### Table 1 Effects entered into individual conjunction analyses Conjunction Description Sustained

2005

"... In PAGE 7: ...i.e., W2 N W12 or W12 N W2), there were six conjunctions in all. Table1 lists the individual effects that were entered into each conjunction. There were several constraints imposed on each test.... ..."

### Table 1 presents an overview on the main evalu- ation measures computed for all 8 runs. At a first glance it is visible, that the topic refined queries show a considerable improvement over the base- line runs. The precision gain is most visible at the P10 measures. If we compare the results with respect to the initial query length, we see the title- and-description runs always ahead of the title-only, but the difference becomes smaller for the refined queries. Hence, the refinement strategy helps the most in the case of short queries, which are typical for common search engine users.

2006

"... In PAGE 7: ...4220 0.3880 Table1 : Result overview: title-only queries (base1) vs. title-and-description (base2) tential of the structural information for these type of retrieval tasks.... ..."

Cited by 1

### Table 2: Ways that similar query strings can differ.

2007

"... In PAGE 4: ... It has been shown that traditional vector space measures of similarity are generally unsuitable for finding query similarity [15]. To understand how to identify re-finding, we explored a number of potential differences between similar queries, enumerated in Table2 . Most of the differences listed are trivial to identify automatically, but some are not.... In PAGE 4: ... Previous work has identified over-lapping click queries as likely to be related in meaning, and therefore useful for clustering queries [25]. To find the optimal normalization our system automatically tested all 2049 possible combinations of the 12 top transformations from Table2 to find the minimal set of transformations that generated normalization-equivalence between each query pair. More than one transformation was often necessary to generate equivalence.... ..."

Cited by 3

### Table 2: Ways that similar query strings can differ.

2007

"... In PAGE 4: ... It has been shown that traditional vector space measures of similarity are generally unsuitable for finding query similarity [15]. To understand how to identify re-finding, we explored a number of potential differences between similar queries, enumerated in Table2 . Most of the differences listed are trivial to identify automatically, but some are not.... In PAGE 4: ... Previous work has identified over-lapping click queries as likely to be related in meaning, and therefore useful for clustering queries [25]. To find the optimal normalization our system automatically tested all 2049 possible combinations of the 12 top transformations from Table2 to find the minimal set of transformations that generated normalization-equivalence between each query pair. More than one transformation was often necessary to generate equivalence.... ..."

Cited by 3

### Table 1: Common operators of description logics

"... In PAGE 2: ... The expressive power of a description logic is in the constructions allowed in T-boxes: which derived no- tions can be de ned? Let apos;s see an example. The following is a valid pair h T-Box, A-box i in the description logic FLEUR de ned in Table1 below (with disjointness axioms). T-BOX: T = 8 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; lt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; : Man : = Human u Male u Rest-Man Woman : = Human u Female u Rest-Woman Father : = Man u 9 1:Child Loves : = Child t Rest-Love dis(Man; Woman) 9 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; = gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; ; A-BOX: A = 8 gt; gt; gt; lt; gt; gt; gt; : Human(m) Human(e) Child(m;e) Male(m) Rest-Man(m) Rest-Love(m; e) 9 gt; gt; gt; = gt; gt; gt; ; The rst three formulas in T de ne concepts (sub- sets of the domain to which the represented infor- mation refers).... In PAGE 2: ... Description logics are interpreted on interpreta- tions I = ( I; I), where I is a non-empty do- main, and I is an interpretation function assigning subsets of I to concept names, binary relations over I to role names and single elements of I to atomic constants. Table1 list operations which appear in di erent description logic, together with their notation and semantics. The logic FL? [4] is de ned as the description logic allowing universal quanti cation, conjunction and unquali ed existential quanti cations of the form 9R: gt;.... In PAGE 3: ...Table 1: Common operators of description logics tion, while N T F is the extension of T F with nega- tion of atomic concept names. The names in paren- thesis in Table1 are the usual ones for de ning ex- tensions. For example, ALC is AL extended with full negation.... In PAGE 5: ... In this way ALC-concepts are completely character- ized by ALC-simulations. In [13, 14] the authors provide, given a set of concept-forming operations from Table1 that are admissible in a description logic L, the cor- responding relation of L-simulation between L- interpretations, as well as suitable analogs of Theo- rem 3. Hence, an explicit and exact characterization of the concepts expressible in a given description logic L is given.... In PAGE 6: ... We have characterized ( rst-order de nable) op- erations on roles (as conjunction, disjunction) us- ing these extended simulation techniques. Charac- terization results for all interesting subsets of the concept- and role-forming operations presented in Table1 are now available. To substantiate this claim, we present a sample: we describe a two- sorted simulation for ALCR that allows us to char- acterize both the concepts and roles that are de n- able in ALCR.... ..."

### Table 1: Common operators of description logics

"... In PAGE 2: ... The expressive power of a description logic is in the constructions allowed in T-boxes: which derived no- tions can be de ned? Let apos;s see an example. The following is a valid pair h T-Box, A-box i in the description logic FLEUR de ned in Table1 below (with disjointness axioms). T-BOX: T = 8 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; lt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; : Man : = Human u Male u Rest-Man Woman : = Human u Female u Rest-Woman Father : = Man u 9 1:Child Loves : = Child t Rest-Love dis(Man; Woman) 9 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; = gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; ; A-BOX: A = 8 gt; gt; gt; lt; gt; gt; gt; : Human(m) Human(e) Child(m;e) Male(m) Rest-Man(m) Rest-Love(m; e) 9 gt; gt; gt; = gt; gt; gt; ; The rst three formulas in T de ne concepts (sub- sets of the domain to which the represented infor- mation refers).... In PAGE 2: ... Description logics are interpreted on interpreta- tions I = ( I; I), where I is a non-empty do- main, and I is an interpretation function assigning subsets of I to concept names, binary relations over I to role names and single elements of I to atomic constants. Table1 list operations which appear in di erent description logic, together with their notation and semantics. The logic FL? [4] is de ned as the description logic allowing universal quanti cation, conjunction and unquali ed existential quanti cations of the form 9R: gt;.... In PAGE 3: ...Table 1: Common operators of description logics tion, while N T F is the extension of T F with nega- tion of atomic concept names. The names in paren- thesis in Table1 are the usual ones for de ning ex- tensions. For example, ALC is AL extended with full negation.... In PAGE 5: ... In this way ALC-concepts are completely character- ized by ALC-simulations. In [13, 14] the authors provide, given a set of concept-forming operations from Table1 that are admissible in a description logic L, the cor- responding relation of L-simulation between L- interpretations, as well as suitable analogs of Theo- rem 3. Hence, an explicit and exact characterization of the concepts expressible in a given description logic L is given.... In PAGE 6: ... We have characterized ( rst-order de nable) op- erations on roles (as conjunction, disjunction) us- ing these extended simulation techniques. Charac- terization results for all interesting subsets of the concept- and role-forming operations presented in Table1 are now available. To substantiate this claim, we present a sample: we describe a two- sorted simulation for ALCR that allows us to char- acterize both the concepts and roles that are de n- able in ALCR.... ..."

### Table 1: Common operators of description logics

"... In PAGE 2: ... The expressive power of a description logic is in the constructions allowed in T-boxes: which derived no- tions can be de ned? Let apos;s see an example. The following is a valid pair h T-Box, A-box i in the description logic FLEUR de ned in Table1 below (with disjointness axioms). T-BOX: T = 8 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; lt; gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; : Man : = Human u Male u Rest-Man Woman : = Human u Female u Rest-Woman Father : = Man u 9 1:Child Loves : = Child t Rest-Love dis(Man; Woman) 9 gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; = gt; gt; gt; gt; gt; gt; gt; gt; gt; gt; ; A-BOX: A = 8 gt; gt; gt; lt; gt; gt; gt; : Human(m) Human(e) Child(m;e) Male(m) Rest-Man(m) Rest-Love(m; e) 9 gt; gt; gt; = gt; gt; gt; ; The rst three formulas in T de ne concepts (sub- sets of the domain to which the represented infor- mation refers).... In PAGE 2: ... Description logics are interpreted on interpreta- tions I = ( I; I), where I is a non-empty do- main, and I is an interpretation function assigning subsets of I to concept names, binary relations over I to role names and single elements of I to atomic constants. Table1 list operations which appear in di erent description logic, together with their notation and semantics. The logic FL? [4] is de ned as the description logic allowing universal quanti cation, conjunction and unquali ed existential quanti cations of the form 9R: gt;.... In PAGE 3: ...Table 1: Common operators of description logics tion, while N T F is the extension of T F with nega- tion of atomic concept names. The names in paren- thesis in Table1 are the usual ones for de ning ex- tensions. For example, ALC is AL extended with full negation.... In PAGE 5: ... In this way ALC-concepts are completely character- ized by ALC-simulations. In [13, 14] the authors provide, given a set of concept-forming operations from Table1 that are admissible in a description logic L, the cor- responding relation of L-simulation between L- interpretations, as well as suitable analogs of Theo- rem 3. Hence, an explicit and exact characterization of the concepts expressible in a given description logic L is given.... In PAGE 6: ... We have characterized ( rst-order de nable) op- erations on roles (as conjunction, disjunction) us- ing these extended simulation techniques. Charac- terization results for all interesting subsets of the concept- and role-forming operations presented in Table1 are now available. To substantiate this claim, we present a sample: we describe a two- sorted simulation for ALCR that allows us to char- acterize both the concepts and roles that are de n- able in ALCR.... ..."

### Table 1: Results of ranked queries on XML data using traditional similarity measures. All results are averages over 10 queries.

2002

"... In PAGE 12: ... Due to the small size of this collection and the fact that the correct results are clearly identi ed, this collection was used for much of the initial experimentation and for development of the methods, but it is arti cial. Table1 shows the outcome of queries on the XML data using traditional ranking methods: the inner product, normalised inner product, and cosine similarity measures. The rst column shows the method used.... ..."

Cited by 17

### Table 2: Comparison of the Models on Arbitrary Boolean and Purely Conjunctive Queries. Conjunctive Arbitrary

2000

"... In PAGE 12: ... The only di erence is that the connective between two attributes was selected as either a disjunction or a conjunction by ipping a fair coin. Table2 compares results on arbitrary and purely conjunctive queries (nQ is the query length, tP , Ct and eP are the average online time, query count and error across 200 runs of the algorithms). The maxent models again enjoy a distinct advantage in accuracy over the independence models.... ..."

Cited by 23

### Table 2: Semantics of n-ary Conjunctive Queries

2006

"... In PAGE 3: ... Query semantics. Let D be the data graph the query Q is evaluated over and q the number of variables occurring in Q, then Table2 gives the precise semantics of n-ary conjunctive queries over graphs as used in this article. The semantics is defined based on sets of valuations for query variables.... ..."

Cited by 1