DEIM Forum 2014 A8-2

Discovery of Surrounding Fact Information
Based on Fact Adjacency Relationships
Meng ZHAO† , Hiroaki OHSHIMA† , and Katsumi TANAKA†† Department of Social Informatics, Graduate School of Informatics, Kyoto University
Yoshida Honmachi, Kyoto, 606–8501 Japan
E-mail: †{zhao,ohshima,tanaka}@dl.kuis.kyoto-u.ac.jp
Abstract During daily web surfing, users encounter vast quantities of information everyday and at most time justpass by. It maybe because there is no more time for further search, or even users did not notice the information
at all. However, among those missed information, there are some meaningful pieces. In this paper, we propose asystem that given a fact, surrounding information of the fact are discovered, and then ranked according to cognition
diﬀerence with context formed by surrounding facts. The objective of our system is to help users eﬃciently findrelated and useful information, reducing users

searching cost.

Key words adjacency relationships, context cognition

1. Introduction

meet his need. Especially, 3). and 4). state in a more general way, while 5). shows the comparison of the Vitamin C

Nowadays, web has become the primary access to acquireamount between lemon and acerola. In a broader sense, 6).

various information. Everyday we encounter vast quantities

also provides useful information about lemon, since we knowof information, but only a small portion of them is viewed bylemon is a kind of citrus fruits so that we can easily infer that

us. This does not mean the rest is insignificant. The reasons“Lemons are full of fiber and important minerals”. Besides,

we miss those significant information may be because evenit does not mean that such kind of information is meaninglessthough we realize their importance, there is no more time forfor those who stop further search. Perhaps they just cannot

deep search. Or we are not aware of them at all. Suppose

catch its significance as an entrance to the wider knowledge

during daily web surfing, the following information is readenvironment which leads to their unawareness. Thus, forby chance.

both those who stop there and those who regard “Lemons
Lemons are rich in Vitamin C.

are rich in Vitamin C” as a starting point and begin to try
finding more information, information like the above listed

Depending on one’s next move, users can be divided into

in the example helps them acquire more useful and related

two types: those who stop there and those who regard it asknowledge indeed.

a starting point and begin to try finding more information.

From the above discussion, we summarize three distinct

Information such as the following ones are considered to berelationships: variable, generalization · specialization,

meaningful for the latter.

conditional negation · counterevidence. (See details in1). Lemons are rich in Citric acid.

Section 4.) All of them are referred to as adjacency rela-2). Acerola is known for being extremely rich in Vitamintionships here. Briefly speaking, variable corresponds tothe situation that an entity is substituted by another entity.

C.
3). Citrus fruits are full of Vitamin C.

In the above example, “Citric acid” substitutes “Vitamin

4). Fresh food is the best source of Vitamin C and mostC” in 1). Generalization · specialization corresponds tofruits and vegetables contain Vitamin C.
5). Lemons contain no more Vitamin C compared toacerolas.

the situation that an entity is substituted by its hypernymor hyponym, i.e. “citrus fruits” substitutes “lemons” in 3).
Conditional negation · counterevidence corresponds to6). Citrus fruits are full of fiber and important minerals.

the situation that contrast and contradiction occur, i.e. 5).

Take an insight into the above enumerative information. If

In this paper, we take a sentence fact as a unit and propose

one is interested in lemon, then 1). and 5). meet his need.

a method to find its surrounding facts in accordance with the

Or if one is interested in Vitamin C, then 2)., 3)., 4). and 5).

distinct relationships mentioned before. （注 1） Besides, we alsoof a given sentence. In [7] and [8], Yamamoto et al. propose

introduce a context-aware ranking method to rank them soa system called “Hondo? Search” to help users determine

as to present more important facts in the upper.

the trustworthiness of uncertain facts based on sentiment

The remainder of the paper is organized as follows. In Sec-and temporal viewpoints. With the help of some additionaltion 2., we discuss related work. Section 3. shows the overall

data, users can judge an uncertain fact by themselves. Unre-

workflow of this work, while Section 4. states the details

liability arising from temporal changes is also taken accountof adjacency relationships between facts. In Section 5., weof. In [5], they improve Hondo? Search to not only col-introduce the method to generate candidate facts by using

lect comparative facts of the input fact, but only provide

dictionary and extract valid ones from the Web. In Section

users important aspects for comparison. Especially, in ad-6., our context-aware ranking method is illustrated. Finally,

dition to an uncertain fact whose credibility one wants to

Section 7. concludes the paper and gives an outline of ourcheck, a verification target should also be indicated by user.

future work.

For example, “Lemons are rich in Vitamin C” as the uncertain fact and “Lemons” as the verification target. In these

2. Related Work

three work, lexical-syntactic patterns of the sentence substi-

2. 1 Contradiction detection in nature language

tutions are used to extract comparative facts from the Web.
Compared with our work, we apply lexical-syntactic patterns

processing
In nature language processing field, some work focuses

to get both comparative facts and conditionally negationalon detecting contradiction between two sentences. In [3],ones. Besides, dictionaries containing hypernym/hyponym

Harabagiu et al. concentrate on three forms of linguistic

and coordinate terms are also used to generate candidate

information - negation, antonymy, semantic and pragmatic

related facts.

information, employ a machine learning approach and pro-

2. 3 Search by sentence queries

vide the first empirical results for contradiction detection.

In [6], Yamamoto and Tanaka concentrate on improving

However, Marneﬀe et al. [1] advocate that contradictions aresearch results responded by sentence queries. They advocate

not limited to these constructions and broader extent should

two cases in which satisfied search results cannot be obtained

be covered. As a result, they propose 9 types for contradic-

by sentence queries: (1) the meaning of sentence is correcttion and categorize them as those occurring via antonymy,

but its expression is rare on the Web; (2) what the sentence

negation, date/number mismatch and those arising from thedescribes is a misunderstanding by user. In their example,

use of factive words, structural and subtle lexical contrasts,

given a sentence query, i.e. “Germany is famous for beer”,

and world knowledge. Although some types of contradiction

phrases such as “Belgium is famous for beer”, “Munich is fa-

are beyond their system’s capability, they get good perfor-

mous for beer” are suggested as substitutions of input query.

mance on types arising from negation and antonymy. Given

Based on the criteria that sentence substitutions which ap-（注 2）

developed by National

pears frequently on the Web and whose context is similar to

Institute of Information and Communications Technologythat of the input sentence query should be ranked higher,

a topic term, WISDOM system（注 3）, can extract contrast sentences from a set ofa ranking algorithm is also stated. Our variable is similar

documents without any training data. This is also the es-

to [6] in this respect. However, we also consider sentence sub-sential diﬀerence compared to [1] and [3]: the objective ofstitution based on hypernym/hyponym. Besides, our ranking

WISDOM system is to find related keywords of a given topic

algorithm is context-aware, which means a sentence’s rank

and present primary, contrast sentences to users, while [1]depends on users’ acceptability of other sentences.

(NICT)and [3] aim at detecting relations between two sentences.
All of them are based on analysis of part-of-speech (POS)
tagging.

3. Overview
In this section, overview of our work is described. We aim

2. 2 Credibility judgment

at discovering surrounding fact information of a user-given

Although one can get a vast amount of information fromfact, while the surrounding is determined by three adjacency

the Web, there mix some unreliable information. To assist

relationships - variable, generalization · specialization, con-users in easily distinguishing reliable information from unre-ditional negation · counterevidence. We believe with theliable one, some work has been done to judge the credibility

help of those information, users are able to easily and eﬃciently obtain meaningful pieces of information of their input（注 1）：In this paper, we only deal with the former two relationships.
（注 2）：http://kc.nict.go.jp/project1/WISDOM TR.pdf（注 3）：http://www.nict.go.jp/en/index.html

fact. Figure 1 shows the overview by using an example fact
“Lemons are rich in Vitamin C”.
The one and only input for our proposed algorithm is a fact

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Figure 1 Overview.

Figure 2 An example of input and output.

top one is {citrus fruit, vitamin c, rich}, the second one is{lemon, pectin,rich}. Moreover, concentrated on the keyword set {acelora, vitamin c, rich}, we can also find thatits detailed facts such as “Acerola berries are rich in natural

one wants to know more about. In general, if one wants toVitamin C” and “Acerola, also known as Barbados cherry,know more about a fact, it is necessary for him to search byis remarkably rich in vitamin C, carotenoids, and bioactive

this fact and check search engine’s response. And not least,

flavonoids, each possessing strong antioxidant capacity” arehe may need to change his queries several times in order toalso attached.

get what he desires for. Here a fact is input as a sentence,such as “Lemons are rich in Vitamin C” shown in Figure 1.

4. Fact Adjacency Relationships

After a fact is input, at the very beginning, morphologicalAccording to the Oxford Dictionary （注 5）, a fact is a thing

analysis is taken to get a keyword set from it, i.e. {lemon,that is known or proved to be true. Based on this definition,

vitamin c, rich}. Then according to the source from where

we hypothesize a fact should meet either the following con-a surrounding fact candidate is obtained, two diﬀerent ways

ditions: (1) What the fact states is accepted by most people.

are employed:

In other words, it appears frequently on the Web. (2) Whata). Using dictionary information such as hyper-

the fact means is true. We apply three distinct relationships

nym/hyponym to generate surrounding fact candidates frombetween facts to obtain surrounding facts of a given one.

the input fact.

Details for each relationship are discussed as follows.

b). Applying web search to get surrounding fact candidates.

4. 1 Variable
Given a fact, we suppose that its surrounding facts can be

For the former case, incorrect combinations may arise be-

generated by replacing an entity. Here an entity indicates acause of simple hypernym/hyponym substitution. For ex-

noun keyword extracted from the given fact. For example,

ample, since “DHA” is a coordinate term of “Vitamin C”, ain the fact f =“Lemons are rich in Vitamin C”, there aresubstitution can be generated as {lemon, DHA, rich} which

two entities - “lemon” and “vitamin c”. Thus, fact “Acelora

means lemons are rich in DHA. But in fact, lemons containberries are rich in Vitamin C” has the same lexico-syntactic

no DHA at all. That is to say, entity combination of “lemon”

pattern as that of f (X are rich in Vitamin C), while factand “DHA” is impossible. Thus, it should be removed be-“Lemons are rich in fiber” also has the same lexico-syntactic

fore fact keyword set ranking. Besides, similar keyword setspattern as that of f (Lemons are rich in X). We regard thisare merged. Finally, in order to show users more meaning-kind of substitutions of an entity as “variable”.

ful and important surrounding facts in the upper, keyword

4. 2 Generalization · Specialization

sets obtained from the above steps are ranked by consider-

Given a fact, we suppose that facts generalized or special-

ing their context（注 4）. We also provide the details of eachized the meaning it has are also its surrounding facts. Take

keyword set for users’ further reading. Figure 2 shows an

the fact f =“Citrus fruits are rich in Vitamin C” as an exam-

output image of the input fact “Lemon is rich in Vitaminple. For the term “vitamin c”, the term “vitamin” is one of

C”. We can see there return some keyword sets, such as theits hypernym, as Vitamin C is a kind of Vitamin. Therefore,
fact “Citrus fruits are rich in Vitamin” is a generalization of（注 4）：What we say “context” here does not indicate the paragraphor document from which the fact keyword set is extracted.

（注 5）：http://www.oxforddictionaries.com

f , since it provides a broader description about citrus fruits
to users. On the other hand, for the term “citrus fruit”, theWe use WordNet （注 6） to generate candidate keyword sets
of fact f and verify their possibilities as follows:

term “lemon” is one of its hyponym, as lemon is one of the（ 1 ） The given fact f is divided into words by taking

citrus fruits. Therefore, fact “Lemons are rich in Vitaminmorphological analysis. Then according to defined stopword

C” is a specialization of f , since it gives users a detailed ex-

list, some words are omitted. The remaining words are de-ample of citrus fruits. We regard these kinds of substitutions

noted as Kf = {k1 , k2 , ..., kn } and we call Kf the keywordof an entity as “generalization · specialization”.

set of fact f .

4. 3 Conditional negation · Counterevidence（ 2 ） For keyword ki in Kf , if it is a noun, replace it by

Given a fact, we suppose its surrounding facts should be

its direct hypernym, direct hyponym, or sister term in Word-

also included those who has a negative or contrastive impli-

Net. If it is a verb, replace it by its sister term. If it is an

cation of it. For example, when the fact f =“Lemons are richadjective, since in this paper we skip conditional negationin Vitamin C” is shown to users, they can catch the meaning· counterevidence relationship, only replace it by adjectives- rich Vitamin C contained in lemons. However, it is hardwhich is semantically similar to it, in other words, “sisterto know its rich degree. If fact “Lemons contain no moreterm” in WordNet.

Vitamin C than acelora berries” is presented, then users are（ 3 ） Verify the possibility of keyword combination byable to have a more clear understanding about lemons in thequerying the Web.

viewpoint of Vitamin C content. Since simple negation of

Ks = {k1′ , k2 , ..., kn′ }, where ki′ indicates a replacement ofa fact, i.e. “Lemons are not rich in Vitamin C”, is always

ki from Kf by using WordNet. Its combination possibil-

incorrect, we only consider negation of a fact with some con-ity is calculated as p(Ks ) =ditions, say “conditional negation”. Besides, it is likely that

HitCount(k1′ ∧ k2 ∧ ... ∧ kn′ ) is the hitcount by issuing query

the input fact is actually a misunderstanding by users. In“k1′ ∧ k2 ∧ ... ∧ kn′ ” to the Web, Qs indicates the query set

this case, presentation of counterevidences is considered to be

Qs = {k2 ∧ ... ∧ kn−1 , ..., k1′ ∧ ... ∧ kn′ } and r is the num-

essential. We regard the above surrounding as “conditionalber of replaced keywords compared to Kf . Actually, here

negation · counterevidence”.

5. Finding Surrounding Fact Keyword
Sets

Given a substitution keyword set′′
HitCount(k1∧k2 ∧...∧kn),
1
HitCount(Qs )
C
r

where

r−1

we aim at estimating the possibility of the combination of
the r replacements. For example, if Kf ={lemon, vitaminc, rich} and Ks ={lemon, DHA, rich}, then for this Ks , its
Qs ={lemon ∧ rich, DHA ∧ rich}. We check the possibility

Given a fact f , we wish to present its surrounding facts sof f by adjacency relationships. We define this as f Θs. Theof the combination of “lemon” and “DHA”. Only Ks whose
combination possibility is above a threshold is remained.

goal in this section is to gather surrounding fact keyword sets

5. 2 Web-search-based approachS = {s|f Θs}. As we mentioned in Section 3., depending on

Given a fact f , we first refine it to a keyword set Kf . Thenthe source from where a surrounding fact is obtained, twoaccording to morphological analysis, only nouns (regarded as

diﬀerent ways are employed. See below.

entities) are selected for variable transformation. We search

5. 1 Dictionary-based approach

the Web to gather candidate surrounding keyword sets Ks

Given a fact f , we first refine it to a keyword set Kf . The

as follows:

keywords are the ones which consist of the fact and are not（ 1 ） Morphological analysis is applied to the input fact

stopwords. Then according to lexical category of each key-f . Only nouns, verbs and adjectives are extracted. The sameword, substitutions using dictionary information are taken

as what we state before, we denote the extracted keywords

place. At this time, incorrect combinations may arise be-

as Kf = {k1 , k2 , ..., kn }.

cause of simple substitutions so that a verification process is（ 2 ） For each noun in Kf , we regard it as an entity. Doc-conducted to filter all possible combinations. For example,

uments denoted as Doc(Ks ) which may contain surrounding

given a fact f =“Lemons are rich in Vitamin C”, its cor-facts of f are extracted by issuing query “(∗) ∧ k2 ∧ ... ∧ kn ”

responding keyword set is {lemon, vitamin c, rich}. Since

to a conventional Web search engine. In this example, k1 isterm “citrus fruit” is a hypernym of “lemon”, a substitu-

an entity but maybe not the only one. To simplify the prob-

tion is {citrus fruit, vitamin c, rich}. Similarly, since termlem, we only consider one entity replacement by asterisk at“DHA” is a coordinate term of “vitamin c”, a substitution is{lemon, DHA, rich}. However, “lemons are rich in DHA” isnot true, so we remove the substitution {lemon, DHA, rich}.
The substitution {citrus fruit, vitamin c, rich} together withother possible combinations are kept for ranking.

（注 6）：http://wordnet.princeton.edu

WordNet is a large lexical database of English. Nouns, verbs, adjectives and adverbs are grouped into sets of cognitive synonyms. Besides, hypernym/hyponym of nouns are also available.

a time in a query. That means even there are two entities inthat we assume that two keyword sets are connected by an

Kf ={lemon, vitamin c, rich}, we only consider the follow-

edge if and only if the edit distance （注 8） between them is

ing two queries: “(*) ∧ vitamin c ∧ rich” and “lemon ∧ (*)

1. Following information in the dictionary, i.e. direct hyper-∧ rich”.

nym, direct hyponym, sister term, the nearby surrounding of（ 3 ） Sentences in Doc(Ks ) which contain all keywords

Kf can be linked. Other keyword sets are then inserted into

in the above queries in a window size are extracted. Thenthe graph relatively. For example, for keyword set {citrus

for each sentence, a surrounding keyword set Ks is refined.

fruit, vitamin, rich}, one of its elements “vitamin” is a di-（注 7）

rect hypernym of “vitamin c”, so we consider this set as a
Similar Ks obtained by dictionary-based approach andWeb-search-based approach are merged.

parent keyword set of {citrus fruit, vitamin c, rich} and con-

For example, anect them by an edge. On the other hand, for each keyword

Ks ={rich, vitamin c, lemon, health, benefit} refined fromset obtained from web search, if it shares any common par-the fact f =“Rich in Vitamin C, lemon has many healthent set with any existing keyword set in the present graph,benefits”, is merged as {lemon, vitamin c, rich}.

add itself and all keyword sets edited to the common par-6. Context-Aware Ranking

ent set; if it shares no common parent set with any existing
keyword set in the present graph, eliminate it. For example,

In this section, we introduce our proposed context-aware

by searching the web through the query “(*) ∧ vitamin c ∧ranking algorithm. Here context is constituted by a factrich”, a candidate keyword set {acerola, vitamin c, rich} isand its surrounding facts. To be specific, for fact keyword

achieved. By looking up the dictionary, we know that a direct

set Kf ={lemon, vitamin c, rich}, its hypernym Ks such

hypernym of “acerola” is “berry”, while a direct hypernym ofas {citrus fruit, vitamin c, rich}, its hypernym’s hypernym“berry” is “edible fruit”. In the meantime, we find that a di-Ks such as {citrus fruit, vitamin, rich}, its sibling Ks such

rect hypernym of “citrus fruit” is “edible fruit”, which means

as {grapefruit, vitamin c, rich}, {lemon, b complex vitamin,that “lemon” and “acerola” share the same ancestor “ediblerich}, together with its hyponym, its hyponym’s hyponym

fruit”. Therefore, the keyword set {acerola, vitamin c, rich}compose its context.

and all keyword sets (here {berry, vitamin c, rich}, {berry,

6. 1 Hierarchical graph construction based on dictionary information

vitamin, rich}) edited to the common parent set {edible fruit,vitamin, rich} are inserted into the graph. Meanwhile, use

Since we consider one of relationships between two facts

dictionary to extend these new inserted keyword sets.

is generalization · specialization, we prefer to use this struc-6. 2 Cognition calculation for keyword setsture to rank fact keyword sets. The idea is to construct a

In short, our basic idea is to rank keyword sets based onhierarchical graph in accordance with hypernym/hyponym

the diﬀerence with its context. As a result, here we introduce

information from dictionary to include all keyword sets.

the benchmarks to compare keyword sets. We use people’s

Given a query fact, as we mentioned in Section 5., firstly

cognition of a fact to indicate how this fact is known by peo-

it is divided into words by taking morphological analysis sople. Besides, we assume that if a fact appears frequently onthat a keyword set is obtained. Still take fact “Lemon is

the web, it is well known by most people. Otherwise, it isrich in Vitamin C” for example. At the very beginning,

unknown by most people. Hence, we take a frequency-based

its keyword set Kf ={lemon, vitamin c, rich} is obtained.

way to calculate cognition of the keyword set of a fact.

Based on substitution of only one element in Kf at a time,
we can construct a subgraph shown as the enclosed area in
Figure 3. To be more specific, since lemon is a kind of citrus
fruits, in other words, “citrus fruit” is a direct hypernym of“lemon”, the substitution of “lemon” to “citrus fruit” leads
to get a parent keyword set {citrus fruit, vitamin c, rich}of Kf . So we add an edge between these two keyword sets.
Similarly, “vitamin” is a direct hypernym of “vitamin c”, thesubstation of “vitamin c” to “vitamin” leads to get a parent
keyword set {lemon, vitamin, rich} of Kf . And an edge isalso added between these two keyword sets. Pay attention

For the keyword set of a fact Kf = {k1 , k2 , ..., kn }, howwell it is cognized by people is computed as follows:n Cn−1
HitCount(k1 ∧k2 ∧...∧kn )q∈Qd
HitCount(q)Cog(Kf ) = ∑where HitCount(q) indicates the hitcount returned by searchengines, such as Google （注 9）, Bing （注 10）, when q is queried.
Qd represents the set of all kinds of combinations of any n−1elements from Kf . n is the number of elements in Kf .
Take Kf ={lemon, vitamin c, rich} for example. How well
（注 8）：The edit distance between two keyword sets is the minimum

number of single-keyword substitutions required to change one keyword set into another.
（注 7）：Asterisk part corresponds to an entity in the extracted sen-

（注 9）：https://www.google.com

tence.

（注 10）：http://www.bing.com

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Figure 3 An example for part of constructed hierarchical graph for fact “Lemon is richin Vitamin C”. Each node indicates a keyword set.

it is cognized by people is determined by the following threevitamin c, rich}. So we eliminate such part(s) in the process

parts:of cognition calculation.

（ 1 ） When people talk about things which is rich in Vi-

tamin C, to what extent they can think up “lemon”, say
HitCount(lemon,vitaminc,rich).
HitCount(vitaminc,rich)

6. 3 Importance calculation based on context cognition
We briefly mentioned that we rank keyword sets based on（ 2 ） When people talk about which kind of thing lemon

the diﬀerence with its context. Actually, according to the hi-

is rich in, to what extent they can think up “vitamin c”, sayerarchical structure of the keyword set graph, we consider the

HitCount(lemon,vitaminc,rich).
HitCount(lemon,rich)

importance of a keyword set is determined by (1) influence（ 3 ） When people talk about the relation between lemon

from its parent sets (only the direct ones); (2) influence fromand Vitamin C, to what extent they can think up “rich”, say

its sibling sets (ones which shares the same direct parent).

HitCount(lemon,vitaminc,rich).
HitCount(lemon,vitaminc)

6. 3. 1 Influence from its parent sets

Actually, the cognition for Kf ={lemon, vitamin c, rich} is

To simplify, let us concentrate on the blue node in Figure

the harmonic mean of the above three parts.

4 which indicates a keyword set. Suppose there are two par-However, if the substitution k1′ of k1 is not as widely

ent sets. In the left side, we can see one of its parent set

discussed as k1 , it will lead a small hitcount of querying

P1 is well known by people, marked high cognition in the“k1′ ∧ k2 ∧ ... ∧ kn ”. Thus, the above calculation for cognition

figure, another P2 is also well known by people. The blue

biases against those not so widely discussed. To solve thisnode itself is highly cognized, too. Since the blue node is the

problem, we also take the cognition of each substitution into

child of P1 and P2 , it is normal that the blue one inherits

account, thensome attributes from its parents. Hence, in this case, that

Cog(Kf ) = ∑2n − 2
HitCount(k1 ∧k2 ∧...∧kn )q∈Qa
HitCount(q)the blue node is highly cognized is to be expected. In otherwords, because the diﬀerence between its cognition and its
parents’s cognition is small, its parents are important rather

where Qa represent the set of all kinds of combination ofelements from Kf , except Kf itself and ∅.
Therefore, for Kf ={lemon, vitamin c, rich}, besides
the above three parts, its cognition is also determinedby how well people cognize every element in Kf , say
HitCount(lemon,vitaminc,rich) HitCount(lemon,vitaminc,rich),,
HitCount(lemon)
HitCount(vitaminc)
HitCount(lemon,vitaminc,rich),respectively.
HitCount(rich)

There appears another problem that when people talkabout the term “rich”, they hardly mention about lemon andVitamin C. Such kind of rare connection is likely to bring an
unrealistic calculation result of cognition for Kf ={lemon,than itself. We believe the influence from its parent sets issmall. In the right side, we can see both of its parent sets P1
and P2 are unknown by people, marked low cognition in the
figure. However, the blue node is well known. In this case,from parent to itself, there occurs a change which is beyond
one’s imagination so that the blue node is more importantcompared to its parents. We believe the influence from its
parent sets is great.
To summarize, we assume the bigger the cognition diﬀerence between one and one’s parent sets is, the more impor-$%&'()*&+%,*+!

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Figure 4 Illustrations of how one’s parent sets influence one’s
importance.

&'()!

#$!

#"!

#%!

&'()!

&'()!

*+,!

&'()!

#$!

#"!

#%!

*+,!

*+,!

*+,!

Figure 5 Illustrations of how one’s sibling sets influence one’s
importance.

tance it is. Thus, influence from one’s parent sets is calcu-

ranked according to cognition diﬀerence with context formed

lated as:by surrounding facts.

P arentInf (Kf ) =∑|Cog(p) − Cog(Kf )|Acknowledgmentp∈P

where P indicates the set of Kf ’s parent sets.

This work was supported in part by the following projects:

6. 3. 2 Influence from its sibling sets

Grants-in-Aid for Scientific Research (Nos.

Similarly, to simplify, let us concentrate on the blue node

24680008) from MEXT of Japan.

24240013,

in Figure 5. Suppose there are three sibling sets. In the left
side, we can see that among four child sets of P2 , there arethree sets which is highly cognized by people, only one set
which is unknown by people. Also, from the left side of the
figure, we know that the blue node itself is well known. Thatmeans our concentrated blue node follows the general trend,
and it is only a common one among the majority. Hence, it
is not so important. In the right side, we can see that amongfour child sets of P2 , there are three sets which is unknown bypeople, only one set which is well known by people. Besides,from the right side of the figure, we know that the blue nodeis the one which is highly cognized by people. That means
our concentrated blue node is an exception so that it should
be presented to users. Hence, it is an important keyword set.
To summarize, we assume the bigger the cognition diﬀerence between one and the majority is, the more importance
it is. Thus, influence from one’s sibling sets is calculated as:
1 ∑SiblingInf (Kf ) = |Cog(m) − Cog(Kf )||M | m∈M
where M is composed by sets which belongs to the major
cognition part.
From the above discussion, we can draw the conclusionthat context constituted by the input fact and its surrounding should be taken into account in order to bring moremeaningful and important facts in the upper to users. Therefore, the final importance score for Kf is
Score(Kf ) = αP arentInf (Kf ) + βSiblingInf (Kf )where α and β are weight factors.

7. Conclusion
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