A SPATIAL QUERY LANGUAGE

FOR PRESENTATION-ORIENTED DOCUMENTS

Ermelinda Oro

DEIS, University of Calabria, Via P. Bucci 41/C, 87036 Rende (CS), Italy

Francesco Riccetti, Massimo Ruffolo

ICAR-CNR, University of Calabria, Via P. Bucci 41/C, 87036 Rende (CS), Italy

Keywords:

Information extraction, Web wrapping, PDF wrapping, Spatial reasoning, Grammars, Chart parsing.

Abstract:

In last years the huge relevance of accessing and acquiring information made available by Web (HTML) pages

and business (PDF) documents has grown much further. In this paper we present a textual query language,

named ViQueL, whose main feature is to identify and extract relevant information from HTML and PDF docu-

ments on the base of their visual appearance by using easy-to-write queries. The proposed language is founded

on spatial grammars, i.e. context free grammars extended by spatial constructs. Despite a considerable ex-

pressive power, combined complexity of ViQueL is in P-Time. Moreover, experiments show that ViQueL is

reasonably efﬁcient for real-life extraction tasks.

1 INTRODUCTION

In the literature is available a large body of work

on formalisms and approaches aimed at manipulat-

ing contents of HTML and PDF documents. Exist-

ing approaches broadly fall in the following main ar-

eas: (i) visual languages aimed at manipulating visual

information for extraction and transformation scopes

(Kong et al., 2006); (ii) web information extraction

approaches that allow for extracting relevant infor-

mation by exploiting mainly the visual appearance of

Web pages (Baumgartner et al., 2001), or their inter-

nal representation (Cafarella et al., 2008); (iii) PDF

wrapping approaches that enable to acquire informa-

tion from PDF documents (Hassan and Baumgartner,

2005); (iv) approaches aimed at manipulating con-

tents of presentation-oriented documents (Adali et al.,

2000; Lee et al., 1999). Nevertheless existing ap-

proaches suffer from the following main drawbacks:

(i) they do not allow for querying the visual structure

of both HTML and PDF documents in a user friendly

way; (ii) they are frequently task oriented so each of

them deal with a speciﬁc problem like record extrac-

tion, table extraction and so on; (iii) frequently their

computational costs are unknown.

In this paper we present a query language, named

ViQueL and based on our previous work (Oro et al.,

2009), that allows for querying both Web and PDF

documents in order to recognize a wide variety of

content structures (e.g. repetitive records, tables,

news, infoboxes, proﬁles, etc) by exploiting their spa-

tial arrangement. The proposed language is founded

on spatial grammars (SGs), i.e. context free grammars

extended by spatial constructs coming from the rect-

angular cardinal relation formalism (Navarrete and

Sciavicco, 2006). ViQueL exploits a spatial docu-

ment model (SDM) in which each document is viewed

as a two-dimensional Cartesian plan on which are

placed rectangles called content items (CIs). A query

on a document is constituted by a set of spatial pro-

duction rules (SPR) of the SG. Basically SPRs de-

scribe how to spatially compose CIs on the plane in

order to identify meaningful content structures. Doc-

ument querying is obtained by parsing the SDM using

SPRs in the query. The parsing strategy is an ad hoc

extension of the CYK parsing algorithm. It is note-

worthy that ViQueL queries can be used for recogniz-

ing a given content structure in different documents

having also different internal encodings. In fact, the

SDM constitutes an abstraction of underlying inter-

nal representation. Experiments on real cases show

how the ViQueL language is pretty much simple and

how it allows users for querying presentation oriented

documents on the base of what they see. Experiments

306

Oro E., Riccetti F. and Ruffolo M..

A SPATIAL QUERY LANGUAGE FOR PRESENTATION-ORIENTED DOCUMENTS.

DOI: 10.5220/0003177603060312

In Proceedings of the 3rd International Conference on Agents and Artiﬁcial Intelligence (ICAART-2011), pages 306-312

ISBN: 978-989-8425-40-9

 2011 SCITEPRESS (Science and Technology Publications, Lda.)

show also that the adopted parsing strategy make the

extraction process reasonably efﬁcient.

The paper is organized as follows. Section 2

presents the spatial document model. In Section

3 syntaxt and semantics of ViQueL is presented

by using some running examples. Section 4 for-

mally presents syntax and semantics of the ViQueL

language, describes the spatial CYK algorithm and

shows languages complexity issues. Section 5 is

about results of experiments. Finally Section 6 con-

cludes the paper and sketches future work.

2 THE SPATIAL DOCUMENT

MODEL

In this paper we subsume documents encoded by

HTML or PDF formats as presentation-oriented doc-

uments (PODs). In the rest of the paper we will as-

sume PODs as represented by the spatial document

model (SDM). Broadly speaking the main idea which

this model is based on is that the area of the screen

aimed at visualizing a POD can be viewed as a 2-

dimensional Cartesian plane on which is arranged a

set of content items (CIs). A CI is an atomic piece

of content (i.e. an images, an alphanumeric string

written with unique font features, a graphical or typo-

graphical element like: a line, a circle etc.) visualized

in a rectangular area of the plane. So the spatial doc-

ument model of a POD consists in a set of CIs which

are formally deﬁned as follows.

Deﬁnition 1. Let T be a set of type names arranged

in a taxonomy, a content item is a 6-tuple of the form:

CI = hτ,σ, r

−

, r

−

, r

where:

• τ ∈ T is the type of the content item (e.g. image,

text, number, percentage, currency, etc).

• σ is the value of the content item (e.g. a string, the

URL of an image, etc).

• The pairs (r

−

, r

−

) and (r

, r

) represent two

points on the Cartesian plane that identify the

bottom-left, and top-right vertices respectively, of

the rectangle that surround the contents σ (see

Figure 1).

Figures 1 and 3 show some CIs computed as de-

scribed in Section 5.

3 QUERYING PODS BY VIQUEL

The scope of this section is to give a by example and

intuitive explanation of ViQueL capabilities and fea-

tures. The language is more rigorously presented and

discussed in following Sections.

Example 1. The example in Figure 1 shows a charac-

teristic Deep Web page in which are contained a set

of repetitive records. In Figure 2 is highlighted one

of the record in Figure 1. The spatial arrangement

of CIs, and the type of their contents, help human

readers to immediately understand the meaning of the

record. For identifying and extracting all records in

that page, the user can pose the following ViQueL

query.

1. S

→ img A

2. A

→ B C

3. B

→ text B

4. B

→ text text

5. C

→ text text

The query is constituted by spatial production

rules of the spatial grammar (see Section 4.2). The

SPR 5 express that a new composed CI (identiﬁed by

the non-terminal symbol C) can be derived when two

CIs of type text are in the spatial relation north (

→).

In Figure 2 such a production compose the name of

the product (ﬁrst terminal text) and its description

(second terminal text). The name of the product, in

fact, is on top (north) of the description. SPRs 3 and

4 allow for recursively identifying sequence of CIs.

So the non terminal B identiﬁes a sequence of CIs in

which each CI is at west (

→) of the others (the be-

havior of the rules is shown in Figure 2 by using the

non-terminal symbol B two times). The SPR 2 allows

for obtaining a new composed CI (represented by the

nonterminal A) by combining non terminals B and C

along the east (

→) direction. Finally, the SPR 1 com-

pletes the records (axiom S) including the picture (ter-

minal img) which is located at west (

→) of A.

Figure 1: The Ebay Web Page with some Highlighted CIs.

Example 2. Figure 3 depicts information in tabular

form contained in a PDF document. The query for

this example is shown in the following.

1. S

→ HEADER BODY

A SPATIAL QUERY LANGUAGE FOR PRESENTATION-ORIENTED DOCUMENTS

307

Figure 2: A Record with Associated CIs.

2. HEADER

→ HEADER text

3. HEADER

→ text text

4. BODY

→ ROW BODY

5. BODY

→ ROW ROW

6. BODY → ROW

7. ROW

→ text DATA

8. DATA

→ percent NUMBERS

9. NUMBERS

→ NUMBERS number

10. NUMBERS

→ number number

The following set of CI types are used: text that

represents plain text, number that represents inte-

ger and ﬂoating point numbers and is is

a of text,

percent that represents percentage and is a sub type

of number.

Figure 3: Table in a PDF Document with Highlighted CIs.

SPRs 9 and 10 (that use nonterminal symbol

NUMBERS) constitute a recursion that allow for cap-

turing composed CIs constituted by sequence of num-

bers (terminal symbol number) along the west direc-

tion (

→). SPR 8 allows for creating composed CIs

(identiﬁed by nonterminal DATA) constituted by the

composition, along the east direction (

→), of a per-

centage (terminal symbol percent) and a composed

CI identiﬁed as a NUMBERS. A row of the table (non-

terminal ROW) is obtained by composing, along the

west direction, a text CI and a composed CI DATA.

SPRs 4, 5 and 6 use non-terminal BODY to identify the

body of the table as a sequence of rows. A BOBY is

a composed CI that can correspond to a single ROW

(SPR 6 that acts as an isa relation) or to a recursive

sequence of ROWs in the south direction (

→). SPRs

2 and 3, based on nonterminal HEADER, identify the

header of the table as a sequence of text-type CIs.

Finally, rule 1 combines header and body in order to

identify the whole table in the axiom S. It is notewor-

thy that tables can be identiﬁed and extracted from

Web documents in the same way. For lack of space we

don’t show here a related example.

4 VIQUEL

In this section we give the formal deﬁnition of

ViQueL. We ﬁrst introduce the preliminary concept

of Rect used for deﬁning the language, then present

language syntax, semantics and complexity issues.

4.1 Rectangular Cardinal Relations

In the Rectangular Cardinal Relations model (RCR)

proposed in (Navarrete and Sciavicco, 2006), let

and r

be two rectangles, spatial relations are

expressed by analyzing the 9 regions (cardinal

tiles) formed by the projections of the sides of a

reference rectangle along the axes of the Carte-

sian plane. By considering cardinal tiles, atomic

RCRs are traditionally expressed by means of the

9 symbols contained in the following set R

card

{B, S, SW, W, NW, N, NE, E, SE}. The sym-

bol B denotes the central tile and the relation be-

longs to. Other symbols in R

card

correspond to the

peripheral tiles and denote the eight RCRs South,

SouthWest, West, NorthWest, North, NorthEast,

East, and SouthEast. Given ρ ∈ R

card

, the expres-

sion r

ρ r

means that r

lies entirely on the tile ρ of

. When a rectangle r

overlaps with several tiles rel-

ative to a reference rectangle r

, we call the relation

that links r

to r

multi-tile relation and represent it by

: ··· : ρ

where ρ

, ..., ρ

are atomic RCRs. Atomic

and multi-tile relations are called Basic RCR. For in-

stance, by considering the RCR r

E:NE r

between

a reference rectangle r

and another rectangle r

, the

RCR relation E:NE means that the rectangle r

lies on

the tiles that are on east and north-east of the rectangle

. In the RCR model, the set R

card

of all basic RCRs

between any couple of MBRs contains 36 elements

(Navarrete and Sciavicco, 2006).

4.2 Language Syntax and Semantics

In the following we introduce the concept of spa-

tial grammar, give language semantics in terms of

the parsing technique adopted for computing ViQueL

queries, and present complexity results.

Deﬁnition 2. A spatial grammar is a 5-tuple of the

form:

SG = (Σ, N, S, R

rec

, Π)

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

308

where: (i) Σ is a set of terminal symbols that corre-

sponds to CI types and identiﬁes all the CIs in a SDM.

(ii) N is a set of nonterminal symbols that identiﬁes

composed content items which structure is in Deﬁni-

tion 3 below. (iii) S ∈ N is the grammar axiom. (iv)

rec

is the set of basic cardinal direction relation in-

troduced in Section 4.1. (iv) Π is a ﬁnite set of spa-

tial production rules (SPRs) of the form A

dir

→ αβ and

A→α, where: A ∈ N, α, β ∈ (Σ ∪ N), and dir ∈ R

rec

Sets Σ, N and R

rec

, are disjoint and ﬁnite.

In order to explain how spatial grammars works

we have to deﬁne the concept of composed content

item (CCI) as follows.

Deﬁnition 3. A composed content item (CCI) is a 6-

tuple of the form:

CCI = hΓ,A, r

−

, r

−

, r

where:

• Γ is a non empty set of spatially contiguous CIs.

• A is the identiﬁer of the CCI corresponding to a

nonterminal symbol of the SG.

• r

−

= min (r

−

|γ ∈ Γ ∧ γ = (τ, σ, r

−

, r

−

, r

))

• r

−

= min (r

−

|γ ∈ Γ ∧ γ = (τ, σ, r

−

, r

−

, r

))

• r

= max (r

|γ ∈ Γ ∧ γ = (τ, σ, r

−

, r

−

, r

))

• r

= max (r

|γ ∈ Γ ∧ γ = (τ, σ, r

−

, r

−

, r

))

CIs in Γ are spatially contiguous when the area de-

ﬁned by r

−

, r

−

, r

do not overlaps with other CIs

not in Γ.

A SPR of the form A→α, where α ∈ Σ, and A ∈ N

allows for creating a CCI corresponding to a CI iden-

tiﬁed by the terminal symbol α. This operation con-

stitutes a generalization of the terminal α into the non-

terminal A, and at the same time a transformation of

the CI related to α into the CCI related to A. Rules of

the form A

dir

→ α β, where α, β ∈ Σ, and A ∈ N, com-

pose the two contiguous CIs related to the terminal

symbols α and β, along the direction speciﬁed by the

relation

dir

→, in order to obtain a new CCI A having the

structure described in Deﬁnition 3. SPRs having the

form A

dir

→ BC, where A, B,C ∈ N, compose the sets Γ

and Γ

of CIs in the CCIs related to the nonterminals

B and C respectively, in order to obtain a new CCI

having coordinates computed as speciﬁed in Deﬁni-

tion 3. Similar considerations can be done for rules

that combine terminal and nonterminal symbols.

4.2.1 The Spatial CYK Algorithm

In the following we present the pseudo-code of the

SCYK algorithm. Computational complexity aspects

are discussed in Section 4.2.2. Before presenting the

algorithm we deﬁne a CNF-like normal form that al-

lows a shortest and more intuitive pseudo-code. It is

obviously possible and easy to extend the algorithm

to parse any type of rules.

Deﬁnition 4. A SG is in SG-normal form if all its

production rules are either in the form A

dir

→ BC, or

A → α where A,B and C are non-terminals, while α

is a terminal symbol. Production of type A → α are

called unary spatial production rules.

The algorithm takes as input a SG Q and a set of CIs

D. In instruction 1 the algorithm creates two ordered

sets L

and L

that contain coordinates r

−

and r

−

all CIs ∈ D respectively.

In the worst case n = |L

| and m = |L

| can be at

most equal to the size of the document |D|, but in real

cases both have a size smaller than |D|. In instruction

4 SPRs in Q are acquired in the set RS. Instruction 6

assigns to RS

all unary SPRs in RS. In instruction

7 non unary SPRs are split in two subsets RS

and

that contain rules of the type V

dir

→ XY , where dir

is a RCR that expresses relations obtained from the

subsets of basic RCR {E, SE, NE, W, SW, NW , B} for

and {N, NW, NE, S, SE, SW , B} for RS

Instructions 8 and 9 generate tables T

and T

re-

spectively. Indices in the ﬁrst four positions of T

and

, namely i

, i

, j

, and j

identify the CCI having as

bottom-left vertex (L

], L

[ j

]) and as top-rigth ver-

tex (L

+ i

], L

[ j

+ j

]). The last position in table

contains a nonterminal symbol.

The general idea which guides the algorithm is

that elements in T

represent CCIs by the correspond-

ing coordinates, while elements in T

are boolean val-

ues that state if a given nonterminal symbol V in the

grammar is associated to the corresponding CCI in T

(it is noteworthy that a CCI can have different asso-

ciated nonterminal symbols). Instruction 10 creates a

two-dimensional table I where elements I[i

, j

] con-

tain a set of couples < i

, j

> which indicate that

the CCI in T

, i

, j

] is not null. Instruction 11

creates the table res that represents the result of the

algorithm (i.e. the set of CCIs that satisﬁes the ax-

iom S in the grammar Q). Instruction 12 initializes

tables T

, T

, and I by using the set D of input CIs

and the set RS

of unary SPRs. The initialization

procedure works as follows: if an area having as ver-

tices (L

], L

[ j

]) and (L

+ i

], L

[ j

+ j

]) con-

tains only one CI, the couple < i

, j

> is added to

I[i

, j

] and T

, i

, j

] is ﬁlled by entering coor-

dinates of that CI. Let α be a CI type (i.e. a terminal

symbol), then for each unary rule V → β, where β

isa α (isa is computed by using the taxonomy of CI

types), element T

, i

, j

,V ] is set to true (such

an operation corresponds to the generation of a CCI

for each initial CI). Remaining values in tables T

, T

and I are computed in the main loop (instructions 13-

A SPATIAL QUERY LANGUAGE FOR PRESENTATION-ORIENTED DOCUMENTS

309

Algorithm 1: Spatial CYK.

Input : A SG Q and a set D of CIs (i.e. the document)

Output: A set of CCIs that satisfy the grammar Q

1 < L

, L

> = createOrderedCoordinateSets(Q);

2 n = |L

3 m = |L

4 RS =getRuleSet(Q);

5 R = getNonTerminalNumber(RS);

6 RS

= getRuleSet(RS);

7 < RS

, RS

> = splitRuleSetByDirection(RS − RS

);

8 createTable T

[n, n, m, m, R];

9 createTable T

[n, n, m, m];

10 createTable I[n, m];

11 createSet res;

12 < T

, T

, I >=initialize(D, RS

);

13 for i

← 1 to n do

14 for j

← 1 to m do

15 for j

← 1 to j

− 1 do

16 indexSet = I[i

, j

];

17 foreach < i

, j

> ∈ indexSet do

18 if j

+ j

≤ m then

19 for i

← i

downto 1 do

20 ver(i

, i

, j

, i

, j

+ j

, j

− j

, T

, RS

, res);

21 end

22 end

23 end

24 end

25 for i

← 1 to j

− 1 do

26 indexSet = I[i

, j

];

27 foreach < i

, j

> ∈ indexSet do

28 if i

+ i

≤ n then

29 for j

← j

downto 1 do

30 ver(i

, i

, j

+ i

, i

− i

, j

, T

, RS

, res);

31 end

32 end

33 end

34 end

35 end

36 end

37 return < res, T

36). CCIs of increasing sizes are discovered by iterat-

ing with the two most external cycle by using indexes

(to increase length) and j

(to increase height) in

instructions 13 and 14 respectively. Two different in-

ternal loops are then used in order to ﬁnd larger CCIs

in horizontal and vertical directions respectively, by

merging contiguous CCIs. In instruction 15 each iter-

ation uses pairs < i

, j

> in the entry I[i

, j

] of table

I (instruction 16) in order to consider each existing

CCI cci

having width equals to |L

+ i

] − L

and height equals to |L

[ j

+ j

] − L

[ j

]|. The inner

Procedure ver(i

, j

, i

, j

, RS, res).

1 p

= Q[i

, i

, j

];

2 p

= Q[i

, i

, j

];

3 if p

6= null then

4 foreach V

dir

→ XY ∈ RS

5 c

= T

, i

, j

, X ] ∧ T

, i

, j

,Y ] ∧

dir p

);

6 c

= T

, i

, j

,Y ] ∧ T

, i

, j

, X ] ∧

dir p

);

7 if c

∨ c

then

8 T

, i

, j

,V ] ← true;

9 update(T

, i

, j

]);

10 if V is axiom then add(res,T

, i

, j

]);

11 end

12 end

13 break;

14 end

cycle (Instruction 19 and procedure veri f y in instruc-

tion 20) takes (if exists) the widest CCI cci

contigu-

ous to cci

. It is necessary to iterate from i

to 1 with

index i

because we have to consider also inclusion

relations (B) between two CCIs. If we don’t con-

sider inclusion relation we can take as contiguous CCI

cci

the one having as corners (L

], L

[ j

+ j3])

and (L

+ i

], L

[( j

+ j3), ( j

− j3)]). If such CCI

exists the algorithm examines each rule V

dir

→ XY ∈

. If values of the two elements in T

referring to

(cci

, X ) and (cci

,Y ) are true and cci

is at dir to

cci

then T

, i

, j

,V ] is set to true and value of

, i

, j

] is set to the proper dimension of the

CCI. If V is the start symbol in Q, the CCI just found

is added to the result set res. Specular operations are

done in the second sub-loop in order to evaluate SPRs

in RS

. At the end the algorithm returns the set res

containing founded CCIs and the table T

that can be

used for generating the query (grammar) parse tree by

a trace-back procedure.

4.2.2 Complexity Issues

Theorem 1. Let D be the SDM of a presentation-

oriented document, the evaluation of a ViQueL

query Q, performed by Algorithm 1, requires space

O(|D|

· |Q|) and time O(|D|

· |Q|), where |D| and |Q|

are the size of the document in terms of CIs and the

size of the query in terms of SPRs respectively.

Proof. Space complexity. Memory usage of the

algorithm corresponds to the size of table T

. In ta-

ble T

, R represents the number of nonterminals in

the query Q, so the space complexity is O(m

· n

· R).

Since the number of non terminals can be at most |Q|,

ICAART 2011 - 3rd International Conference on Agents and Artificial Intelligence

310

and in the worst case we have that m = n = |D|. The

space complexity bound O(|D|

· |Q|) follows. ♦

Proof. Time complexity. Filling ordered sets L

and L

can be done in O(|D| · lg(|D|)). Comparisons

in the algorithm can be all done in constant time

using appropriate data structures. Remember also

that m and n are both bounded by |D|. Operations

concerning splitting of SPRs in Q are done in linear

time, i.e. O(|Q|). Initialization procedure can be

performed in O(|D|

· |Q|). Main loop is clearly the

part of the algorithm that takes more time. Procedure

veri f y is manifestly in O(|Q|). The maximum

number of couple < i

, j

> that can be contained in

a element I[i

, j

] of table I is O(m · n), in the worst

case scenario we have O(|D|

)) because m = n = |D|.

So time complexity in the worst case is computed

as O(n · m · (m

· n · (n · |Q|) + n

· m · (m · |Q|))) =

O(n

· m

· |Q|) , i.e. O(|D|

· |Q|). ♦

5 EXPERIMENTS

In Figure 4 are shown results of experiments carried

out for empirically verify Theorem 1. We ran experi-

ments on a Windows 7 machine with 2,53 GHz Intel

core-duo processor and 4 GB of RAM. We considered

the table and the query in Example 2 and linearly in-

creased their sizes by adding table rows and dummy

rules respectively. Figures 4a and 4b show required

space and time for document sizes that grown from

|D| = 25 to |D| = 1020. Figures 4c and 4d show re-

quired space and time considering query sizes form

|Q| = 10 to |Q| = 108. Curves in the ﬁgures refer to

normalized values and has been drown in loglog scale.

Experiments show that required space in the average

case is linear w.r.t. both the size of Q and D, while

required time, in the average case, is linear w.r.t. the

size of Q and polynomial (with a degree smaller that

in the worst case) w.r.t. the size of D. In Example 2

the system takes about 200 milliseconds for recogniz-

ing the table, so the implemented system results effec-

tively usable in real cases. We have, also, performed

usability experiments by asking 10 user to learn the

language and apply it for extracting tables and data

records from a dataset composed of 5 PDF documents

and 5 web pages. Experiments have shown that the

language is easy to learn and that can by intuitively

applied for real extraction tasks. We don’t give further

details about usability experiments for lack of space.

6 CONCLUSIONS AND FUTURE

WORK

In this paper we presented ViQueL, a spatial

query language that allow for querying presentation-

oriented documents on the base of their visual ap-

pearance. The language is founded on spatial gram-

mars that are obtained by combining classical con-

text free grammars and qualitative spatial reasoning

constructs. The main feature of ViQueL is that it al-

lows for easily deﬁning visual queries that enable to

recognize complex content structures in both HTML

and PDF documents. ViQueL queries are computed

by the SCYK algorithm, a spatial extension of the

well known CYK algorithm. Despite the increased

expressiveness of spatial grammars, the complexity

of the SCYK algorithm remains in P-Time. Further-

more, experiments prove that the algorithm is efﬁ-

cient and usable in real-life cases with satisfactory

results. The proposed approach can be improved by

adopting a stochastic extension to SGs in order to

better manage ambiguous queries. As future work

we intend to apply inductive approaches for learning

ViQueL from portions of documents visually selected

by users. This way no manual code writing will be

required to the users.

Figure 4: Results of Experiments.

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