Reflections on: KnowMore - Knowledge Base
Augmentation with Structured Web Markup
Ran Yu1 , Ujwal Gadiraju2 , Besnik Fetahu2 , Oliver Lehmberg3 ,
Dominique Ritze3 , and Stefan Dietze1,2,4
1
GESIS - Leibniz Institute for the Social Sciences, 50667 Cologne, Germany
{ran.yu, stefan.dietze}@gesis.org
2
L3S Research Center, 30167 Hannover, Germany
{gadiraju,fetahu}@l3s.de
3
University of Mannheim, 68159 Mannheim, Germany
{oli,dominique}@informatik.uni-mannheim.de
4
Heinrich-Heine-University Düsseldorf
Abstract. Knowledge bases are in widespread use for aiding tasks such as in-
formation extraction and information retrieval. However, knowledge bases are
known to be inherently incomplete. As a complimentary data source, embed-
ded entity markup based on Microdata, RDFa, and Microformats have become
prevalent on the Web. RDF statements extracted from markup are fundamentally
different from traditional knowledge graphs: entity descriptions are flat, facts are
highly redundant and of varied quality, and, explicit links are missing despite a
vast amount of coreferences. Therefore, data fusion is required in order to facil-
itate the use of markup data for KBA. We present a novel data fusion approach
which addresses these issues. We perform a thorough evaluation on a subset of the
Web Data Commons dataset and show significant potential for augmenting exist-
ing knowledge bases. A comparison with existing data fusion baselines demon-
strates superior performance of our approach when applied to Web markup data.
1 Introduction
Knowledge bases (KBs) such as Freebase [1] or YAGO [8] are in widespread use to aid
a variety of applications and tasks such as Web search and Named Entity Disambigua-
tion (NED). While KBs capture large amounts of factual knowledge, their coverage
and completeness vary heavily across different types or domains. In particular, there
is a large percentage of less popular (long-tail) entities and properties that are under-
represented. Recent efforts in knowledge base augmentation (KBA) aim at exploiting
data extracted from the Web to fill in missing statements. These approaches extract
triples from Web documents [2], or exploit semi-structured data from Web tables [6,
7]. After extracting values, data fusion techniques are used to identify the most suitable
value (or fact) from a given set of observed values.
Building on standards such as RDFa, Microdata and Microformats, and driven by
initiatives such as schema.org, a joint effort led by Google, Yahoo!, Bing and Yandex,
markup data has become prevalent on the Web. Through its wide availability, markup
lends itself as a diverse source of input data for KBA. However, RDF statements ex-
tracted from markup are fundamentally different from traditional knowledge graphs:
Copyright © 2019 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
entity descriptions are flat, facts are highly redundant and of varied quality, and, ex-
plicit links are missing despite a vast amount of coreferences.
In this work, we introduce KnowMore, an approach based on data fusion techniques
which exploits markup crawled from the Web as source of data to aid KBA. Our ap-
proach consists of a two-fold process, where first, candidate facts for augmentation of a
particular KB entity are retrieved through a combination of blocking and entity match-
ing techniques. In a second step, correct and novel facts are selected through a super-
vised classification approach and an original set of features. We apply our approach
to the WDC2015 dataset and demonstrate superior performance compared to state-of-
the-art baselines. We also demonstrate the capability for augmenting three large-scale
knowledge bases, namely Wikidata, Freebase and DBpedia through markup data based
on our data fusion approach. The main contributions of our work are threefold:
– Pipeline for data fusion on Web markup. We propose a pipeline for data fusion that
is tailored to the specific challenges arising from the characteristics of Web markup.
– Model & feature set. We propose a novel data fusion approach consisting of a su-
pervised classification model, utilising an original set of features geared towards val-
idating correctness and relevance of markup facts.
– Knowledge base augementation from markup data. As part of our experimental
evaluation, we demonstrate the use of fused markup data for augmenting three well-
established knowledge bases.
2 Motivation & Problem Definition
Motivation. For a preliminary analysis of DBpedia, Freebase and Wikidata, we ran-
domly select 30 Wikipedia entities of type Movie and Book and retrieve the correspond-
ing entity descriptions from all three KBs. We select the 15 most frequently populated
properties for each type and provide equivalence mappings across all KB schemas as
well as the schema.org vocabulary manually. Since all vocabulary terms and types in the
following refer to schema.org, prefixes are omitted. Figure 1 shows the proportion of
instances for which the respective properties are populated. We observe a large amount
of empty slots across all KBs for most of the properties, with an average proportion of
missing statements for books (movies) of 49.8% (37.1%) for DBpedia, 63.8% (23.3%)
for Freebase and 60.9 % (40%) for Wikidata.
% of entity descriptions containing particular predicate
% of entity descriptions including particular predicate
DBpedia Freebase Wikidata DBpedia Freebase Wikidata
100 100
90 90
80 80
70 70
60 60
50 50
40 40
30 30
20 20
10 10
0 0
e
n
or
ge
r
e
at
Pu es
d
bn
or
r
n
d
r
e
t
n
ge
or
er
r
r
n
y
y
r
d
e
ds
ou
he
a
ito
to
ho
a
cB
n
m
tio
nr
he
tio
ar
m
tio
io
he
nr
rm
th
at
Ye
ct
uc
Ye
ag
pa
ar
ua
is
ua
ac
at
na
ed
na
ab
is
ge
aw
t
ge
rip
di
rip
is
re
i
is
au
l
au
us
ns
od
aw
Fo
ht
ht
bl
fP
r
om
ng
ng
bl
kE
bl
du
di
sc
sc
pu
ig
m
ig
tra
pr
rO
ok
Pu
La
La
o
nC
de
r
de
r
py
py
bo
bo
be
te
te
in
in
tio
co
co
da
da
m
uc
nu
od
pr
(a) Book (b) Movie
Fig. 1: Proportion of book and movie instances per KB that include selected popular predicates.
In addition, coverage varies heavily across different properties, with properties such
as editor or translator being hardly present in any of the KBs. Tail entities/types as well
2
as time-dependent properties which require frequent updates, such as the award of a
book, are prevalent in markup data [4], yet tend to be underrepresented in structured
KBs. Hence, markup data lends itself as data source for the KBA task. However, given
the specific characteristics of markup data [9], namely the large amount of coreferences
and near-duplicates, the lack of links and the variety of errors, data fusion techniques
are required which are tailored to the specific task of KBA from Web markup.
Problem Definition. Our work is concerned with entity descriptions extracted from
structured Web markup. We refer to such a dataset as M , where the WDC dataset is an
example. Data in M consists of entity descriptions ei , each consisting of a set of RDF
quads, i.e. a set of hs, p, o, ui quadruples which are referring to entities. The elements
hs, p, o, ui of the quadruple represent subject, predicate, object and the URL of the
document from which the triplehs, p, oi has been extracted, respectively.
There exist n ≥ 0 subjects s1 , s2 , ..., sn , and consequently, n entity descriptions
ei = hsi , pi , oi i ∈ E which represent a particular query entity q in M . Here, E is
the set of all entity descriptions which (co)refer to entity q. We define a property-value
pair hp, oi describing the entity q as a fact of q. Note that we explicitly consider multi-
valued properties, i.e. a particular predicate p might be involved in more than one fact
for a particular entity q. We define the task of augmenting an entity description eq ,
representing a query entity q within a particular KB from data in a markup corpus M
as follows:
Definition 1. KBA task: For a query entity q that is represented through an entity de-
scription eq in a KB, we aim at selecting a subset Fnov from M , where each fact
fi ∈ Fnov represents a valid fact which augments the entity description eq for q.
Fnov represents the final output of the KnowMore pipeline. We consider a fact valid
for augmentation, if it meets the following criteria:
– A fact is correct with respect to query entity q, i.e. consistent with the real world
regarding query entity q according to some ground truth (Section 4).
– A fact represents novel, i.e. not duplicate or near-duplicate, information with regard
to the entity description eq of q in a given KB.
– The predicate pi of fact hpi , oi i should already be reflected in a KBs given schema.
3 Approach
3.1 Entity Matching
The first step, KnowM orematch , aims at obtaining candidate facts fi ∈ F by collect-
ing the set E of coreferring entity descriptions ei ∈ E from M which describe q and
corefer to the entity description eq in a given KB. We use a three step approach in order
to efficiently achieve high accuracy results.
Data Cleansing. This step aims at (i) resolving object references and (b) fixing common
errors [3] to improve overall usability of the data. Given the prevalence of literals in
Web markup and the need to homogenise entity descriptions for further processing,
we resolve object references into literals by replacing object URIs with the labels of
the corresponding entity. In addition, based on earlier work [3] which studied common
3
errors in Web markup, we implement heuristics and apply these to E as a cleansing step
to fix wrong namespaces, and handle undefined types and properties.
Blocking. We implement the blocking step through entity retrieval using the BM25
model to reduce the search space. We created an index for each type-specific subset
using Lucene, and then use the label of eq to query the field name within a type-specific
index. This result in a set of candidate entity descriptions e0i ∈ E0 that potentially
describe the same entity as eq .
Entity Matching. This step is for the validation of each entity description e0i ∈ E0 in
the result of the blocking step. We use supervised classification on the similarity vector
between e0i and eq . In order to compute the similarity for each property, we consider
→
−
all properties as attributes of the feature space A = {a1 , a2 , ..., an }, so that each entity
description e can be represented as a vector of values → −v = {oa1 , oa2 , ..., oan } which
represent the objects of the considered hp, oi tuples. We construct a similarity vector
−−→ −− → −
sim(v KB , → v ) between e and each entity description e0 ∈ E as in Equation 1.
q i 0
−−→ −− → −
sim(v KB , →
v ) = {λa1 , λa2 , ..., λan } (1)
λai = sim(oKB
ai , oai ) (2)
In order to compute sim(oKB
ai , oai ), we employ datatype-specific similarity met-
rics, i.e., we implemented one similarity measure for each schema.org datatype, and
automatically select the appropriate metric. We then train a supervised classification
model, to make the decision whether or not e0i is a match for eq . We experimented with
several state-of-the-art classifiers (SVM, Logistic Regression and Naive Bayes). Since
Naive Bayes achieves a F 1 score that is 0.08 higher than the best SVM (linear kernel),
and 0.123 higher than the Logistic Regression (LR), throughout the remaining paper
we rely on a trained Naive Bayes classifier unless otherwise stated.
3.2 Data fusion
During the data fusion step, KnowM oreclass , we aim at selecting a subset Fnov ⊂ F
that fulfills the criteria as listed in Section 2. More specifically, we introduce data fusion
techniques based on supervised classification to ensure the correctness and two dedupli-
cation steps to ensure novelty, namely deduplication with respect to M (KnowM oreded )
and deduplication with respect to the KB (KnowM orenov ).
Table 1: Features for supervised data fusion from markup data.
Category Notation Feature description
tr1 , tr2 , tr3 Maximum, minimum, average PageRank score of the PLDs containing fact f
Source level
tr4 , tr5 , tr6 Maximum, minimum, average percentage of common errors [3] of the PLDs containing fact f
tr7 , tr8 , tr9 Maximum, minimum, average precision (based on training data) of the PLDs containing fact f
Entity level te1 , te2 , te3 Maximum, minimum, average size (number of facts) of ei containing f
tp1 Predicate term
tp2 Predicate frequency in F
Property level †tp3 Amount of clusters of predicate p
†tp4 Average cluster size of predicate p
†tp5 Variance of the cluster sizes of predicate p
tf1 Fact frequency in F
Fact level
†tf2 Normalized cluster size that f belongs to
†-features extracted based on clustering result
4
Correctness - Supervised Classification. The first step (KnowM oreclass ) aims at
detecting correct facts by learning a supervised model that produces a binary classifi-
cation for a given fact f ∈ F into one of the labels {‘correct’, ‘incorrect’}. For the
classification model, we have experimented with several different approaches. We rely
on a Naive Bayes classification since our experiments have shown superior performance
over other classifiers. The features used are listed in Table 1.
While we aim to detect the correctness of a fact, we consider characteristics of the
source, that is the Pay-Level-Domain (PLD) from which a fact originates, the entity
description, the predicate term as well as the fact itself. From the computed features
we train the classifier for classifying the facts from F into the binary labels {‘correct’,
‘incorrect’}. The ‘correct’ facts form a set Fclass that is the input for the next steps.
Novelty. A fact f is considered to be novel with respect to the KBA task, if it fulfills the
conditions: i) is not duplicate with other facts selected from our source markup corpus
M , ii) is not duplicate with any facts existing in the KB. Each of these two conditions
corresponds to a deduplication step.
Deduplication with respect to M (KnowMoreded ). We detect near-duplicates via
clustering. For each predicate p, all the facts f = hp, oi i corresponding to p are clustered
into n clusters {c1 , c2 ,· · · ,cn }. Each cluster ci , i = 1, ..., n contains a set of near-
duplicates. To fulfill i), we select only one fact from each cluster by choosing the fact
that is closer to the cluster’s centroid. This results in the fact set Fded that is the input
for next deduplication step.
Deduplication with respect to KB (KnowMorenov ). We compute the similarity
sim(fi , fKB ) between a fact fKB in a respective KB for a particular predicate p and a
fact fi for the same (mapped) predicate p in Fded with the datatype-specific similarity
metrics. If sim(fi , fKB ) is higher than a threshold τ , we remove the fact. We explain τ
and its configuration during the experimental Section 4.3. The facts selected from Fnov
in this step are the final result for augmenting the KB.
Note that our deduplication step considers and supports multi-valued properties. By
relying on the clustering features, computed during the fusion step, we select facts from
multiple clusters (corresponding to multiple predicates) as long as they are classified as
correct. As documented by the evaluation results (Section 5), this does not negatively
affect precision while improving recall for multi-valued properties.
4 Experimental Setup
4.1 Ground Truth
We use the WDC2015 dataset5 , where we extracted 2 type-specific subsets consisting of
entity descriptions of the schema.org types Movie and Book. As input for the KBA task,
we randomly select 30 entities for each type Book and Movie. We evaluate the perfor-
mance of our approach for augmenting entity descriptions of these 60 entities obtained
from three different KBs: DBpedia (DB), Freebase (FB) and Wikidata (WD). To sim-
plify the schema mapping problem between WDC data and the respective KBs while at
the same time taking advantage of the large-scale data available in our corpus, we limit
5
http://webdatacommons.org/structureddata/index.html#toc3
5
the task to entities annotated with the http://schema.org ontology for this experiment.
We manually create a set of schema mappings that maps the schema.org vocabularies
to the DB, FB, WD vocabularies.
Data Fusion - Correctness. We used crowdsourcing to build a ground truth for the cor-
rectness of facts fi ∈ F . For the valid entity descriptions in E, we acquire labels for all
distinct facts, as either correct or incorrect with respect to q. We acquired 5 judgments
from distinct workers for each entity and corresponding facts through Crowdflower.
Data Fusion - Novelty. We built ground truths for validating (i) deduplication perfor-
mance within M , as well as (ii) novelty with respect to the different KBs. Authors of
this paper acted as experts and designed a coding frame to decide whether a fact is
novel. After resolving disagreements on the coding frame on a subset of the data, every
fact was associated with one expert label through manual deliberation.
4.2 Metrics
We consider distinct metrics for evaluating each step of our approach.
– KnowM oreclass . We evaluate the performance of the approaches through standard
precision P , recall R and F 1 scores, based on our ground truth.
– KnowM oreded . We evaluate the performance of deduplication with respect to M
using Dist% - the percentage of distinct facts within the respective result set. We
compare between Dist% (Fded ) and Dist% (Fclass ), that is, before and after the
deduplication within M .
– KnowM orenov . For evaluating the performance of deduplication with respect to
a given KB, we measure the novelty as N ov - the percentage of novel facts - and
compare between N ov (Fded ) and N ov (Fnov ), that is, the novelty before and after
this step. We also measure the recall R - the percentage of distinct and accurate facts
in Fded that have been selected by KnowM orenov into Fnov .
Furthermore, we demonstrate the potential of our approach for augmenting a given
KB by measuring the coverage gain. The coverage gain of predicate p is computed
as the percentage of entity descriptions having p populated through the KnowM ore
approach (i.e. after step KnowM orenov ) with at least one fact hp, oi, out of the ones
that did not have statement involving property p within the KB before augmentation.
4.3 Configuration & Baselines
Configuration. For the entity matching step, we use Lucene for indexing and BM25
retrieval with the Lucene default configuration where k1 = 1.2, b = 0.75. For the
deduplication with respect to KBs, we report the evaluation result of KnowM orenov
using different τ = {0.3, 0.5, 0.7} in Section 5.
Baselines. We compare (KnowM oreclass ) with P recRecCorr that is proposed by
Pochampally et al. [5] and CBF S [10]. To the best of our knowledge, the CBF S
approach is the only available method so far geared towards the challenges of markup
data, while P recRecCorr represents a recent and highly related data fusion baseline.
– P recRecCorr: facts selected based on the approach from candidate set F . We con-
sider each PLD as a source and implemented the exact solution as described in the
paper. We use the threshold as presented in the paper, i.e. 0.5, to classify facts.
6
– CBF S: facts selected based on the CBF S approach from F . The CBF S approach
clusters the associated values at the predicate level into n clusters (c1 , c2 , · · · , cn ) ∈
C. Facts that are closest to the centroid of each cluster are selected, provided the
cluster meet the criteria that its size is larger than half of the largest cluster size.
5 Evaluation Results
Correctness - Data Fusion. The results for KnowM oreclass and the baselines are
shown in Table 2. Our chosen configuration, i.e. using a Naive Bayes classifier achieves
highest F 1 scores among all the different configurations. The presented F1 score of the
P recRecCorr baseline is the best possible configuration for our given task, where we
experimented with different thresholds ([0,1], gap 0.1) as discussed in [5] and identified
0.5 experimentally as the best possible configuration. We observe that the F 1 score of
our approach is 0.141 higher than P recRecCorr and 0.119 higher than CBF S on
average across datasets. This indicates that our approach provides the most efficient
balance between precision and recall across the investigated datasets. Although, the
precision of the baseline approach P recRecCorr is 0.013 higher than the one from
KnowM oreclass on the Book dataset, the baseline fails to recall a large amount of
correct facts, where the recall of KnowM oreclass is 0.388 higher. This also is reflected
in the average size of entity descriptions obtained through both approaches, where the
entity descriptions from P recRecCorr consist of 4.88 statements on average, and the
ones from KnowM oreclass are 8.83, indicating a larger potential for the KBA task.
Table 2: Performance of KnowM oreclass Table 3: Diversity Dist% before and
and baselines. after deduplication.
Movie Book
Approach P R F1 P R F1 Dataset Dist%(Fclass ) Dist%(Fded )
KnowMoreclass 0.954 0.896 0.924 0.880 0.868 0.874
PrecRecCorr 0.924 0.861 0.891 0.893 0.48 0.624
Movie 94.8 96.1
CBFS 0.802 0.752 0.776 0.733 0.842 0.784 Book 82.1 95.6
Diversity. Table 3 presents the evaluation result before (Dist% (Fclass )) and after
(Dist% (Fded )) the step KnowM oreded . The Dist% of facts improves by 1.3 per-
centage points for the Movie dataset and by 13.5 percentage points for the Book dataset.
The less improvement gain for the Movie dataset presumably is due to the nature of
the randomly selected Movie entities. As these appear to be mostly tail entities, candi-
date facts in our markup corpus M are fewer and less redundant. Hence, the amount of
duplicates and near-duplicates is smaller, reducing the effect of the deduplication step.
Table 4: Novelty of Fded and Fnov with respect to target KBs.
Nov R
KB Fded Fnov , τ = 0.3 Fnov , τ = 0.5 Fnov , τ = 0.7 Fded Fnov , τ = 0.3 Fnov , τ = 0.5 Fnov , τ = 0.7
Movie DBpedia 0.631 0.963 0.962 0.962 1 0.927 0.939 0.939
Freebase 0.527 0.747 0.742 0.742 1 0.942 0.957 0.957
Wikidata 0.412 0.929 0.929 0.897 1 0.963 0.963 0.963
Book DBpedia 0.736 0.962 0.929 0.92 1 0.826 0.848 0.870
Freebase 0.639 0.915 0.846 0.825 1 0.833 0.846 0.846
Wikidata 0.705 0.944 0.933 0.923 1 0.791 0.814 0.837
7
Novelty with respect to KB. The results before (N ov (Fded )) and after (N ov (Fnov ))
the deduplication for specific KBs using different similarity thresholds (τ ) are presented
in Table 4. Since our approach is not aware of the total number of novel facts for a par-
ticular entity description on the Web a priori, in this evaluation, we consider all the
novel facts in Fded as the gold standard, and compute the recall of Fnov after applying
the KnowM orenov accordingly. We evaluate the performance of KnowM orenov us-
ing τ in {0.3, 0.5, 0.7}. As shown in Table 4, even though there is a trade-off between
novelty and recall, different values of τ do not have a strong influence on the evaluation
metrics. One of the reasons is that, a large proportion of facts have non-literal (e.g. nu-
meric) values. While our datatype-specific similarity computes a binary (0 or 1) score
in these cases, it is not influenced by the selection of τ .
DB FB WD DB FB WD
100 100
80 80
coverage gain
coverage gain
60 60
40 40
20 20
0 0
tor on re ed ar ds on ge or ny tor ge or on at er ed bn ar es on
accripti genblish htYeawarurati gua authmpa irec ua authcripti Form blishblish is htYefPag Editi
s u rig d Lan Co d a ng s k pu Pu rig O k
de teP py in on inL de boo te py er oo
da co u cti da co umb b
d n
pro
(a) Movie (b) Book
Fig. 2: Proportion of augmented entity descriptions with KnowM ore.
Coverage Gain. Figure 2 shows the coverage gain on the previously empty slots as
shown in Figure 1 per predicate and KB for our selected entities. Based on the result,
the KnowM ore pipeline shows a coverage gain of 34.75% on average across different
properties for DBpedia, 39.42% for Freebase and 36.49% for Wikidata. We observe that
the obtained gain varies strongly between predicates and entity types, with a generally
higher gain for book-related facts. For instance, within the Movie case, for property
actor we were able to gain 100% coverage in both DBpedia and Freebase, while the
property award shows a coverage gain of 10% or less for all three KBs. Reasons behind
low coverage gain for a particular property are 2-fold: 1) the lack of data in the Web
markup data corpus, and 2) the lack of true facts in the real world for a particular
attribute, e.g. only a small proportion of movies have won an award. On average, we
obtained 2.8 (6.8) facts for each movie (book) entity in our experimental dataset.
6 Conlusions
We have introduced KnowM ore, an approach towards knowledge base augmentation
from large-scale Web markup data. We apply it to the WDC2015 corpus and augment
three established KBs. Evaluation results suggest superior performance of our approach
with respect to novelty as well as correctness compared to state-of-the-art data fusion
baselines. Our experimental results indicate comparably consistent performance across
a variety of types, whereas the performance of baseline methods tends to vary strongly.
8
References
1. K. Bollacker, C. Evans, P. Paritosh, T. Sturge, and J. Taylor. Freebase: a collaboratively
created graph database for structuring human knowledge. In Proceedings of the 2008 ACM
SIGMOD international conference on Management of data, pages 1247–1250, Vancouver,
Canada, 2008. ACM. DOI: 10.1145/1376616.1376746.
2. X. Dong, E. Gabrilovich, G. Heitz, W. Horn, N. Lao, K. Murphy, T. Strohmann, S. Sun, and
W. Zhang. Knowledge vault: A web-scale approach to probabilistic knowledge fusion. In
Proceedings of the 20th ACM SIGKDD International Conference on Knowledge Discovery
and Data Mining, KDD ’14, 2014.
3. R. Meusel and H. Paulheim. Heuristics for fixing common errors in deployed schema.org
Microdata. In The Semantic Web. Latest Advances and New Domains: 12th European Seman-
tic Web Conference, ESWC 2015, Portoroz, Slovenia, May 31 – June 4, 2015. Proceedings,
2015.
4. R. Meusel, D. Ritze, and H. Paulheim. Towards more accurate statistical profiling of de-
ployed schema.org Microdata. J. Data and Information Quality, 8(1):3:1–3:31, Oct. 2016.
DOI: 10.1145/2992788.
5. R. Pochampally, A. Das Sarma, X. L. Dong, A. Meliou, and D. Srivastava. Fusing data
with correlations. In Proceedings of the 2014 ACM SIGMOD International Conference on
Management of Data, SIGMOD ’14, pages 433–444, New York, NY, USA, 2014. ACM.
DOI: 10.1145/2588555.2593674.
6. D. Ritze, O. Lehmberg, and C. Bizer. Matching HTML tables to DBpedia. In Proceedings
of the 5th International Conference on Web Intelligence, Mining and Semantics, WIMS ’15,
pages 10:1–10:6, New York, NY, USA, 2015. ACM. DOI: 10.1145/2797115.2797118.
7. D. Ritze, O. Lehmberg, Y. Oulabi, and C. Bizer. Profiling the potential of web tables for
augmenting cross-domain knowledge bases. In Proceedings of the 25th International Con-
ference on World Wide Web, WWW ’16, pages 251–261, Republic and Canton of Geneva,
Switzerland, 2016. International World Wide Web Conferences Steering Committee. DOI:
10.1145/2872427.2883017.
8. F. M. Suchanek, G. Kasneci, and G. Weikum. Yago: A core of semantic knowledge. In
Proceedings of the 16th International Conference on World Wide Web, WWW ’07, pages
697–706, New York, NY, USA, 2007. ACM. DOI: 10.1145/1242572.1242667.
9. R. Yu, B. Fetahu, U. Gadiraju, and S. Dietze. A survey on challenges in Web markup data
for entity retrieval. In International Semantic Web Conference (Posters & Demos), Kobe,
Japan, October 17-21, 2016.
10. R. Yu, U. Gadiraju, X. Zhu, B. Fetahu, and S. Dietze. Towards entity summarisation on struc-
tured web markup. In H. Sack, G. Rizzo, N. Steinmetz, D. Mladenić, S. Auer, and C. Lange,
editors, The Semantic Web: ESWC 2016 Satellite Events, Heraklion, Crete, Greece, May 29
– June 2, 2016, Revised Selected Papers, pages 69–73, Cham, 2016. Springer International
Publishing. DOI:10.1007/978-3-319-47602-5 15.
9