IDB tree the minimal information for making the connection. We then add the IDB tree information to the cooperative response. 3. 3 Relaxation As noted by many researchers, including [4, 6, 14, 1, 24, 25, 23, 17], an alternative form of cooperative behavior involves providing associated information which is relevant to a query. Generalizing a query in order to capture neighboring information is one means to obtain possibly relevant information. Gaasterland, Godfrey and Minker have defined a method to ....

A. Motro. SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems. ACM Transactions on Office Information Systems, 4(4), October 1986.

....relevant to (an attempt to generate) the missing fact can indeed be identified, as discussed in Section 6.2. There has also been some interesting work with respect to the understanding of null answers to a query, and the identification of erroneous pre suppositions contained in the query [Mot86, Mot90]. This is closely related to the issue of missing facts , and our research does not address this aspect of query explanation. 3 This is described only briefly in [ST90] and the debugger is not available with the LDL system we have. There is also a connection between Explain and Prolog ....

Amihai Motro. Seave: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312-- 330, October 1986.

....An IC can logically subsume a query, meaning the query cannot have answers. For instance, say ae emp (X) owns (X, Y) car (Y) were a constraint itself, meaning that employees do not own cars. This is a necessarily failing subquery of Q 2 , thus Q 2 itself must fail. The SEAVE system of Motro [50] also uses integrity constraints together with false presuppositions in order to provide additional information about failed queries. Consider the query professor (X) enrolled in ( CMSC 420 ) Q 3 ) in a database with the following integrity constraints 1994 Overview of Cooperative ....

A. Motro. SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems. ACM Transactions on Office Information Systems, 4(4), October 1986.

....restricts people in a database from having two genders. Because the constraints on a database add no new deductive knowledge to the database, they are considered semantic information rather than syntactic information. 3 Relaxation As noted by many researchers, including [ 1, 3, 4, 12, 13, 15, 16, 18 ] , one form of cooperative behavior involves providing associated information that is relevant to a query. Generalizing a query in order to capture neighboring information is a means to obtain possibly relevant information. Gaasterland, Godfrey, and Minker [ 7 ] have defined a method to relax a ....

A. Motro. SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems. ACM Transactions on Office Information Systems, 4(4), October 1986.

....the database. A query with a false presupposition has no answer, positive or negative, and a cooperative response would identify the false presupposition to the user. The SEAVE system also uses integrity constraints and false presuppositions to provide additional information about failed queries [Mot86b] Our relaxation method is a general approach to seek additional answers to a query that may or may not be of direct interest to the user. To ensure that the additional answers are relevant, some schemes posed by others for detecting users plans and intents may be used to refine the approach. ....

Amihai Motro. SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems. ACM Transactions on Office Information Systems, 4(4), October 1986.

....was the first to study the computational feasibility of reporting the smallest sub queries that fail, and studied queries in a relational setting. Corella et al. 5] considered finding the relaxations of conjunctive boolean queries for library searches. We discuss this in the next section. Motro [24, 25] extended on the notion of false presuppositions and relaxation: instead of eliminating conditions from a query, they can be replaced by more general conditions. Gal and Minker [12, 13] considered false presuppositions in coordination with miscon fa; cg 0 fcg 31 fbg 23 fag 15 fa; b; cg 0 fg 1 ....

A. Motro. SEAVE: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.

....9:00am and ends after 10:00am. Notice that the elaboration does not contain the literal in the rule I about ser vice type. The algorithm for selecting search tree information described in [10] adds the elaboration to the logical response. 3. 3 Relaxation As noted by many researchers, including [1, 4, 6, 18, 21, 27, 28, 30], an alternative form of cooperative behavior involves providing associated information which is relevant to a query. Generalizing a query in order to capture neighboring information is one means to obtain possibly relevant information. Gaasterland, Godfrey and Minker have defined a method to ....

A. Motro. SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems. ACM Transactions on Office Information Systems, 4(4), October 1986.

....database systems easier to use. All three of these cooperative behaviors for better response to failing database queries require search for a minimal subquery. It is this search for a minimal that is the focus of this paper. There has been much research on the topic, in particular on finding MFSs [7, 24, 26, 27, 28, 31, 32]. This work has not formally addressed the complexity of finding MFSs to queries. The implicit assumption has been that it may cost exponential time in worst case even to find one MFS. Much work has been devoted to heuristics and other means to reduce the search time to find MFSs, so that the ....

.... staph (I) Figure 2: A lattice of subqueries. system also reports the number of matches found for each subquery and displays the results graphically. The MFSs then are just the subqueries which had zero matches. This is intended to help the user to choose among the subqueries to pursue. Motro [31, 32] extended on the notion of false presuppositions. Instead of considering only the subqueries of a query, as defined above, he considers certain generalizations of the query as well, which are logical presuppositions to the query. The query generalizations are obtained by relaxing to a degree some ....

[Article contains additional citation context not shown here]

A. Motro. SEAVE: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.

....was CO OP (A Cooperative Query System) by Kaplan [29, 30] The system combined a natural language query front end over the CODASYL database management system SEED. The system was tested over a real database from the National Center for Atmospheric Research on both users and programmers. Motro [33, 34, 35] has considered modifications to the relational model that are easily incorporated into a relational database system to allow for certain 8 References to the Carmin system in this paper refer to Carmin II. June 1994 An Architecture for a CDBS GNM 94 p. 7 of 22 cooperative behaviors. In [35] ....

....the user corrected queries with which to proceed. Motro introduces the notion of returning to a user related queries that may more closely match the user s intended question. FLEX also incorporates false presupposition analysis for queries which have empty answer sets. Motro s earlier system SEAVE [34] is designed to find false presuppositions in queries. He extends the notion of false presupposition to that of weakening the query. So instead of throwing literals away to obtain subqueries, they are replaced with weaker literals. 9 This unifies a notion of query generalization with false ....

[Article contains additional citation context not shown here]

A. Motro. SEAVE: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.

....the user. Checking all presuppositions can be costly, but integrity constraints can be used to rule out presuppositions that are certain to be true [Jan81] The SEAVE system also uses integrity constraints together with false presuppositions to provide additional information about failed queries [Mot86b] Answers to queries which consist of exhaustive lists of values may be represented more succinctly by hierarchical class descriptions [SM87] For example, the query Who works from 9 to 5 may have fTom, Sue, Ann, Maryg as its answer set. If Tom and Sue are all the secretaries in the database ....

A. Motro. SEAVE: A Mechanism for Verifying User Presuppositions in Query Systems. ACM Transactions on Office Information Systems, 4(4), October 1986.

No context found.

A. Motro. SEAVE: The mechanism for verifying user presuppositions. ACM TOIS, 4(4):312--330, October 1986.

No context found.

Amihai Motro. Seave: A mechanism for verifying user presuppositions in query systems. ACM Transactions on Office Information Systems, 4(4):312--330, October 1986.

Online articles have much greater impact   More about CiteSeer.IST   Add search form to your site   Submit documents   Feedback  

CiteSeer.IST - Copyright Penn State and NEC