Open-Domain Attribute-Value Acquisition
from Semi-Structured Texts
Naoki Yoshinaga1 and Kentaro Torisawa2
1

Japan Society for the Promotion Science,
6 Ichibancho, Chiyoda-ku, Tokyo, 102-8471, Japan,n-yoshi@jaist.ac.jp
2
Japan Advanced Institute of Science and Technology,
1-1, Asahidai, Nomi, Ishikawa, 923-1292, Japan,
torisawa@jaist.ac.jp

Abstract. This paper proposes an unsupervised method that acquires aset of attribute-value pairs (avps, e.g., ⟨director, W. Wyler⟩) for a given
object (e.g., “Ben-Hur”) from semi-structured HTML documents. The
objects’ avps are one of the principal components of domain ontologies.
We ﬁrst acquire class attributes that are used by many web authors to
describe the objects’ avps. Then, we exploit the acquired class attributes
to induce patterns for extracting avps from web pages. Experimental
results show that, with our method, at least one set of correct avps
are acquired for 67.7% of objects among open-domain class-object pairs
whose source documents (web pages) include the objects’ avps in layouts.
Key words: open-domain attribute-value acquisition, semi-structured
texts, question answering, faceted search

1Introduction

This paper proposes an unsupervised method that extracts a set of attributevalue pairs (avps, e.g., ⟨director, W. Wyler⟩) for a given object (e.g., “Ben-Hur”)
from the web. Here, attributes of an object are the words (e.g., director) thatrefer to those aspects of the object in which people are interested (e.g., “a person
who directed a ﬁlm object”).
Objects’ avps have been studied as one of the principal components of domain ontologies [1], and have been found beneﬁcial in a wider range of nlp/ir
applications such as question-answering (Q&A) [2] and faceted search [3]. The
automatic extraction of object’ avps has been attempted by several nlp researchers [4, 2, 5] to build a knowledge base for Q&A systems (e.g., Q: “Tell me
the height of Mt. Everest.”— A: “8,848m”). Objects’ avps can also provide orthogonal categories of the objects (e.g., “ﬁlms whose director is W. Wyler” —“Roman Holiday”, “Ben-Hur”, ...), and such categories can play the role of facets
in faceted search (e.g., the Flamenco system3 ), which receives much attention
from ir researchers these days.
3

http://flamenco.berkeley.edu/

2Naoki Yoshinaga, Kentaro Torisawa

(1)

(2)

(3)

1950’ Selected films

Academy Awards Nominations

Film info. – Ben-Hur

1. Ben-Hur (1959)
list-casting/ Charlton Heston,Jack Hawkins, …
-director/ William Wyler
This film is taken in …
2. Roman Holiday (1953)-casting/ Gregory Peck,
Audrey Hepburn-director/ William Wyler

This page summarizes nominations for
Academic Awards for Best Picture.

-starring: C. Heston-DVD Price: $19.96 list
The details of the film:

tableYear Title
Director
Casting… …
…
…
1953 Ben-Hur W. Wyler C. Heston…
1953 Shane
G. Stevens A. Ladd,…
… …
…
…

director: W. Wyler
story: K. Tunberg
awards: 11 Oscars
runtime: 212 min.

list

(4)
List of films by William Wyler
Mrs. Miniver (1942)
Starring G. Garson, W. Pidgeon
Plot
When a Jewish prince isoutline betrayed…
table
Ben-Hur (1959)
Starring C. Heston, J. Hawkins
Plot
The Minivers, an English
outline "middle class“ …

Fig. 1. Web pages that show Ben-Hur ’s avps

The aim of this study is to acquire avps for a wide range of objects inan unsupervised manner. Our procedure ﬁnds speciﬁc layouts from the web(tables or lists, as shown in Fig. 1) that describe objects, and then extracts
avps for the objects from these layouts. The key idea in our avp acquisition
is to use attributes of classes, to which the objects belong, as clues to induce
regular expression patterns for extracting avps for objects from the layouts. We
acquire class attributes which are used by many web authors to describe theclass’s objects in the layouts by using an author-aware scoring function calledsite frequency. Our method is applicable to several types of web layouts, as longas the layouts include the acquired class attributes.
The rest of this paper is organized as follows. Section 2 describes our methodof acquiring avps from web pages. Section 3 reports experimental results. Section 4 discusses previous approaches to avp acquisition. Section 5 concludes thispaper and mentions future directions of this study.

2

Proposed method

In this section, we describe our method of acquiring avps for a given object(e.g., “Ben-Hur”). Currently, we assume that the name of a class to which thegiven object belongs is also given (e.g., “ﬁlm”). The class name is needed toﬁnd attributes for the objects through the class name. In the future, we willautomatically assign class names to input objects by using large-scale opendomain class-object databases [6].
Our avp acquisition consists of two phases. First, we acquire attributes for agiven class. Second, using the acquired class attributes, we ﬁnd web pages thatare likely to include the object’s avps and then extract avps from these pages
by using automatically induced regular expressions.
In what follows, we ﬁrst describe a method for acquiring attributes for a givenclass, and then present a method for acquiring avps for objects in the class.
2.1

Attribute Knowledge Base Construction

We acquire class attributes by basically following the procedure proposed by
Yoshinaga and Torisawa [7], which consists of the following three steps, namely:

Open-Domain Attribute-Value Acquisition

3– “X(の)Y ” (Y of X), where Y ∈ { リスト/一覧/名簿/目録 (list), 図鑑/案内/ガイド (guide), データ (data), 名鑑 (directory), データベース (database), カタログ
(catalog), 情報 (info.), 評価 (rating), ランキング (ranking), 選び (choice), レポー
ト (report), 解説 (note), アーカイブ (archive), 概要 (summary), 紹介 (intro.), 詳
細 (detail), 感想 (impression), 特集 (focus)– X について (About X)– X (which appears as a suﬃx of the title string)

Fig. 2. Queries to collect pages from which we acquire attributes of class X (Japaneseterms used in our experiments are followed by English translations)<H3>Ben-Hur(year:’59)</H3>Director/ W. Wyler<BR />The details:<TABLE><TR><TD>Starring</TD><TD>C. Heston, J. Hawkins</TD></TR><TR><TD>Runtime</TD><TD>212 min.</TD></TR></TABLE>

Fig. 3. Emphasized attributes in a web page
Table 1. HTML tags and text decorations used for identifying attributes

HTML tags: TD, TH, LI, DT, DD, B, STRONG, FONT, SMALL, EM, TT
Brace: 〔-〕, 【-】, 《-》, ［-］, 〈-〉, ＜-＞, [-] <->Preﬁx: *, ＊, ●, ○, ■, □, ・, ◆, ◇, ★, ☆, ◎, ・, ○ , ◎Suﬃx: ：, :, ／, /, ＝i) sampling pages that are likely to include class attributes, ii) extracting classattribute candidates and iii) ﬁltering out erroneous attribute candidates. In thefollowing, we describe each step in detail.
Sampling Web Pages from the Web: We ﬁrst prepare web pages fromwhich we extract class attributes. We collect web pages that show catalogs or
speciﬁcations of the objects because these pages often contain attributes of theclass objects, as shown in pages 3 and 4 in Fig. 1. In practice, we retrieved
at most 2,000 pages that included strings referring to typical captions for thecatalogs or speciﬁcations of the class objects (e.g., “list of ﬁlms”, Fig. 2), on thepositions of the page title (regions enclosed by title tags).
Extraction of Attribute Candidates: In the web pages retrieved in thissection, the attributes are likely to be emphasized in visually distinguishable
ways through HTML tags and symbolic decorations, as illustrated in Fig. 3.
We employ such HTML tags and symbolic decorations as clues to acquire classattributes from the web pages. More precisely, we regard noun sequences that are
surrounded by particular HTML tags or braces, preceded by preﬁxes or followed

4

Naoki Yoshinaga, Kentaro Torisawa
C の A が (A of C [verb]), C の A に (at/in A of C), C の A より (from/than A of C)
C の A は (A of C [verb]), C の A で (by/with A of C), C の A から (from A of C)
C の A を ([verb] A of C), C の A へ (to A of C), C の A まで (even/until A of C)

Fig. 4. Pattern queries to eliminate irrelevant attributes A for class C

by suﬃxes, to be candidate attributes. We used patterns based on HTML tags
and symbolic decorations shown in Table 1 to recognize candidate attributes.
Filtering Acquired Class Attributes: We use the statistics and hit counts
of a commercial search engine to ﬁlter out erroneously acquired attributes, andthen obtain a ﬁnal set of attributes that includes attributes used by many web
authors to describe objects in the class.
We then deﬁne a site frequency for each acquired attribute A, which directly
reﬂects the number of authors who used that attribute to describe objects inthe class, and rank the attributes according to site frequency to use the top n
attributes in the following avp acquisition:
Deﬁnition 1 (Site Frequency (SF)). The sf for an attribute A extracted
from a set of web pages D is the number of distinct authors of pages d ∈ D.
In this study, we consider that web pages whose URLs share the same host nameand directories are described by a single author. We then calculate the sf of A
as the number of variations in the host name + directories of URLs of web pages
from which the attribute A is extracted.
We further ﬁlter out erroneously acquired attributes from the ranked list of
the candidate attributes. For each candidate attribute A for the class C, we
obtain hit counts for queries that express lexico-syntactic patterns for classattribute relation such as “C の A (A of C)”. We employed the patterns shownin Fig. 4, which are used by Tokunaga et al. [8]. When the sum of hit counts for
all the queries is 0, we remove the attribute from the candidate attributes.
2.2

Automatic AVP acquisition from the Web

Using class attributes acquired with the method described in Section 2.1, we
extract avps for a given object according to the steps shown in Fig. 5. The basic
idea is to induce regular expressions for extracting avps from layouts by using
the contexts in which the class attributes appear.
Our avp acquisition consists of the following steps. we ﬁrst retrieve web pages
that are likely to include avps for a given object (Step 1). We next enumerateregions that include objects’ avps (avp blocks) from each page retrieved in Step
1 (Step 2). We then recognize symbolic patterns for emphasizing attributes ineach block, and then induce patterns to extract each avp from that block (Step3). We ﬁnally apply the pattern induced in Step 3 to each block and acquire
avps from it (Step 4). In what follows, we describe each step in detail.

Open-Domain Attribute-Value Acquisition

5

Input: Ben-Hur,
Hur, Film
Class Attribute KB
Film: director, starring, …
Novel: author , publisher, …

Query: “Ben-Hur”
Hur” “Film”“director” OR “starring” OR …
Step1: Web page retrieval
Best films by W.
W Wyler
1. Ben-Hur
Hur (1959)
starring/ Charlton Heston,Jack Hawkins, …
writers/ Karl Tunberg,
Tunberg …
This film is taken in …
2. Roman Holiday (1953)
starring/ …

Step2: AVP block extraction
AVP blocks starring/ Charlton Heston,Jack Hawkins, …
writers/ Karl Tunberg, …
This film is taken in …

Step3: Pattern induction(.+?)/ (.+?)#
Attribute = $1
Value = $2

Step4: Pattern match
AVPs<starring, Charlton Heston, Jack Hawkins><writers, Karl Tunberg, …>

Fig. 5. Attribute-driven avp Acquisition

Step 1. Collecting pages that include AVPs: To collect web pages that arelikely to include avps for the input object, we use a commercial search engine
with a query consisting of the object, the class, and the class attributes combinedwith ‘OR’ operators (see Fig. 5).
Step 2. Identifying AVP blocks: In Step 2, we identify tables or lists that
describe the object’s avps in the web pages retrieved in Step 1. The pages may
even include layouts that describe avps for other objects (e.g., those for “Roman
Holiday” in page 1 and “Mrs. Miniver” in page 4 of Fig. 1). We search where
objects and their avps appear in web pages, and hypothesize the positional
relations between objects and layouts that describe the avps for them as follows:
Hypothesis I: avps for a given object are likely to appear concentrated in asmall number of regions (avp blocks) enclosed by HTML block tags. An avp
block describing an object should include attribute words, and also include
or closely follow the object name.
We process each occurrence of the object name in the page from ﬁrst to last, anditeratively collect innermost blocks enclosed by block tags such that the blocks
include the input attributes preceded by the appearance of the object name as

6Naoki Yoshinaga, Kentaro Torisawa

candidate avp blocks. For example, on page 3 of Fig. 1, when we have “BenHur” as an input object and “director” and “starring” as the class attributes, we
obtain two blocks that describe avps: a list with “starring” and “DVD price”,
and a list with “director”, “story”, “awards”, and “runtime”.
Step 3. Inducing AVP Extraction Patterns: We then induce patterns for
extracting avps from each avp block collected in Step 2. To design the induction
procedure, we made the following assumption:
Hypothesis II: In an avp block, an attribute immediately precedes its value,
and another avp immediately follows those values. When attributes in an
avp block are emphasized by some HTML tags, braces, preﬁxes or suﬃxes,
the same symbols are used to emphasize other attributes in that block.
Following this hypothesis, our procedure induces a regular expression as a
generic pattern for extracting attributes and their values. The procedure ﬁrst applies to the avp block the same patterns that we used for extracting attributes(in Section 2.1), and utilizes the matched strings that include the class attributes
to identify HTML tags or symbolic cues that are used to emphasize attributes inthe block. For example, when we have ‘starring’ and ‘runtime’ as class attributes
and process an avp block of the ﬁrst listing in page 3 of Fig. 1, we obtain thematched string ‘-starring:’, which contains the class attribute surrounded by thesymbolic decorations ‘-’ and ‘:’. By replacing the class attribute in this stringwith a wildcard that matches the shortest string, we acquire a regular expression/-(.+?):/ for extracting attributes in this block. A value for an attribute is astring that spans from immediately after the corresponding attribute to just before another attribute. By putting the special character ‘#’ before the attributes
in the block, we thus obtain a generic pattern for extracting attributes and values
in this example as /-(.+?):(.+?)#/.
The induced pattern can extract avps for attributes other than the class attributes the procedure used (e.g., “DVD price”, “director”, “story”, and “awards”
in page 3 of Fig. 1). We can thereby extract all the avps in the block even when
we have only one class attribute included in the avp block.
Step 4. Acquiring AVPs from AVP blocks: We iteratively acquire avps
from each avp block by applying to the block the extraction pattern induced forthe block in Step 3. We merge all the avps acquired from the blocks and returnthe resulting avps. When the pattern extracts the same attributes twice, westop the acquisition and return the acquired avps until that point. This is done
because, when the same attribute appears twice in the page, it usually expresses
an avp for diﬀerent objects.
The above process (also Hypothesis II) assumed that the avp block is described by a list. When we fail to recognize avps from avp blocks enclosed by thor td tags (which express a single cell in a table), we attempt to recognize avps
from the whole table structure. We can extract avps from those tables by regarding a row or a column that contains the maximum number of class attributes as
expressing attributes. We omit further details due to space limitations.

Open-Domain Attribute-Value Acquisition

3

7

Experiments

In this section, we report some experimental results of our avp acquisition. We
ﬁrst prepared open-domain class-object pairs in Japanese, and then acquired
avps for these objects with our method.
3.1

Experimental Settings

We obtained a development/test set of class-object pairs from hyponymy relations automatically acquired from the deﬁnition sentences in Wikipedia [6]4 .
These hyponymy relations included not only class-subclass relations but also
various class-instance relations. We sorted hypernyms in the hyponymy relations
according to the number of hyponyms, and selected the top 1,000 hypernyms to
obtain major hypernyms with a large number of hyponyms. We manually eliminated hypernyms from those hypernyms when they were too abstract (e.g., “形式(style)”, “銘柄 (brand)”). We also replaced hypernyms with more general hypernyms when they were too speciﬁc (e.g., “フランスの作曲家 (French composer)”→ “作曲家 (composer)”) or when they referred to a role/relation associated withother objects (“関連会社 (aﬃliate company)” → “会社 (company)”). The numberof resulting hypernyms was 620. Next, we picked up objects from the hyponymsof the 620 hypernyms. Since the resulting hyponyms included not only instances
but also concepts which do not have speciﬁc values for their attributes, we automatically eliminated from those hyponyms those which cannot be instances.
More precisely, we eliminated hyponyms when they also appeared as hypernyms
in the original hyponymy relations, or when the hit count of a query “どの X(which X)” was greater than 0 since generic concepts appear in this pattern(e.g., “which car”) [9]. We thereby obtained open-domain class-object pairs.
3.2

Experimental Results

We then acquired attributes for the 620 classes in the above class-objects pairs.
We used Yahoo! API5 to collect web pages for acquiring class attributes (Section 2.1). We randomly split the 620 classes into two sets with the same size, andused one as the development set and the other as the test set. Since the main
aim of the following experiments was to show the utility of the class attributes
in the avp acquisition task, we analyzed the performance of avp acquisition forclasses for which reliable class attributes were available. We observed that theacquired attributes with a larger site frequency (sf) were more reliable thanthose with a smaller sf. To obtain classes that are likely to have at least one
reliable class attribute, we then sorted the test classes according to the value ofthe largest sf among those for the attributes acquired for each class, and thenused the top 20 classes for evaluation (left column in Table 2). We note that theresulting classes included various classes that had few common attributes. Five
45

http://ja.wikipedia.org/
http://developer.yahoo.co.jp/

8

Naoki Yoshinaga, Kentaro Torisawa
Table 2. Top six class attributes acquired from the Webclass
武器 (weapon)

acquired attributes
攻 撃 力 (oﬀensive power), 名 称 (name), 武 器 名 (weapon name),
備考 (remarks), 価格 (price), ブーメラン (boomerang)
会社 (company)
資本金 (capital fund), 事業内容 (description of business), 所在地 (location),
代表者 (representative), 従業員数 (# employee), 設立 (foundation)
機関 (agency/organization) 電話番号 (phone number), 所在地 (location), 電話 (phone), 住所 (address),
医療機関名 (name of medical institution), 診療科目 (department)
ホテル (hotel)
住 所 (address), ホ テ ル 名 (name of hotel), チェック イ ン (check-in),
駐車場 (parking), チェックアウト (check-out), 料金 (rate)
選手 (player)
生年月日 (birthdate), 背番号 (uniform number), ポジション (position),
身長 (height), ＧＫ (goal keeper), 監督 (manager)
公園 (park)
所 在 地 (location), 駐 車 場 (parking), 電 話 (phone), 住 所 (address),
交通 (transportation), 場所 (place)
車両 (wheeled vehicle)
年 式 (model year), メ ー カ ー (manufacturer), グ レ ー ド (grade),
走行距離 (mileage), 排気量 (engine displacement), 仕様 (speciﬁcation)
行事 (event)場所 (place), 日時 (date), 内容 (content), 会場 (venue), 講師 (lecturer),
参加費 (entrance fee)
営業所 (sales oﬃce)
住所 (address), 営業時間 (oﬃce hour), 本社 (head oﬃce), 電話番号 (phone
number), 所在地 (location), 電話 (phone)
規則 (rule)
施行期日 (eﬀective date), 趣旨 (point), 目的 (purpose), 経過措置 (interim
measure), 定義 (deﬁnition), 委任 (commission)
医者 (medical doctor)
院長 (director), 副院長 (assistant director), 整形外科 (orthopedic surgery),
内科 (internal medicine), 外科 (surgery), 専門分野 (expertise)
食品 (foodstuﬀ)
価格 (price), 内容量 (volume), 日時 (date), 商品名 (product name),
ダイエット (diet), 原材料 (material)
制度 (institution)
電話 (phone), 問い合わせ先 (contact info.), 電話番号 (phone), 対象者 (object
person), 参考 (reference), 出版社 (publisher)
ドラマ (TV drama)
出 演 (casting), 脚 本 (story), 発 売 日 (release date), 価 格 (price),
原作 (original), 日時 (date)
会議 (meeting)
日 時 (date), 場 所 (location), 電 話 (phone), 事 務 局 (organizer),
出席者 (participants), 会場 (venue)
ソフトウェア (software)
発売日 (release date), 価格 (price), 日時 (date), 販売価格 (sales price),
メーカー (manufacturer), 商品名 (product name)アニメ (animated movie)
監督 (director), 日時 (date), カテゴリー (category), 原作 (original),
価格 (price), 音楽 (music)
思想 (ideology)
カテゴリー (category), メーカー (maker), 著者 (author), 発売日 (releasedate), 目次 (content), 出版社 (publisher)
活動 (activity)
日時 (date), 場所 (place), 電話 (phone), 活動内容 (activity content),
内容 (content), 会場 (venue)
アルバム (album)
発 売 日 (release date), 価 格 (price), 作 詞 (lyric), Ｃ Ｄ (CD),
初回限定盤 (limited edition), 作曲 (composition)

objects were randomly chosen from the objects in the class-object pairs, and we
obtained 100 test class-object pairs.
As we have described in Section 2.1, we use the top n attributes ranked by thesf, and we set n = 6 here. Table 2 shows the top six attributes used for acquiringavps. We obtained meaningful attributes for 18 classes other than “medical doctor” and “ideology”. We failed to acquire correct attributes for “medical doctor”
because objects in this class often appear together with objects in other classes(“hospital”). We failed to acquire correct attributes for “ideology” because “思想 (ideology)” often appeared in the substrings of names of objects present inother classes (e.g., ‘book’).
We applied our method to the 100 class-object pairs and acquired avps forthem. In Step 1, we retrieved the top ten pages by using Yahoo! API. Due to a

Open-Domain Attribute-Value Acquisition

9

Table 3. Number of pages where the objects’ avps were acquiredN◦ of pages w/ avps in layouts
N◦ of pages w/o avps in layouts
total

correct avps incorrect avps avps not acquired total
9722
90 2090
21483 5049743
573 713

download failure, we processed on average 7.13 pages for each pair after Step 2(713 pages in all).6 We successfully acquired avps for 140 pages.
A human subject was then asked to evaluated our avp acquisition. The subject ﬁrst determined whether each of the 713 pages included the layouts describing avps for the given objects, and the subject judged that 209 pages had layoutsthat described the objects’ avps.7 The subject next analyzed these layouts to
identify attributes, values, and their correspondences, and then checked whether
the avps acquired from the page are the same as those he recognized.
This check allowed us to evaluate our avp acquisition by using the following
two criteria: 1) we ﬁrst count the number of pages from which we acquired
correct avps to show the accuracy/coverage of our method to acquire avps fromeach page, and 2) then count the number of class-object pairs for which we
could acquire correct avps from at least one page to address the coverage of our
method for (open-domain) objects.
Coverage/Accuracy of our AVP acquisition from web pages: Table 3
shows the number of pages for which we acquired correct avps for the given
objects. Our method extracted correct avps from 97 web pages in terms of avp
acquisition from the pages, the coverage is 46.4% (97/209) and the accuracy is
69.3% (97/140). We acquired in total 683 avps from these 97 web pages (onaverage 7.04 avps per page). Table 4 shows the detailed analysis for all the avps
extracted from the 140 pages. In the table, the column corr shows the numberof pages for which all the acquired avps are correct (the same as the ones the
subject identiﬁed), while the column corr* shows the number of pages for which
all the acquired attributes are correct, but some of their values include irrelevant
strings together with the correct values (e.g., a string “William Wyler This ﬁlm
is taken in ...” as a value for the director of “Ben-Hur” in page 1 of Fig. 1). In
this analysis, we consider (a set of) avps judged as corr or corr* as correct
avps when we obtain the above-mentioned coverage and accuracy. The column
attr incorr shows the number of pages for which the method extracted avps
from appropriate layouts, but the attributes are recognized incorrectly, while the6

We excluded web pages of Wikipedia since objects in the test set were obtained from
Wikipedia entries and we will easily obtain avps from those pages.
7
We asked the subject to consider that there were layouts for avps when at least one
attribute-value relation for the objects could be identiﬁed by any visually distinguishable ways other than sentences. Although most cases were covered by our cuesin Table 1, a few exceptions included tables expressed using spaces in plain texts.

10

Naoki Yoshinaga, Kentaro Torisawa
Table 4. Classiﬁcation of pages where the objects’ avps were acquired
class
corr
corr* attr incorr obj incorr total
武器 (weapon)
4
1
5
1 11
企業 (company)*
14
0
2
0 16
機関 (organization/agency)
1
0
0
2
3
ホテル (hotel)*
8
2
1
0 11
選手 (player)*
3
0
0
0
3
公園 (park)*
5
0
0
3
8
車両 (wheeled vehicle)
0
0
0
1
1
行事 (event)
2
0
0
3
5
営業所 (sales oﬃce)
1
0
2
0
3
規則 (rule)
100
12
医師 (medical doctor)
1
0
0
3
4
食品 (foodstuﬀ)*
13
1
1
0 15
制度 (institution)
1
0
2
1
4
ドラマ (TV drama)*
5
3
0
2 10
会議 (meeting)
1
0
1
1
3
ソフトウェア (software)*
2
1
0
2
5
アニメ (animation movie)*
10
1
2
2 15
思想 (ideology)
3
0
0
3
6
活動 (activity)*
1
0
0
1
2
アルバム (album)*
6
6
0
1 13
total
82(58.6%) 15(10.7%)
16(11.4%) 27(19.3%) 140
best-10 (*)
67(68.4%) 14(14.3%)
6(6.1%) 11(11.2%) 98

column obj incorr shows the number of pages from which we extracted avps
for irrelevant objects. When we focus on the best 10 classes (marked * in thetable), the accuracy reaches 82.7% (81/98).
Coverage of our AVP acquisition for open-domain objects: We thendetermined the number of class-object pairs for which correct avps are acquired.
For the ﬁve objects in the 20 classes, the subject judged that, for 65 objects,
there is at least one page that includes the objects’ avps. In order to show thecoverage of our method in terms of avp acquisition for the objects, we calculated
the number of pairs for which correct avps are acquired from at least one page.
From the web pages for these 65 pairs, we could acquire at least one correct
set of avps for 44 objects (67.7%). We also determined the classes for which we
could acquire the objects’ correct avps. We acquired correct avps for at least one
object in 19 classes (95%), namely, all the classes other than “wheeled vehicle”.
We note that, when we extracted avps from more than one page for one pair, theprobability that we acquired the correct avps drastically increases. For 35 classobject pairs for which we extracted avps from more than one page, we acquired
correct avps for 33 pairs (94.2%). These results show that we can acquire avps
with high accuracy/coverage by selecting the target classes appropriately.

Open-Domain Attribute-Value Acquisition

4

11

Related WorkIn this section, we ﬁrst review existing approaches to acquire attributes of classes,
and then mention other work to obtain avps from (web) documents.
4.1

Automatic attribute acquisition

Tokunaga et al. [8] acquired attributes for a given class by applying lexicosyntactic patterns to normal texts. Paşca and Durme [10, 11] have recently proposed a method that extracts class attributes from massive query logs by using
seed objects and seed attributes. However, it is unclear whether attributes extracted from normal texts or query logs are useful for extracting avps in layouts.
We have found some cases where, to refer to the same attributes, simpler terms
are preferred, at least in normal texts, while more technical terms are used inlayouts (e.g. to refer to “outside dimension” of “digital camera”, “サイズ (size)”
is preferred in normal texts, while “外形寸法 (dimension)” is often used in layouts). It is also diﬃcult for people who are not employed by a commercial searchengine vendor to obtain a large set of query logs, and it is desirable to have amethod to acquire avps from freely available resources such as web pages.
4.2

Automatic AVP acquisition

Brin [12], Etzioni et al. [4], and Paşca et al. [13] proposed semi-supervised methods that used seed data to obtain patterns for acquiring avps from texts. Sekine
et al. [5] used supervised learning to generate patterns for ﬁnding values of manually tailored attributes from texts. Kushmerick [14] advocated a supervised
wrapper induction for general information extraction from semi-structured texts
such as HTML documents, and avp acquisition can be seen as a special case ofthis task. Although these (semi-)supervised approaches have been used to collectavps in a pre-deﬁned domain, avps acquired with our method will help thosemethods to acquire avps for open-domain objects.
Takahashi [2] acquired objects’ avps using lexico-syntactic patterns. However, it is extremely diﬃcult to design such patterns for some value types such as
sentences (e.g., values of “plot outline” for “ﬁlm”). Another problem is anaphoraor omission in texts. Objects are often anaphorically referred to or often omitted
in sentences (e.g., “I watched Ben-Hur on TV. The director was W. Wyler”).
Chen et al. [15] and Yoshida et al. [16] proposed unsupervised methods for ﬁnding avps from HTML tables in a certain domain. However, because tables do
not always include the object names together with the avps (e.g., “Ben-Hur” inpage 4 in Fig. 1), we need to ﬁnd the objects in the pages for which the tables
describe avps. These studies did not address this problem, while we solved it byusing the class attributes and heuristics.

5

Conclusion

This paper proposed an unsupervised method that acquires attribute-value pairs(avps) for a wide range of objects from layouts on the web. We exploit class

12

Naoki Yoshinaga, Kentaro Torisawa

attributes acquired from the web, in order to induce patterns for extracting avps
from the layouts. For each set of ﬁve objects in the 20 open-domain classes, we
successfully extracted at least one set of correct avps for 44, or 67.7%, of the
65 objects whose source documents (web pages) included the objects’ avps. Our
method has been proven to be useful for acquiring avps for certain classes.
We will extend our method to handle other languages including English. We
plan to apply our method to large-scale class-object relations [6], in order toconstruct a large-scale avp database. We will exploit that database to support
Q&A systems and to build a faceted search system for a wide range of objects.

References
1. Lenat, D.B., Guha, R.: Building Large Knowledge-Based Systems: Representation
and Inference in the CYC Project. Addison-Wesley (1990)
2. Takahashi, T.: Computation of Semantic Equivalence for Question Answering.
PhD thesis, Nara Institute of Science and Technology, Nara, Japan (2005)
3. Broder, A., Maarek, Y., eds.: Proc. of the SIGIR Workshop on Faceted Search.
Springer (2006)
4. Etzioni, O., Cafarella, M., Downey, D., Kok, S., Popescu, A.M., Shaked, T., Soderland, S., Weld, D.S., Yates, A.: Web-scale information extraction in KnowItAll(preliminary results). In: Proc. of WWW. (2004) 100–110
5. Sekine, S., Sudo, K., Ando, M.: Encyclopedia QA system using automatic discovery
of attribute value and question sentence patterns. In: Proc. of the Annual Meetingof Natural Language Processing. (2005) (in Japanese).
6. Sumida, A., Torisawa, K.: Hacking Wikipedia for hyponymy relation acquisition.
In: Proc. of IJCNLP. (2008) to appear.
7. Yoshinaga, N., Torisawa, K.: Finding speciﬁcation pages according to attributes.
In: Proc. of WWW. (2006) 1021–1022
8. Tokunaga, K., Kazama, J., Torisawa, K.: Automatic discovery of attribute words
from web documents. In: Proc. of IJCNLP. (2005) 106–118
9. Sumida, A., Shinzato, K., Torisawa, K.: Concept-instance relation extraction from
simple noun sequences using a search engine on a web repository. In: Proc. of
the ISWC workshop on Web Content Mining with Human Language Technologies.
(2006)
10. Paşca, M., Durme, B.V.: What you seek is what you get: Extraction of classattributes from query logs. In: Proc. of IJCAI. (2006) 2832–2837
11. Paşca, M.: Organizing and searching the World Wide Web of facts - step two:Harnessing the wisdom of the crowds. In: Proc. of WWW. (2007)
12. Brin, S.: Extracting patterns and relations from the World Wide Web. In: Proc.
of the WebDB workshop at EDBT. (1998)
13. Paşca, M., Lin, D., Bigham, J., Lifchit, A.: Organizing and searching the World
Wide Web of facts - step one: the one-million fact extraction challenge. In: Proc.
of AAAI. (2006) 1400–1405
14. Kushmerick, N.: Wrapper induction: Eﬃciency and expressiveness. Artiﬁcial Intelligence 118(1–2) (2000) 15–68
15. Chen, H.H., Tsai, S.C., Tsai, J.H.: Mining tables from large scale HTML texts.
In: Proc. of COLING. (2000)
16. Yoshida, M., Torisawa, K., Tsujii, J.: A method to integrate tables of the World
Wide Web. In: Proc. of WDA. (2001)