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Extracting Contextualized Quantity Facts from Web Tables

Extracting Contextualized antity Facts from Web Tables
Vinh Thinh Ho
Max Planck Institute for Informatics
Saarbrücken, Germany
Koninika Pal
Max Planck Institute for Informatics
Saarbrücken, Germany
Simon Razniewski
Max Planck Institute for Informatics
Saarbrücken, Germany
Klaus Berberich
Max Planck Institute for Informatics
htw saar
Saarbrücken, Germany
Gerhard Weikum
Max Planck Institute for Informatics
Saarbrücken, Germany
Quantity queries, with lter conditions on quantitative measures
of entities, are beyond the functionality of search engines and QA
assistants. To enable such queries over web contents, this paper
develops a novel method for automatically extracting quantity facts
from ad-hoc web tables. This involves recognizing quantities, with
normalized values and units, aligning them with the proper entities,
and contextualizing these pairs with informative cues to match
sophisticated queries with modiers. Our method includes a new
approach to aligning quantity columns to entity columns. Prior
works assumed a single subject-column per table, whereas our ap-
proach is geared for complex tables and leverages external corpora
as evidence. For contextualization, we identify informative cues
from text and structural markup that surrounds a table. For query-
time fact ranking, we devise a new scoring technique that exploits
both context similarity and inter-fact consistency. Comparisons of
our building blocks against state-of-the-art baselines and extrinsic
experiments with two query benchmarks demonstrate the benets
of our method.
Information Extraction, Quantity Facts, Web Tables
ACM Reference Format:
Vinh Thinh Ho, Koninika Pal, Simon Razniewski, Klaus Berberich, and Ger-
hard Weikum. 2021. Extracting Contextualized Quantity Facts from Web
Tables. In Proceedings of the Web Conference 2021 (WWW ’21), April 19–
23, 2021, Ljubljana, Slovenia. ACM, New York, NY, USA, 10 pages. https:
A good fraction of web queries revolve around quan-
tities of entities: looking up, ltering, comparing and aggregating
quantitative properties such as heights of buildings, running times
of athletes, goals or scoring rates of footballers, energy consump-
tion of electric cars, etc. [
]. In this paper we focus on quantity
This paper is published under the Creative Commons Attribution 4.0 International
(CC-BY 4.0) license. Authors reserve their rights to disseminate the work on their
personal and corporate Web sites with the appropriate attribution.
WWW ’21, April 19–23, 2021, Ljubljana, Slovenia
2021 IW3C2 (International World Wide Web Conference Committee), published
under Creative Commons CC-BY 4.0 License.
ACM ISBN 978-1-4503-8312-7/21/04.
Table 1: Illustrative example on football teams.
Team Stadium Capacity Coach Value (in Bio)
Bayern Allianz Arena ca. 75000 Hansi Flick 2.549 Euro
Real Bernabéu 81,044 Zidane 3.649 Euro
Man City unknown n/a Pep Guardiola 2.055 GBP
Chelsea Stamford Bridge 40,834 Frank Lampard 1.958 GBP
Liverpool Aneld 53,394 Jürgen Klopp ca. 1.7 GBP
lters [
], an important class of queries and also a building
block for comparative search. Examples are:
British football teams worth more than 1.5 billion pounds
sprinters who ran 100 m under 9.9 seconds
electric cars with energy eciency above 80 MPG-e
Note that this kind of query is more dicult than quantity
lookups, such as “the value of Manchester City” or “the personal
100 m record of Usain Bolt”. Lookups are well supported by search
engines and QA assistants. Quantity lters, on the other hand, lack
this support as conditions like “more than 1.5 billion pounds” or
“under 9.9 seconds” are mostly interpreted in string-matching mode.
For some examples, search engines return good web pages, such
as Wikipedia articles on “10-second barrier” or “100 metres”, but
this is not the user’s query intent and she has to tediously sift
through these pages rather than receiving a crisp entity-list answer.
Moreover, the result quality depends on the value in the query, as
some (string-interpreted) values match good list pages. For example,
there is a list of 100m races under 10 seconds, but none ready for
9.9, 9.8, etc.
Instead of tapping the web, we could turn to knowledge bases
(KBs) and structured sources in the Open Data ecosystem. How-
ever, KBs hardly cover quantities; for example, Wikidata contains
thousands of sprinters but knows their personal records only for a
few instances. To tap Open Data sources, one would still have to
nd the relevant datasets in a sea of data sources, and assess their
freshness and completeness.
At the core of answering quantity-lter queries is the
problem of extracting entity-quantity facts from web sources. This
has been successfully addressed in [
] for the case of single sen-
tences from text sources, by recognizing entity-quantity pairs along
with relevant context words and building on prior work for spotting
quantities with numeric values and units [
]. In this paper, we
aim to tap into a dierent kind of data sources, namely, ad-hoc
web tables embedded in HTML pages, and address the problem of
accurately extracting entity-quantity facts with relevant context.
WWW ’21, April 19–23, 2021, Ljubljana, Slovenia Vinh Thinh Ho, Koninika Pal, Simon Razniewski, Klaus Berberich, and Gerhard Weikum
An illustrative example, which could serve to answer the query
about British football teams, is shown in Table 1.
There are good prior works on extracting entity-centric facts
from web tables, including surveys [
]. The output is typ-
ically a set of subject-predicate-object (SPO) triples, obtained by
judiciously picking two cells in the same row as S and O and de-
riving P from the column header of O. In conjunction with entity
linking to a KG [
], an extractor could yield, for instance, (Real
Madrid, hasCoach, Zinedine Zidane).
However, state-of-the-art methods do not work well for quantity
facts for several reasons:
First, quantities appear in very diverse and potentially noisy
forms. For example, the team values in Table 1 are just strings,
varying in units and scale and missing values (“unknown”, “n/a”).
Proper interpretation of table cells may require understanding
the surrounding text.
Second, it can be hard to infer which column pair denotes a
quantity fact, that is, to which entity column a quantity column
refers. In the example Table 1, we need to determine that Capacity
refers to Stadium and Value to Team, but this is not obvious for
a machine. This is further aggravated by the common situation
that column headers are more generic and less informative. For
example, instead of headers like Team, Stadium, Capacity, etc.,
we could have Name, Site, Size, etc., which are hard to interpret.
Prior works on web tables seemed to assume that all columns (for
possible choices of O) refer to the same column (for S), and that
this per-row-entity column is usually the leftmost one [
However, these assumptions are not always true.
Third, extracting entity-quantity pairs alone is not sucient for
query answering, as many queries include additional modiers
such as “British” or cues for the measure of interest such as
“energy eciency”. To be able to match these against a repository
of quantity facts, the fact extraction needs to capture also relevant
context. Prior works on triples from web tables ignored this
important issue; they viewed the extraction as uncoupled from
downstream use cases like user queries and questions.
This paper addresses the outlined problems and presents
a full-edged solution, called
for extracting contextualized quantity facts from web tables, to
support quantity-lter queries. First, to cope with noisy quantities
and diverse units and scales in tables, we employ pattern-based
extractors and rule-based normalization. Second, for the problem
of aligning the right pair of entity and quantity columns, one of the
key tasks, we devise a statistical inference method that leverages
external text corpora. Third, to contextualize the extracted quantity
facts, we exploit text and DOM-tree markup that surround a table,
and we introduce a novel way of computing condence scores for
quantity facts, based on evidence in text collections. Finally, as the
resulting facts may still yield many false positives in query results,
we have developed additional methods for enhanced scoring at
query time based on consistency learning [39].
Contribution. The following are novel contributions:
We present a robust solution for the column alignment problem
posed by complex tables, by harnessing external text corpora
and joint inference with entity linking. This is the rst method
specically geared for extracting quantity facts, with the novel
technique of leveraging cues from a large text corpus (Sections 3
and 4).
We introduce a new way of computing quantity fact condence
scores, by incorporating evidence from text collection, with type-
based inference to overcome sparseness problems (Section 4).
We present a new method for corroborating extracted facts at
query time, re-ranking them and pruning false positives based
on a technique for consistency learning (Section 5).
Experiments include comparative evaluations of our major build-
ing blocks against various baselines, and an extrinsic study of
how well the extracted facts support quantity queries. The latter
is based on a benchmark of 100 queries from [
] and a new
collection of 150 queries with list-based ground-truth.
Experimental data and code are available at: https://www.mpi-
A QuTE-based search demonstrator is accessible at: https://qsearch.
2.1 Model
The input for fact extraction is a collection of ad-hoc tables, from a
web crawl, spreadsheet corpus or Wikipedia dump (e.g., [13]).
Denition [Web Table].
A web table with
rows and
is a tuple T=(H,B,X ) where:
-H={hi|i∈ {1..c}} are the headers of the ccolumns;
-B={bi,j|i∈ {1..r},j∈ {1..c}} are cells in the table body;
is the context surrounding the table, which typically includes
web page title, table caption, DOM-tree headings for the HTML
path to the table, and text in proximity to the table.
We denote
Ck={hk}∪{bi,k|i∈ {
Rk={bk,j|j∈ {
as the k-th column and k-th row, respectively.
This denition is geared for “horizontal” tables with column
headers and row-wise records. For “vertical” tables with row head-
ers and data records per column, we can detect the orientation and
apply a transpose operation, using heuristics from [6].
Denition [E-column and Q-column].
For a given table, all
columns whose cells predominantly contain named entities (which
could be linked to a knowledge base) are referred to as E-columns.
All columns whose cells predominantly contain numeric quantities
are denoted as Q-columns. The implementation of “predominantly”
is based on thresholds (say 80%) for the fraction of cells that qualify
one way or the other. Columns that are neither labeled E nor Q (e.g.,
with many cells containing long text) are disregarded. In Table 1,
the columns Team, Stadium and Coach are E-columns, and Capacity
and Value are Q-columns.
The output of extracting facts from a table is represented in the
form of triples called quantity facts, or
for short (cf. [
where this terminology is dened for text-based extraction).
Denition [Qfact].
A quantity fact extracted from table
(H,B,X) is a triple of the form F=(e,q,X)where:
is an entity in a table-body cell
of an E-column
, either
in the string form of an entity mention or already in the form of
a linked entity uniquely identied in a KB;
Extracting Contextualized antity Facts from Web Tables WWW ’21, April 19–23, 2021, Ljubljana, Slovenia
Quantity Query
Qfact Extraction Search & Ranking
Qfact Scoring
Joint CA & EL Qfact
Figure 1: Overview of the QuTE system.
is a quantity, properly normalized and with proper unit, in a
cell bi,kof a Q-column Ck;
is Qfact context, a (small) set of cue words (or phrases) ex-
tracted from the table (incl. context
) that are specically infor-
mative for the pair (e,q).
As an example, a perfect extractor from Table 1 should produce
Qfacts such as (Estadio Santiago Bernabéu, 81044, “stadium, capac-
ity, seats, Madrid”), (Chelsea F.C., 1,958,000,000 GBP, “team, value,
football, London”), assuming informative text surrounding the table.
For the downstream use case of query answering, we consider a
simple model of telegraphic or question-style queries containing a
single quantity lter, following [16]:
Denition [Qquery].
A quantity query is a triple
Q=(qt,qq,qX )
is the expected type of answer entities, such as
football team
or sprinter;
is a quantity condition of the form “
θvalue unit
” where
can be
, between, or (approximately) equal, and the unit is
optional, as some measures do not have units, such as stadium
capacity or country population;
is a set of additional qualier terms that an answer should
match, such as “British” or “100 meters” or “Olympics”, etc.
The answer to a Qquery is a Qfact that matches all query con-
ditions, where context terms can be matched approximately (e.g.,
partially or by embedding-based similarity):
Denition [Qanswer].
An answer to a Qquery
Q=(qt,qq,qX )
is a Qfact
such that
is an entity of type
the lter condition
, and
is a sucient match to the query
context qX .
For example, the Qfact (Chelsea F.C., 1,958,000,000 GBP, “team,
value, football, London”) would approximately match a query about
“British football teams with value above 1.5 billion pounds” (as
“British” and “London” are highly related by word embeddings).
2.2 System
All components of QuTE, i.e, Qfact extraction method along with a
quantity-query processor and result ranker, are implemented in a
pipeline depicted in Figure 1.
The pipeline starts with quantity recognition and normalization
for Q-columns and entity linking to a KB for E-columns. A crucial
step then is column alignment that links a Q-column with its proper
E-column, to obtain a valid Qfact. Contextualization and scoring of
Qfacts involves analyzing the context around a table and statistics
from external corpora. Finally, query processing involves matching
and an additional scoring step, taking inter-fact consistency into
quantity recognition
, we employ a combination of the
prior works on QEWT [
] and Illinois Quantier [
]. The latter
is used to extract numeric values and units from table cells. QEWT
is applied to the column headers to discover additional information
about units and, possibly, scaling factors. Then, detected quantities
are linked to the QuTree catalog [
] for normalization, including
unit conversions.
entity recognition
, we employ the AIDA dictionary (github.
com/ambiverse-nlu), which provides a large set of entity names,
such as “Real”, “Bayern”, etc., and candidate entities.
entity linking (EL)
(i.e., disambiguating the recognized
mentions onto KB items), there are ample prior works specically
geared for web tables [
]. We follow [
], with inference
over a probabilistic graphical model. This takes into account a prior
for entity popularity, context similarity between mentions in table
cells and the KB entities, and the coherence among entity candidates
for the same row (which should be semantically related entities)
and the same column (which should be of the same semantic type).
We denote result entities by
, with
is the entity for input
mention bi,jin the table body.
A major building block of QuTE is the column alignment, which
aligns a Q-column with its proper E-column, in order to extract
Qfacts from the right pair of columns. This section discusses the
limitation of prior works on web table processing and proposes
a robust method for this task. Key novelties of our method are
to leverage cues from an external text corpus, and to couple the
inference for column alignments with the entity linker.
Denition [Column Alignment (CA)].
Given a pre-processed table
{Ck1,Ck2, .. ., Ckx}
{Cv1,Cv2, .. ., Cvy}
, a column alignment is a func-
tion Λthat maps each Q-column to one E-column:
Λ=CkiCvj|i∈ {1..x}
3.1 Heuristics and their Limitations
Column alignment has been addressed in prior works [
under simplifying assumptions, like mapping all Q-columns to the
same E-column, which boils down to identifying a single subject
column for the entire table. We overcome this limitation, but nev-
ertheless consider heuristics that are inspired from prior works.
Denition [Leftmost Heuristic].
Each Q-column
is mapped
to the leftmost E-column
, that is, the smallest
for which
qualies as an E-column.
WWW ’21, April 19–23, 2021, Ljubljana, Slovenia Vinh Thinh Ho, Koninika Pal, Simon Razniewski, Klaus Berberich, and Gerhard Weikum
Denition [Closest-Left Heuristic].
Each Q-column
is mapped
to the closest E-column
that is left of
, that is,
is minimal.
Denition [Most-Unique Heuristic].
Each Q-column is mapped
to the E-column with the largest number of unique values (resem-
bling a relational key). In case of a tie, pick the leftmost one.
In many cases, these three heuristics perform remarkably well,
notwithstanding their simplicity. For our example in Table 1, they
would be far from perfect, though. The Leftmost heuristic maps
all Q-columns to Team. The Closest-Left heuristic correctly aligns
Capacity to Stadium, but erroneously aligns Value to Coach. The
Most-Unique heuristic does not help in this example, as all table
cells have unique values.
Methods that consider multiple subject columns within the same
table mostly rely on linking column headers to classes or concepts
in a comprehensive knowledge base (e.g., [
]), to
map column pairs to KB relations. However, quantity measures are
covered only very sparsely in state-of-the-art KBs. As our goal is
to cover a wide variety of quantity types, we cannot rely on the KB
for column alignment. The only prior work for handling multiple
subject columns and aligning other columns without assuming a
prior KB is [
]. This method is based on discovering functional
dependencies by analyzing entropy measures between columns.
However, in Q-columns the typical situation is that all values are
distinct, so that their frequencies are trivial and do not give hints
for cross-column scoring. Moreover, we found that even when a
web table has multiple E-columns, the values in all of them are
often unique – as tables often have only few rows. Table 1 is a
typical case, and the method of [
] does not add any benet over
the simpler heuristics here. Hence we disregard this method.
3.2 QuTE Method for Column Alignment
We propose a robust column alignment approach by modelling the
connections between a pair of E-column and Q-column as a graph.
To compute a CA-score for a candidate alignment, we devise a graph-
based connectivity measure that considers the co-occurrence sig-
nals for same-row entity/quantity pairs, with entities chosen by the
initial entity linking
. Essentially, we treat these entity/quantity
pairs as Qfacts and leverage external corpus evidence to assess their
Denition [CA-score]. The quality of a column alignment Λis:
i=1.. r
is a normalization constant and
ext-score(F )
is a score for
observing a Qfact that
has the quantitative property
in an
external data collection, and Xis the header of Q-column Ck.
Prior works on extracting SPO triples from web tables often re-
sorted to pre-existing triples in a knowledge base as “witnesses” for
the scoring of newly extracted facts (in the spirit of distant supervi-
sion). For our task, this idea would boil down to a chicken-and-egg
problem, as we do not yet have a richly populated KB of quanti-
ties. Therefore, we harness a dierent source of external evidence,
namely, large text corpora that potentially contain sentences about
having property
. Observations of this sort, with potential
relaxation of the exact value
, are the basis for the computation of
ext-score(F ). We describe this building block in Section 4.
3.3 Iterative Learning of Column Alignment
Column alignment (CA) can be integrated with entity linking (EL)
for joint inference. The rationale for tackling CA and EL jointly
is that either one can give informative cues to the other, to arrive
at a better solution. CA can build on the output of EL, by incorpo-
rating more precise information about the entities in a candidate
E-column. To this end, it can test if the entities exhibit high relat-
edness with the header of the Q-column under consideration. For
example, “Capacity” is rarely seen in combination with Real Madrid,
FC Bayern Munich, etc., but it is often co-occurring with Estadio
Santiago Bernabéu,Allianz Arena, etc. Conversely, if we already
have a good CA solution, this can benet the EL task by identifying
more focused context. In particular, rather than considering all
cells in the same row of an entity as equally relevant for per-row
coherence, we could give higher weight to the coherence between
cells of the aligned E-column and Q-column. For example, frequent
co-occurrence of “Bernabéu” and the aligned cell “Capacity: 81,044”
(in a text corpus, e.g., Wikipedia, possibly with 81,044 relaxed into
any number around 80,000), could boost the linking to Estadio Santi-
ago Bernabéu rather than the footballer and club president Santiago
Bernabéu (after whom the stadium is named).
We incorporate these mutual benets by devising a joint objec-
tive function as follows.
Denition [Plausibility Maximization].
We dene the plausi-
bility of interpreting table
with entity linking
and column
alignment Λas:
λ·CA-score(Λ|Φ)+(1λ) · EL-score(Φ|Λ)(1)
is a tunable hyper-parameter. Here,
is the col-
lective inference of entity linking module considering E-column/Q-
column pairs selected by Λ.
Inference Algorithm.
For joint inference about CA and EL, we
adopt the collective classication method from [
], called ICA,
which was also used by [
]. This avoids the high complexity of full-
edged MRF inference, which would be prohibitive as our factor
graphs are very dense.
In essence, for each column alignment
, we compute the best
EL solution
conditioned on
using the ICA method. The pair
that maximizes the joint objective function (plausibility max-
imization in Equation 1) is chosen as the nal result.
3.4 Contextualization of Qfacts
We extract Qfacts based on the optimal pair
computed from
the joint inference model. All extracted Qfacts are contextualized
with the Q-column header, informative cue words from table cap-
tion, same-row cells, page title, all DOM-tree headings leading to
the table, and the text in proximity to the table (e.g., preceding and
following paragraph). All these components are optional. This way,
we capture cues such as “football clubs” for Table 1. We include all
words from these context items, forming a bag-of-words. The nal
output is a Qfact in the form
with entity
, quantity
contextualization X.
Extracting Contextualized antity Facts from Web Tables WWW ’21, April 19–23, 2021, Ljubljana, Slovenia
This section explains how we utilize external text corpora to com-
pute ext-score(F ) for the CA-score model of Section 3.2.
The key idea is to retrieve evidence for a candidate Qfact
, spotted from a table with Q-column
, in a larger corpus of
text, such as sentences from Wikipedia articles. To this end, we
employ the text-based extraction method from [
]: a trained LSTM
network classies sentences that contain at least one entity and
one quantity and tags proper pairs of entity and quantity, along
with informative context words from the sentence. Running this on
Wikipedia full-text, followed by removing duplicates and threshold-
ing on condence, we obtained a collection
of 1.6M million Qfacts
triples in the form
. By using an entity coreference resolu-
tion tool ( neuralcoref ) on two consecutive
sentences and combining them into one input, we enlarged this to
a total of 2.4M million Qfacts – with a fair amount of uncertainty,
We treat this collection
as external evidence against which we
can assess Qfact candidates distilled from web tables. A candidate
is highly-condent if related information can
be found in text, in particular, in C.
Denition [Evidence Score].
For Qfact
, the
evidence score from collection Cis:
ext-score(F ) =max
,X)∈Ce=esim (q,X),(q,X)
where sim(q,X),(q,X)=w1·sim1(q,q)+w2·sim2(X,X)
with tunable coecients
. The function nds the best match-
ing evidence Qfact in
with same entity
compares quantities
(after normalization to the same unit) and returns a score
that is equal to their relative numeric distance
max(| q|,|q|)
. We
consider a 1% dierence as a perfect match, because quantity values
are often rounded or truncated. Note that if the two quantities are
incomparable (from dierent concepts, e.g., length vs. monetary),
we do not consider
at all.
compares the Q-column
with the evidence context bag-of-words
by the
directed embedding distance of [
]. This rewards if the column
name appears in
, but also gives credit to dierent words that
are related by their word2vec embeddings.
Type-based Evidence.
Many of the candidate facts from tables
may not nd any text-based evidence by the above procedure. This
is natural, as we expect to obtain a large number of facts from tables
that cannot be spotted in text corpora at all. If this were not the
case, we would not need to tap into tables and could instead extract
from text only.
We can relax our notion of text evidence, however, and settle
for the softer task of spotting some Qfact evidence with the same
entity type as the candidate at hand. For example, to scrutinize the
candidate (Estadio Santiago Bernabéu, 81044, “Capacity”), we can
consider the text evidence (Old Traord, 74140, “Capacity”) or (Camp
Nou , 99354, “Capacity”). Intuitively, for an entity of the same type
, the cue word “Capacity” is important and the respective
quantities fall into the same order of magnitude. In contrast, when
examining candidates (Santiago Bernabéu, 81044, “Capacity”) and
(Real Madrid, 81044, “Capacity”), there is hardly any text evidence
that a
or a
has a Capacity. This reinforces the hypothesis
that the table-based candidate is valid, including the chosen EL
target (“Bernabéu” refers to Estadio Santiago Bernabéu, not to the
club president Santiago Bernabéu) and the CA inference (Capacity
refers to Stadium, not to Team).
Denition [Type-based Evidence Score].
For candidate Qfact
and each entity
sharing the same type with
, we
compute a type-based evidence score of Fwith respect to eas:
t-ext-score(F |e)=max
,X)∈Ce=erel(e,e) · sim(q,X),(q,X)
is the semantic relatedness between the two en-
tities (ext-score is actually a special case when
rel =
1). The
function can be based on distance measures in the underlying type
taxonomy, or alternatively by the cosine between word2vec (or
wikipedia2vec) embeddings. In our implementation, we chose the
shortest Wu-Palmer taxonomy distance [
] between the direct
types of two entities. This has the advantage that we can incorpo-
rate entities
incrementally in ascending order of distance. This
way, we eciently prune the huge space of potential evidence items.
Combining Scores.
A good fraction of the table-based Qfact can-
didates may have both kinds of text evidence: matching entities and
merely matching types. Thus, it is natural to combine both scores.
We dene the nal evidence score of
as the average of matching
evidence scores from top-kbest entities e(including eitself). We
hypothesize that this yields a more robust signal from the wealth
of text-based evidence.
Table 2 shows examples of top-scoring text evidence for examin-
ing the Qfact candidate (Allianz Arena, 75000, “Capacity”).
5.1 Matching and Ranking
All Qfacts from web tables are fully contextualized into the form
, stored and indexed. We process a Qquery
(qt,qq,qX )
against this data by mostly following the method of
]: Qfact entities are matched against the target type
using type
information from the KB, quantities are compared to query condi-
, and the context agreement between
is quantied
by the directed embedding distance of [
]. This yields a ranking of
entity answers to a given query.
We extend the context comparison, as our setting diers from
[16]. In text-based Qfacts, the context tokens come from the same
sentence or short snippet. In contrast, for the table-based setting,
we combine a set of cues from dierent kinds of context: Q-column
header, page title, table caption, DOM-tree headings, same-row
cells, and surrounding text window. To reect this heterogeneity,
we assign tunable weights to the context tokens based on their
Denition [Weighted Directed Embedding Distance].
w-ded(X,qX )= Õ
ω(u) · min
vX(σ(v) · d(u,v))!/ Õ
denoting tf-idf-based weights of tokens and
the weight of Qfact context token
depending on the kind of
context from where it originates. We have six dierent
for the six kinds of context considered (see above); they are not
is the word2vec embedding distance between
WWW ’21, April 19–23, 2021, Ljubljana, Slovenia Vinh Thinh Ho, Koninika Pal, Simon Razniewski, Klaus Berberich, and Gerhard Weikum
Table 2: Top-scoring text evidence for Qfact candidate (Allianz Arena, 75000, “Capacity”).
Evidence Qfact Type Source
(Allianz Arena, 75000, “capacity, now” ) Exact entity
In January 2015, a proposal to increase the capacity was approved by the city council so now Allianz Arena has
a capacity of 75,000 (70,000 in Champions League).
(Wembley Stadium, 90000, “ocial, capacity”)
It was revealed today that I have made an oer to purchase Wembley Stadium from The Football Association. ...
The stadium opened in 2007 and has an ocial capacity of 90,000.
(Great American Ball Park, 42271, “capacity”)Type-based Great American Ball Park opened in 2003 at the cost of $290 million and has a capacity of 42,271.
Algorithm 1: Consistency-based Re-scoring
Input :
Candidate Qfacts
F={F1,F2, . ..|Fi=(ei,qi,Xi)}
with initial scores for Qquery Q=(qt,qq,qX )
Output : Consistency-aware scores of candidate Qfacts
1Sample randomly a probe set from the candidate list PF.
2Train a Qfact quality predictor from the remaining
candidate Qfacts
, using initial scores as ground-truth.
3Run the learned predictor on the probe set Pto compute
quality scores for all Qfacts in P.
4Repeat steps 1-3 a large number of times.
5The consistency score of a candidate Qfact, cons-score(Fi), is
computed as the average quality predicted, aggregated
over all cases where Fiwas in the probe set.
two words
(from the query) and
(from the answer candidate).
In essence, this directed scoring function nds for each Qquery
context word
the best matching token
from the Qfact context,
taking context type into account by using σ(v).
5.2 Corroboration by Inter-Fact Consistency
We use the w-ded distances between candidate answers and the
query as initial scores for answer ranking. This initial ranking is
further improved by considering the mutual consistency among the
answer facts for the same query. To this end, we can exploit that
our data often yields several Qfacts for the same answer entity.
If all or most of them agree on their quantities and contextual
cues, their scores should be close to each other. This idea can be
generalized to all answer candidates even if they dier in their
entities: they should still mostly agree on their contextual cues, and
their quantities should have comparable order of magnitude. For
example, if the candidate pool for answering a query about “British
football stadiums with a seating capacity above 50,000” includes
spurious results like (Wembley Stadium, 32,000,000, “world cup, 1966,
TV viewers”), or (Maracana Stadium, 78,838, “FIFA, Rio, 2014”), these
stand out against many good results by having the wrong order of
magnitude in quantities or by missing important contextual cues
about UK.
To detect and leverage such situations for elimination or demo-
tion of noisy results, we have devised a method for consistency-
aware corroboration and re-scoring of answer candidates. This is
inspired by earlier work on consistency learning for image classi-
cation [39]. Algorithm 1 outlines this method.
The method is a form of self-validation, analogous to the prin-
ciple of cross-validation. We randomly sample a probe set from
the candidate Qfacts, and use the remaining Qfacts and their ini-
tial scores as ground-truth for training a quality predictor. The
learned predictor is applied to the probe set, and we keep track of
the predicted quality scores cons-score(Fi).
The dierence between the initial score and the consistency score
|w-ded cons-score|
denotes the condence of the initial score. A
high dierence between them denotes a noisy Qfact in the candidate
list (i.e., either a high-ranked bad-Qfact, or low-ranked good-Qfact),
which requires re-scoring.
Denition [Re-Scoring of Qfacts].
We re-score candidate fact
with regard to a query as a weighted combination of initial score
(using w-ded) and consistency-aware cons-score:
nal-score(F ) =(1ρ) · w-ded(F ) +ρ·cons-score(F )
with hyper-parameter ρto control the re-scoring eect.
Learned Predictors.
As this method requires frequent re-training
of the predictor, we choose a very simple k-NN technique, which
as the average initial scores of the
Qfacts in the training set. This avoids the bottleneck of explicit
re-training. We dene the distance between Qfacts as the weighted
combination of (1) the relative numeric distance between quantities
(converted to standard units) and (2) the context similarity. The
latter is computed by a vector space model, with features comprising
the tf-idf values of context terms weighted by the context item
from which they originate (column header, table caption, etc.).
6.1 Intrinsic Evaluation of QuTE Components
We present experimental results on the key components of Qfact ex-
traction: entity-quantity column alignment (CA) and entity linking
(EL). The contextualization of Qfacts and the inter-fact consistency
model matter only at query-time, and are thus evaluated in that
extrinsic use case in Section 6.2.
Hyper-Parameter Tuning.
Our method has a number of hyper-
parameters for Qfact extraction:
in Equation 1; weights for dif-
ferent context categories; and weights for the text-based evidence
scoring model. For tuning these, we performed a grid search to de-
termine the conguration with the best performance on a withheld
validation dataset.
Testsets. Our experiments use three table collections:
Wiki_Links-Random: a dataset introduced by [
], sampling 3000
tables from Wikipedia. As we are only interested in tables that
express quantity properties, we lter this data and obtain a set
of 259 tables, referred to as Wiki_Links-Random_Qt.
Equity: a set of 69 content-rich tables introduced by [
]. Analo-
gously to Wiki_Links-Random, we lter for tables with quantities,
which results in a set of 30 tables, called Equity_Qt.
Wiki_Di: We observe that many tables from the above two
datasets are easy cases for column alignment. Very often, the
linked E-column is the rst one, or the table has only one E-
column, so linking all Q-columns to that one is trivially correct.
Hence, we compile a new dataset called Wiki_Di, consisting
Extracting Contextualized antity Facts from Web Tables WWW ’21, April 19–23, 2021, Ljubljana, Slovenia
Table 3: Column alignment precision (macro_avg).
Method Wiki_L-R_Qt Equity_Qt Wiki_Di
Leftmost 0.736 0.817 0.045
Most-Unique 0.868 0.873 0.409
Closest-Left 0.728 0.674 0.705
Classier [37] 0.864 0.717 0.597
Iterative CA 0.934 0.900 0.769
Table 4: Entity linking precision (micro_avg).
Method Wiki_L-R_Qt Equity_Qt Wiki_Di
Prior 0.849 0.821 0.846
EL-MRF [3] 0.893 0.863 0.902
Joint EL&CA 0.900 0.876 0.902
of 134 Wikipedia tables, which are dicult cases for column
alignment: there are at least two E-columns and the referred
E-column is not the rst one, or dierent Q-columns refer to
dierent E-columns.
All three datasets originally contain only ground truth for entity
linking; we annotated them with the proper column alignment.
Performance Metrics.
For the CA task, we use the precision of
correct alignments, macro-averaged over tables. Since there are
many tables where all Q-columns refer to the same E-column,
macro-averaging is meaningful to give each table the same weight
(regardless of its width). For entity linking (EL) the metric is the
precision, micro-averaged over entity mentions.
Results for Column Alignment.
We compare our Iterative CA
method with text-based evidence against several baselines (see
Section 3.1): (1) Leftmost,(2) Most-Unique,(3) Closest-Left, and a (4)
Classier with features from column-wise properties (column-pair
distances, distinct values per column, etc.) as employed by [37].
The results are shown in Table 3. We observe that our Iterative
CA method outperforms all baselines by a large margin over all
three datasets. This gives our approach a decisive advantage in
extracting more and better quantity facts from web tables.
Results for Entity Linking.
Although our EL method mostly
follows prior works [
], we report the performance of EL when
computing jointly with CA, against two baselines: (1) Prior uses
popularity of mention-entity pairs to link each mention to the most
salient entity that matches the name, and (2) EL-MRF [
] is a state-
of-the-art method based on MRF that incorporates priors, context
similarity, row-wise coherence and column-wise coherence, but
does not consider CA.
Table 4 shows that the Joint EL&CA method is as good as and
sometimes better than the baselines, on all three datasets. Although
the improvement over EL-MRF is not that large, it is notable and
shows the positive impact of integrating CA information on the
inference of EL.
Ablation Study.
To analyze the inuence of dierent components
of our CA method, we conducted a comprehensive ablation study,
by selectively disabling the following components: (1) type-based
evidence for text-based scoring, and (2) coreference resolution for
entity mentions when building the background Qfact collection
from text. The results are shown in Table 5.
Table 5: Ablation study results for CA.
Method Wiki_L-R_Qt Equity_Qt Wiki_Di
Iterative CA 0.934 0.900 0.769
type-based evidence 0.796 0.617 0.254
coreferences 0.877 0.892 0.728
Table 6: Table collection statistics.
Source #tables #E-Q-tables #distinct-entities #qfacts
Wikipedia 1.8M 339K 757K 8.87M
TableL 2.6M 278K 255K 9.94M
Total 4.4M 618K 863K 18.81M
We observe that without type-lifted evidences, the CA precision
decreases by more than 10 percent on all three datasets; for the
dicult dataset Wiki_Di, performance even drops by 50 percent.
Exact-entity matching alone is insucient as it suers from the
sparseness. This emphasizes the decisive role of our novel contribu-
tion to leverage external text evidence, as opposed to prior works
that restricted information extraction from web tables to the tables
themselves (and their local context). Disabling coreference reso-
lution, for collecting background Qfacts from text, also degrades
precision, but to much lesser degree: 5 percent at most.
6.2 Extrinsic Evaluation of Search and Ranking
This section presents experimental results for an end-to-end use
case of quantity queries and their result rankings.
Hyper-Parameter Tuning.
Analogously to Section 6.1, we tune
query-time hyper-parameters for the w-ded distance and for the
mixture with cons-score (see Section 5.2) by grid search for best
Precision@10 on a withheld validation set.
Datasets. We run queries on a large collection of web tables com-
piled from two major sources:
TableL: introduced by [
]. It contains 2.6M tables from 1.5M
web pages, mostly falling under ve major topics: nance, envi-
ronment, health, politics, and sports.
Wikipedia Tables: rst introduced by [
]. As the original collec-
tion from 2015 is outdated, we processed a recent version of
the English Wikipedia XML dump (March 2020) to construct an
analogous dataset, containing a total of 1.8M tables.
The combined collection was ltered for tables that contain both
E-columns and Q-columns. Table 6 shows data statistics of the
large scale extraction, where we report the number of ltered E-Q-
tables, the number of extracted Qfacts, and the number of extracted
entities for each table corpus. In total, we end up with 618K tables
and 18.8M extracted Qfacts, ready for large scale search.
Query Benchmarks.
We use two sets of telegraphic queries:
Q100: an established benchmark of 100 quantity queries from
], featuring questions on a range of quantity measures for
four domains: Finance,Transport,Sports and Technology. Ground-
truth answers are annotated as relevant or irrelevant for the
top-10 results of the original, text-based work in [
]. We extend
these annotations to the top-10 results of all methods under
comparison. However, there is no ground-truth about ideal top-
10 results, like lists of all answers or answers sorted in ascending
WWW ’21, April 19–23, 2021, Ljubljana, Slovenia Vinh Thinh Ho, Koninika Pal, Simon Razniewski, Klaus Berberich, and Gerhard Weikum
or descending order of quantity value (e.g., the largest stadiums
for a query about “sports arenas with capacity above 50K”). So
there is no way to evaluate recall with this benchmark.
NewQ150: To allow evaluating both precision and recall, we con-
structed a new collection of 150 queries, similar in nature to
those of Q100 but such that each of them has a ground-truth
answer list. To this end, we identied Wikipedia list pages that
either capture the desired query result or provide a superset that
is sorted by the quantity of interest. Examples for this kind of
ground-truth is a list of all sprinters who ran 100 meters under 10
seconds, which by its sorting, also provides a sub-list of results
under 9.9 seconds.
Performance Metrics.
For both benchmarks, we report Preci-
sion@10, macro-averaged over 100 or 150 queries, respectively. For
NewQ150, we also report Recall@10 and mAP@10 with regard to
the answers in the ground-truth list.
To the best of our knowledge, QuTE is the rst system
addressing quantity lters based on web tables. Therefore, there
is no direct reference baseline; instead we compare against two
strong baselines on quantity search over textual and general web
Qsearch is a text-based quantity search engine [
] (accessi-
ble at https:// It runs on a collection of
21.7M Qfacts automatically extracted from sentences in Wikipedia
articles and news articles from the New York Times archive and
web crawls.
This setup is not comparable to our QuTE method, as the under-
lying data sources are not the same. Nevertheless, having this
baseline gives insights on the value of tapping into web tables.
Google serves as the reference point for search-engine methodol-
ogy. When we pose our benchmark queries, Google returns ten
blue links along with preview snippets. The results are typically
a mix of highly informative snippets, irrelevant snippets, and
links to authoritative lists. These list pages often contain very
good results, but the user would have to explicitly access and
browse them (as opposed to being provided with direct answers
in terms of entities).
For Google results, we assess the top-10 answer quality (with regard
to the ground-truth top-10) in two dierent modes:
Direct answers (Google-DA): only named entities that appear in
the preview snippets are considered. This is a conservative mode,
assuming lazy users who do not engage on further browsing.
List expansion (Google-LE): each list-page answer (with the word
“list” in its title) is fetched to materialize the list of entities, in the
order of the list itself. Conceptually, this is done for each top-10
result of this kind, and the resulting lists are concatenated. The
top-10 entities are considered as query answers in this mode,
where users continue browsing.
Main Results.
The precision results for the Q100 benchmark are
shown in Table 7. We see that Qsearch performs best for the top
rank alone, but drops in precision with more results. This is because
it is designed to retrieve a few high-condence results and has very
limited recall due its data based on single sentences. QuTE has lower
precision but keeps this fairly high also for lower ranks, being able
to nd more correct answers from its table collection. The weak
Table 7: Performance results for Q100.
System Prec.@1 Prec.@5 Prec.@10
Google-DA 0.340 0.280 0.274
Google-LE 0.460 0.518 0.462
Qsearch 0.690 0.559 0.492
QuTE 0.540 0.512 0.491
Table 8: Performance results for NewQ150.
System Prec.@10 Recall@10 mAP@10
Google-DA 0.167 0.076 0.041
Google-LE 0.342 0.251 0.193
Qsearch 0.290 0.177 0.119
QuTE 0.519 0.341 0.294
results for Google-DA show that search engines are really missing
the ability to compute direct answers for quantity lters. Google-LE
performs better, benetting from list expansion because it often has
one or two good super-lists of proper results in its 10 “blue links”.
The results for the NewQ150 benchmark are shown in Table 8,
including recall and mAP for the top-10 query results. Here we see
that QuTE clearly outperforms all baselines, especially in terms of
recall@10 and mAP@10. Extracting Qfacts from web tables with
high yield enables QuTE to compute many correct answers. Qsearch
is limited by its text-based pool of candidate answers. The search
engine again shows its missing support for quantity lters in direct-
answers mode; in list-expansion mode, it performs much better but
is still inferior to QuTE.
Table 9 shows a few anecdotal query results obtained by QuTE.
Ablation Study.
To obtain insight into which components con-
tribute how much, we performed an in-depth ablation study, by
(1) discarding table-context categories from the contextualization
step: dropping table captions, page titles, etc., except the Q-column
header which was always kept as the most vital cue, and (2) dis-
abling the inter-fact corroboration phase. The results of this study
are shown in Tables 10 and 11 for Q100 and NewQ150 benchmarks,
We observe that page titles are the most important element for
the contextualization step; discarding them led to a substantial drop
in performance. As for the other context categories, their disabling
resulted in some performance uctuation, but overall their inuence
is relatively minor. So the bottom line is that page titles and column
headers are crucial for Qfact extraction, and additional context
categories do not have substantial benets due their inherent noise.
The results also show that the inter-fact consistency corrobora-
tion is a vital component that improves the quality of top-10 results.
Though the improvement is small (ca. 2 percent), the p-value from
a paired t-test suggests that this improvement is statistically signif-
icant (0.034 and 0.019 for Q100 and NewQ150 benchmarks).
Text-based vs. Table-based Search.
In terms of precision, Table 7
suggests that table-based (QuTE) and text-based query answering
(Qsearch) produce results of comparable quality. Does that imply
that they are interchangeable? However, a closer analysis shows
that they are not simply interchangeable, but rather return com-
plementary results. For Q100, each of the two methods has about
45% unique answers in their correct top-10 (not found by the other
Extracting Contextualized antity Facts from Web Tables WWW ’21, April 19–23, 2021, Ljubljana, Slovenia
Table 9: Anecdotal examples of quantity queries and top results by QuTE.
Query Top Results
Skyscrapers higher than 1000 feet Empire State Building, One World Trade Center, The Shard, Chrysler Building, etc.
British football teams worth more than 1.5 billion pounds Manchester United F.C., Arsenal F.C., Liverpool F.C., Chelsea F.C., Manchester City F.C.
Sprinters who ran 100 meters under 9.9s Usain Bolt, Carl Lewis, Maurice Greene, Justin Gatlin, Christian Coleman, etc.
Mobile games with number of players more than 250 million Angry Birds, Super Mario Run, Candy Crush Saga, Temple Run, Pokémon Go, etc.
Table 10: Ablation study results on Q100.
Method Prec.@1 Prec.@5 Prec.@10
QuTE 0.540 0.512 0.491
page title 0.450 0.438 0.421
table caption 0.550 0.522 0.477
same-row cells 0.520 0.502 0.485
dom-tree headings 0.540 0.504 0.486
surrounding text 0.560 0.504 0.481
corroboration 0.530 0.494 0.475
Table 11: Ablation study results on NewQ150.
Method Prec.@10 Recall@10 mAP@10
QuTE 0.519 0.341 0.294
page title 0.434 0.286 0.233
table caption 0.495 0.327 0.277
same-row cells 0.513 0.338 0.295
dom-tree headings 0.521 0.341 0.293
surrounding text 0.513 0.336 0.289
corroboration 0.497 0.327 0.279
method). For NewQ150, which tends to have more dicult queries,
the fractions are 18% for QuTE and 8% for Qsearch.
Quantity Recognition.
Detecting quantities in text and tables
has been well researched, with prevalent methods based on rules,
CRFs or neural learning [
]. This involves recognizing
numeric expressions in combination with units, and ideally includes
also normalization of values (considering scale indicators as in “10
mio” or “10K”) and conversions of units (e.g., from US dollars into
GBP or MPG-e into kWh/100km). Normalization and conversions
are handled via rules. This prior work solely focuses on the numeric
quantity alone, and does not include inferring to which entity the
quantity refers. Moreover, it does not identify contextual cues that
are necessary for querying. Our paper starts with state-of-the-art
quantity recognition, and makes novel contributions on inferring
respective entities and relevant contexts.
Fact Extraction from Web Tables.
Ad-hoc tables in HTML pages
and spreadsheet contents have been studied as a target for entity
and concept linking, fact extraction, search and question answering.
The surveys by [6] and [41] discuss the relevant literature.
Our work builds on state-of-the-art entity linking for web tables
] sharing the general approach of combining per-
row contexts with per-column coherence based on probabilistic
graphical models or random walks.
Prior methods for fact extraction from tables, for the task of KB
augmentation, have followed the standard model of SPO triples,
with focus on entity linking for the S and O arguments from the
same row [
]. Target predicates P are assumed to
come from a pre-existing knowledge base (as opposed to OpenIE).
None of the prior works distinguish whether the O column contains
entities or numeric quantities. In contrast, our method includes
specic techniques to handle quantity columns.
A prevalent assumption is that there is a single subject column
where all S arguments come from, regardless of the choice of O
column. Some works use the heuristics that S is the leftmost non-
numeric column of a table; other works employ a supervised clas-
sier based on simple features of candidate columns [
]. Our
approach does not make this assumption of a single subject column,
thus being able to tap into more complex content-rich tables. The
only prior work that considered multiple S-columns is [
]. This
method critically relies on the detection of approximate functional
dependencies and value correlations between column pairs. This
does not work for quantity columns, though, as their values can be
anywhere between all-distinct and many-duplicates (e.g., if stadium
capacities in Table 1 were crudely rounded to 50K, 60K, etc.).
Entity Search and Question Answering.
Entity-centric search
and question answering are broad areas that cover a variety of
information-seeking needs, see surveys like [
]. As far as
quantities are concerned, lookups are supported by many methods,
over both knowledge graphs and text documents, and are part
of major benchmarks, such as QALD [
], NaturalQuestions [
ComplexWebQuestions [
], LC-QuAD [
] and others. However,
lookups such as “What is the value of Real Madrid?” or “energy
consumption of Toyota Prius Prime” are much easier to process than
queries with quantity lters. The former do not need to interpret
quantities in terms of measure, value and unit, whereas this is
crucial for evaluating lter conditions. The only prior work that
specically addressed quantity lters is [
], which was solely
based on textual contents, though.
Search and QA over web tables have been addressed in various
settings. Methods in [
] support querying heterogeneous
collections of tables, but focus on the joint mapping of keywords
onto entities and column headers in the underlying data. Quantity-
lter queries are not addressed.
This paper presents the rst method, called QuTE, for extracting
quantity facts from web tables, to support queries with quantity
lters. In experiments, QuTE clearly outperforms both prior works
on text-based Qfacts and a major search engine. An overarching
goal of this work is to extensively populate a high-quality knowl-
edge base with quantity properties, including advanced measures
such as energy consumption and carbon footprint for car models.
This is ongoing and future work.
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... The research that led to this system demo proposal pursues the goal of supporting web-based quantity queries, bringing database functionality to unstructured and semi-structured content. Our work includes tapping into textual contents [5,6] as well as web tables (embedded in HTML pages) [7]. This demo paper is about the latter. ...
... It has two major components: 1) extracting entity-quantity facts from tables using machine-learning techniques, and 2) matching queries against an indexed repository of such facts and computing ranked answer lists. The extraction component is described in detail in [7]. The query processor is based on techniques originally developed in [5] for the different case of quantitites in text documents. ...
... Upfront, it is unclear whether Capacity refers to Team or Stadium and whether Value is a property of Team, Stadium or Coach. In [7] we have devised a novel solution for this column alignment problem, with much higher precision than prior baselines [3,11,18]. ...
In a relational table, core columns represent the primary subject entities that other columns in the table depend on. While discovering core columns is crucial for understanding a table’s semantic column types, column relations, and entities, it is often overlooked. Previous methods typically rely on heuristic rules or contextual information, which can fail to accurately capture the dependencies between columns and make it difficult to preserve their relationships. To address these challenges, we introduce Dependency-aware Core Column Discovery (DaCo), an iterative method that uses a novel rough matching strategy to identify both inter-column dependencies and core columns. Unlike other methods, DaCo does not require labeled data or contextual information, making it suitable for practical scenarios. Additionally, it can identify multiple core columns within a table, which is common in real-world tables. Our experimental results demonstrate that DaCo outperforms existing core column discovery methods, substantially improving the efficiency of table understanding tasks.
Providing machines with comprehensive knowledge of the world's entities and their relationships has been a long-standing vision and challenge for AI. Over the last 15 years, huge knowledge bases, also known as knowledge graphs, have been automatically constructed from web data, and have become a key asset for search engines and other use cases. Machine knowledge can be harnessed to semantically interpret texts in news, social media and web tables, contributing to question answering, natural language processing and data analytics. This position paper reviews these advances and discusses lessons learned. It highlights the role of "DB thinking" in building and maintaining high-quality knowledge bases from web contents. Moreover, the paper identifies open challenges and new research opportunities. In particular, extracting quantitative measures of entities (e.g., height of buildings or energy efficiency of cars), from text and web tables, presents an opportunity to further enhance the scope and value of knowledge bases.
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Open-domain textual question answering (QA), which aims to answer questions from large data sources like Wikipedia or the web, has gained wide attention in recent years. Recent advancements in open-domain textual QA are mainly due to the significant developments of deep learning techniques, especially machine reading comprehension and neural-network-based information retrieval, which allows the models to continuously refresh state-of-the-art performances. However, a comprehensive review of existing approaches and recent trends is lacked in this field. To address this issue, we present a thorough survey to explicitly give the task scope of open-domain textual QA, overview recent key advancements on deep learning based open-domain textual QA, illustrate the models and acceleration methods in detail, and introduce open-domain textual QA datasets and evaluation metrics. Finally, we summary the models, discuss the limitations of existing works and potential future research directions.
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Tables are powerful and popular tools for organizing and manipulating data. A vast number of tables can be found on the Web, which represent a valuable knowledge resource. The objective of this survey is to synthesize and present two decades of research on web tables. In particular, we organize existing literature into six main categories of information access tasks: table extraction, table interpretation, table search, question answering, knowledge base augmentation, and table augmentation. For each of these tasks, we identify and describe seminal approaches, present relevant resources, and point out interdependencies among the different tasks.
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We analyze comparative questions, i.e., questions asking to compare different items that were submitted to Yandex in 2012. Responses to such questions might be quite different from the simple “ten blue links” and could, for example, aggregate pros and cons of the different options as direct answers. However, changing the result presentation is an intricate decision such that the classification of comparative questions forms a highly precision-oriented task.From a year-long Yandex log, we annotate a random sample of 50,000 questions; 2.8% of which are comparative. For these annotated questions, we develop a precision-oriented classifier by combining carefully hand-crafted lexico-syntactic rules with feature-based and neural approaches—achieving a recall of 0.6 at a perfect precision of 1.0. After running the classifier on the full year log(on average, there is at least one comparative question per second),we analyze 6,250 comparative questions using more fine-grained subclasses (e.g., should the answer be a “simple” fact or rather a more verbose argument) for which individual classifiers are trained.An important insight is that more than 65% of the comparative questions demand argumentation and opinions, i.e., reliable direct answers to comparative questions require more than the facts from a search engine’s knowledge graph.In addition, we present a qualitative analysis of the underlying comparative information needs (separated into 14 categories like consumer electronics or health), their seasonal dynamics, and possible answers from community question answering platforms.
The aim of this survey is to bridge two important components of modern information access: information retrieval (IR) and knowledge graphs (KGs). Modern IR systems can benefit from information available in KGs in multiple ways, independent of whether the KGs are publicly available or proprietary ones. The authors provide an overview of the literature on KGs in the context of IR and the components required when building IR systems that leverage KGs. As an understanding of the intersection of IR and KGs is beneficial to many researchers and practitioners, they consider prior work from two complementary angles: leveraging KGs for information retrieval and enriching KGs using IR techniques. They summarize research work, group related approaches, and discuss challenges shared across tasks at the interface of IR and KGs. In Knowledge Graphs: An Information Retrieval Perspective, the authors present an extensive overview of tasks related to KGs from an IR perspective, provide a thorough review for each task, and present discussions on common issues that are shared among the tasks. They discuss common issues that appear across the tasks that consider and identify future directions for addressing them. They also provide pointers to datasets and other resources that should be useful for both newcomers and experienced researchers in the area.
Providing machines with the capability of exploring knowledge graphs and answering natural language questions has been an active area of research over the past decade. In this direction translating natural language questions to formal queries has been one of the key approaches. To advance the research area, several datasets like WebQuestions, QALD and LCQuAD have been published in the past. The biggest data set available for complex questions (LCQuAD) over knowledge graphs contains five thousand questions. We now provide LC-QuAD 2.0 (Large-Scale Complex Question Answering Dataset) with 30,000 questions, their paraphrases and their corresponding SPARQL queries. LC-QuAD 2.0 is compatible with both Wikidata and DBpedia 2018 knowledge graphs. In this article, we explain how the dataset was created and the variety of questions available with examples. We further provide a statistical analysis of the dataset.
We propose a new end-to-end method for extending a Knowledge Graph (KG) from tables. Existing techniques tend to interpret tables by focusing on information that is already in the KG, and therefore tend to extract many redundant facts. Our method aims to find more novel facts. We introduce a new technique for table interpretation based on a scalable graphical model using entity similarities. Our method further disambiguates cell values using KG embeddings as additional ranking method. Other distinctive features are the lack of assumptions about the underlying KG and the enabling of a fine-grained tuning of the precision/recall trade-off of extracted facts. Our experiments show that our approach has a higher recall during the interpretation process than the state-of-the-art, and is more resistant against the bias observed in extracting mostly redundant facts since it produces more novel extractions.