The Deep Web is constituted by data accessible through Web pages, but not readily indexable by search engines, as they are returned in dynamic pages. In this paper we propose a framework for accessing Deep Web sources, represented as relational tables with so-called access limitations, with keyword-based queries. We formalize the notion of optimal answer and propose methods for query processing. To the best of our knowledge, ours is the first systematic approach to keyword search in such context

Calì, Andrea

Lynch, Thomas W.

Martinenghi, Davide

Torlone, Riccardo

Calì, Andrea

MARTINENGHI, DAVIDE

Archivio istituzionale della ricerca - Politecnico di Milano

Processing keyword queries under access limitations

Martinenghi, D.

Torlone, R.

Name not available

The Deep Web is constituted by data accessible through web pages, but not readily indexable by search engines, as they are returned in dynamic pages. In this paper we propose a framework for accessing Deep Web sources, represented as relational tables with so-called access limitations, with keyword-based queries. We formalize the notion of optimal answer and investigate methods for query processing. We also outline the main ideas of our implementation of a prototype system for Deep Web keyword search

CALI', ANDREA

TORLONE, Riccardo

Archivio della Ricerca - Università di Roma 3

Andrea Calì

Thomas W. Lynch

Davide Martinenghi

Riccardo Torlone

Crossref

Processing Keyword Queries under AccessLimitations?Andrea Cal`ı1,4, Thomas W. Lynch5,1,Davide Martinenghi2, and Riccardo Torlone31Birkbeck, University of London, UKandrea@dcs.bbk.ac.uk2Politecnico di Milano, Italydavide.martinenghi@polimi.it3Universita` Roma Tre, Italytorlone@dia.uniroma3.it4Oxford-Man Inst. of Quantitative FinanceUniversity of Oxford, UK5Reasoning Technology Ltd, UKthomas.lynch@reasoningtechnology.comAbstract. The Deep Web is constituted by data accessible through webpages, but not readily indexable by search engines, as they are returnedin dynamic pages. In this paper we propose a framework for accessingDeep Web sources, represented as relational tables with so-called ac-cess limitations, with keyword-based queries. We formalize the notion ofoptimal answer and investigate methods for query processing. We alsooutline the main ideas of our implementation of a prototype system forDeep Web keyword search.1 IntroductionIt is reasonabe to assume that a user might want or need to query relational datawith keywords – for instance, if he is accessing data whose structure he ignores;in this setting, the user is also free from having to know the query language.This idea has been around for over a decade [3] Since then, a vast corpus ofresearch has been carried out in this field from the point of view of applications(see e.g. [8] for a survey). The problem has been recently formalized in [2], wherea formal approach is provided, which is independent of the organization of datain tables.In this paper we propose an approach to keyword search on so-called DeepWeb sources. The Deep Web is constituted by data that are accessible only ifqueried through a web page, usually by filling in an HTML form. We representDeep Web sources as relational tables; the necessity of filling in the aforemen-tioned form with values forces one to access the relations with suitable selections;these restrictions are referred to as access limitations. It is well known that an-swering relational queries under access limitations requires the execution of a? A preliminary version of this paper was published in the 9th Al-berto Mendelzon Workshop on Foundations of Data Management, 2015,http://ceur-ws.org/Vol-1378recursive query plan [5, 4]. This is also true in our setting, where a recursiveextraction of tuples (or facts) is performed, according to the access limitations,so as to search for the keywords in the data.Our main contributions are the following.– We formally define the notion of keyword queries under access limitations,and the notion of answer (Section 2).– We propose an algorithm for processing keyword queries under access limi-tations (Section 3).– We discuss the computational complexity of our algorithm, showing that theproblem of computing answers to keyword queries in our setting is tractable(Section 3).– We outline the main features of a prototype system that processes keywordqueries in the Deep Web (Section 4).2 Problem definitionBasics. We model data sources as relations of a relational database and weassume that, albeit autonomous, they have “compatible” attributes. For this,we assume that the attributes of relations are defined over a set of abstractdomains D = {D1, . . . , Dm}, which, rather than denoting concrete value types(such as string or integer), represent data types at a higher level of abstraction(for instance, car or country). The set of all values is denoted by D = ⋃ni=1Di.In the following, we shall denote by R(A1, . . . , Ak) a (relation) schema, bydom(A) ∈ D the domain of an attribute A, by r a relation over R, and byr = {r1, . . . , rn} a (database) instance of a database schema R = {R1, . . . , Rn}.Access limitations. An access pattern Π for a schema R(A1, . . . , Ak) is a mappingsending each attribute Ai into an access mode, which can be either input oroutput; Ai is correspondingly called an input (resp., output) attribute for R wrt.Π. For ease of notation, we shall mark input attributes with an ‘i’ superscript todistinguish them from the output ones. Let A′1, . . . , A′l be all the input attributesfor R wrt. Π; any tuple 〈c1, . . . , cl〉 such that ci ∈ dom(A′i) for 1 ≤ i ≤ l is calleda binding for R wrt. Π. An access α consists of an access pattern Π for a schemaR and a binding for R wrt. Π; the output of such an access α on an instance ris the set T = σA1=c1,...,Al=cl(r). Intuitively, we can only access a relation if wecan provide a value for every input attribute. Given an instance r for a databaseschema R, a set of access patterns Π for the relations in R, and a set of valuesC ⊆ D, an access path (for R, Π and C) is a sequence of accesses α1, . . . , αnon r such that each value in the binding of αi, 1 ≤ i ≤ n, either occurs in theoutput of an access αj with j < i or is a value in C. A tuple t in r is said to bereachable if there exists an access path P such that t is in the output of someaccess in P ; the reachable portion reach(r,Π, C) of r is the set of all reachabletuples in r given the values in C.2t31 t33t23t11t21t12(a) Access pathst31 t33t23t11t21t12(b) Join grapht31 t33t23t11t11A2A1(c) AnswersFig. 1. Example 1: Reachable portion, corresponding join graph, and answers.Keyword queries. A keyword query is a set of values in D called keywords.Example 1 Consider a query q = {k1, k2}, a schema (with access patterns Π)R = {R1(Ai1, A2), R2(Ai2, A1), R3(Ai1, A2, A3)}, and an instance r such thatr1 =Ai1 A2k1 c1 t11c2 c3 t12r2 =Ai2 A1c1 c2 t21c4 c2 t22c1 c6 t23r3 =Ai1 A2 A3c2 c1 k2 t31c5 c4 k2 t32c6 c7 k2 t33Figure 1(a) shows the reachable portion of r given the values in q along with theaccess paths used to extract it, with dotted lines enclosing outputs of accesses.Given a set T of tuples, the join graph of T is a node-labelled undirected graphT = 〈N,E〉 constructed as follows: (i) the nodes N are labelled with tuples ofT , and (ii) there is an arc between two nodes n1 and n2 if the tuples labellingn1 and n2 have at least one value in common.Example 1 (cont.) The join graph of reach(r,Π, q) is shown in Figure 1(b).Definition 1 (Answer). An answer to a keyword query q against a databaseinstance r over a schema R with access patterns Π is a set of tuples A inreach(r,Π, q) such that: (1) each c ∈ q occurs in at least one tuple t in A;(2) the join graph of A is connected; (3) for every subset A′ ⊆ A such that A′enjoys Condition 1 above, the join graph of A′ is not connected.It is straightforward to see that there could be several answers to a keywordquery; below we give a widely accepted criterium for ranking such answers [8].Definition 2. Let A1,A2 be two answers of a keyword query q on an instancer of size |A1| and |A2| respectively; we say that A1 is better than A2, denotedA1  A2, if |A1| ≤ |A2|. The optimal answers are those of minimum size.Example 1 (cont.) The sets A1 = {t11, t31} and A2 = {t11, t23, t33} are an-swers to q; A1 is better than A2 and is the optimal answer to q.33 Keyword-based answering in the Deep WebWe now present a vanilla algorithm to discuss the computational complexity ofanswering a keyword query q in the deep Web modeled as an instance r of aschema R with access patterns Π. Example 1 shows that, in the worst case,we need to extract the whole reachable portion to obtain the tuples involvedin an optimal answer. In fact, s = reach(r,Π, q) is actually a connected joingraph, since every tuple in it is in some output of some access path startingfrom the values in the query (see for example Figure 1.a), but further paths mayexist between tuples in s (see Figure 1.b). Therefore, query answering requiresin general two main steps, described in Algorithm 1: (i) extract the reachableportion s of r; (ii) if possible, remove tuples from s so that the obtained setsatisfies the conditions of Definition 1, while minimizing its size.Algorithm 1: Computing an optimal answer (Answer(q,Π, r))Input: Keyword query q, access patterns Π, instance r over ROutput: Answer A1. A := reachablePortion(r,Π, q); // see Algorithm 22. if A does not contain all values in q then return nil;3. else prune(A, q); // see Algorithm 34. return A;A simple way of extracting the reachable portion, inspired by the proceduredescribed in [4], is shown in Algorithm 2. This algorithm may be allowed toterminate early if the answer is not required to be optimal (flag ω set to false),and thus can stop as soon as the reachable portion contains all the keywordsin the query. This is coherent with the distinct root-based semantics of keywordsearch in relational databases, which provides a tradeoff between quality of theresult and efficiency of the method to evaluate it [8].Algorithm 2: Reachable portion (reachablePortion(r,Π, q))Input: Instance r over R, access patterns Π, initial values qFlag: boolean ω // if ω = true the answer is guaranteed to be optimalOutput: Reachable portion RP1. RP := ∅; C := ∅;2. while an access can be made with a new binding b for some R ∈ R wrt. Π using values in C ∪ q3. O := output of access to r over R with binding b;4. RP := RP ∪ O; // cumulating all the obtained tuples into RP5. C := C ∪⋃A∈R,t∈O{t(A)}; // cumulating all the obtained values into C6. if C ⊇ q ∧ ¬ω then break;7. return RP ;Basically, determining an optimal answer from the reachable portion cor-responds to finding a Steiner tree of its join graph [8], i.e., a minimal-weight4subtree of this graph involving a subset of its nodes. An efficient method forsolving this problem in the context of keyword search over structured data ispresented in [6], where a q-fragment can model our notion of answer. Yet, whenoptimality is not required, a simple technique (quadratic in the size of r) toobtain an answer (steps 2–6 of Algorithm 3) consists in trying to remove anytuple from the set as long as it contains all the keywords and remains connected.Algorithm 3: Pruning (prune(T , q))Input: Set of tuples T , keyword query qFlag: boolean ω // if ω = true the answer is guaranteed to be optimalOutput: Minimal set of tuples T1. if ω then return a minimal subtree of the join graph of T that contains q;2. T ′ := T ; T ′′ := ∅;3. while T ′′ 6= T ′4. T ′′ := T ′;5. for each t ∈ T ′′ if T ′ \ {t} is connected and T ′ \ {t} ⊇ q then T ′ := T ′ \ {t};6. return T ′;The extraction of the reachable portion of an instance r with access lim-itations can be implemented by a Datalog program over r [4], which can beevaluated in polynomial time in the size of the input [7]. In addition, in [6] itis shown that the optimal q-fragments of r can be enumerated in ranked-orderwith polynomial delay, i.e., the time for printing the next optimal answer is againpolynomial in the size of r. Hence, we can state the following preliminary result.Theorem 1. An optimal answer to a keyword query against a database instancewith access limitations can be efficiently computed under data complexity.4 ImplementationWe have implemented a prototype version of a system for processing keywordqueries over a set of Deep Web sources. Given the nature of the search, we extractdata on-the-fly and store them in main memory, conveniently represented ingraph format. The keyword search algorithms are run on the extracted data inmain memory rather than in a DBMS; the nature of keyword query processingin this setting suggests that relying on a relational DBMS does not provide anysignificant advantage.The system is based on an underlying relational layer of Deep Web data calledDataplex 1. The Dataplex framework was developed in Racket, a language of theLisp/Scheme family. In Dataplex, the only field type is text (string), and tablesare seen as containers with interface methods. The main idea behind Dataplexis that facts can have lists as arguments; facts belong to shapes, that are theequivalent of single-relation schemata. A shape is created with the call1 http://thomaswlynch.com/liquid/liquid-doc/liquid/doc/liquid/index.html5(dataplex:create-shape name length )and a tuple can be placed into the created container by(shape:insert name value list ).Notice that in Dataplex data are stored, logically speaking, in two structures:one that holds the values, and the other that bridges references to these values;indices are used to improve performances in the accesses to data in Dataplex.We are currently running experiments on keyword search by using the Dat-aplex framework, which we will publish soon. As it is natural to predict, thebottleneck in the efficiency of query processing is the extraction of data fromthe sources.5 Discussion and future workIn this paper we provided preliminary insights on keyword search in the DeepWeb. As future work on the problem, we plan to:– devise optimization strategies for query answering; in particular, identifyconditions under which an optimal answer can be derived without extractingthe whole reachable instance;– leverage known values (besides the keywords), modeled as relations with onlyone (output) attribute, to speed up the search for an optimal answer;– study the problem in a scenario in which the domains of the keywords areknown in advance: in this case schema-based techniques can be used;– consider the case in which nodes and arcs of the join graph are weighed tomodel source availability and proximity, respectively.Acknowledgments. Andrea Cal`ı and Thomas Lynch acknowledge support bythe EPSRC grant “Logic-based Integration and Querying of Unindexed Data”(EP/E010865/1).References1. A.V. Aho, C. Beeri, and J.D. Ullman. The theory of joins in r elational databases.ACM Trans. on Database Syst., 4(3):297314, 19792. R. Torlone. Towards a new Foundation for Keyword Search in RelationalDatabases. AMW, 2014.3. V. Hristidis, Y. Papakonstantinou. DISCOVER: Keyword Search in RelationalDatabases. VLDB, pages 670–681, 2002.4. A. Cal`ı, D. Martinenghi. Querying Data under Access Limitations. In ICDE,pag. 50–59, 2008.5. C. Li, E. Y. Chang. Answering queries with useful bindings. ACM Trans. DatabaseSyst., 26(3), pages 313–343, 2001.6. B. Kimelfeld and Y. Sagiv. Finding and approximating top-k answers in keywordproximity search. In PODS, pag. 173–182, 2006.7. M.Vardi.The complexityof relational querylanguages. InSTOC,pag.137–146,1982.8. J. Xu Yu, L. Qin, and L. Chang. Search in Relational Databases: A Survey. IEEEData Eng. Bull., 33(1): 67–78, 2010.6

Processing Keyword Queries Under Access Limitations

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Processing keyword queries under access limitations

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