=Paper=
{{Paper
|id=Vol-1532/paper1
|storemode=property
|title=A Language-Aware Web will Give us a Bigger and Better Semantic Web
|pdfUrl=https://ceur-ws.org/Vol-1532/paper1.pdf
|volume=Vol-1532
|dblpUrl=https://dblp.org/rec/conf/esws/Stellato15
}}
==A Language-Aware Web will Give us a Bigger and Better Semantic Web==
https://ceur-ws.org/Vol-1532/paper1.pdf
A Language-Aware Web will Give Us
a Bigger and Better Semantic Web
Armando Stellato
ART Group, Dept. of Computer Science, Systems and Production
University of Rome, Tor Vergata
Via del Politecnico 1, 00133 Rome, Italy
stellato@info.uniroma2.it
Abstract. The role of natural language is becoming in these years a more and
more acknowledged aspect of the Semantic Web. Not limited to mere modeling
and representation proposals, but backed by concrete use-cases and scenarios, the
use of natural language is emerging through a plethora of approaches and
solutions. Now that we have languages and protocols for modeling and
publishing content, for querying, and for efficiently describing datasets and
repositories (metadata), it is time for natural language to regain its due space and
become a first-class-citizen in the Web of Data. Lexical resources need to comply
with standard, unifying vocabularies upon which they can be discovered, chosen,
evaluated and ultimately queried upon need. At the same time, NLP systems and
components should pull their head out of their esoteric corner and become
classifiable, discoverable and interactive elements in the Semantic Web, so that
many language related tasks can be carried on more easily thanks to coordinating
modules/agents sensible to this information.
In this position paper, I will provide by first a quick outlook into the last years of
language and ontologies and describe what the community has achieved by the
state-of-the-art. I will then discuss open points, and try to draw conclusions,
based on my perspective and contributions to this research field, towards the
future of a more Language-Aware Semantic Web.
1 Introduction
More than a decade has passed since the first OntoLex workshops [1,2] and the earliest
attempts at modeling lexical resources and language aspects of ontologies in the
Semantic Web, and a whole community has grown around these intents.
Even though no specific criticisms exists in literature, the objectives of this
community are sometimes being claimed by critics and detractors to be unreasonably
complex, looping in a never-ending lack of consensus and providing no benefit to the
Web of Data. However, the role of natural language has become in recent years a more
and more acknowledged aspect of the Semantic Web. Not limited to mere modeling
and representation proposals, but backed by concrete use-cases and scenarios (e.g.
question answering [3,4], ontology-based information extraction [5], ontology learning
[6], semi-automatic lexical enrichment [7,8,9]), “ontolexical ideas” are now emerging
as a plethora of approaches and solutions
Still the question arises: what can we do with a more linguistically-aware Web? Can
it contribute to the Web of Data, which is meant to be food for machines, or will it be
doomed to consume itself, in a niche of language-oriented tasks?
As we have learned from the slow evolution of the Semantic Web itself, its vision
has not lost any of its original power, yet some of the related innovations and problems
took time to be acknowledged, or required foundations to be laid before they even
started to make sense. For many years, “language” has always been the seasoning added
by experts for improving performances and quality in data-intensive tasks such as
ontology alignment, question answering over ontologies and knowledge acquisition,
decorating them with evocative terms such as “language-based” or “language-driven”,
and still being out of any specification on the Web architecture. Now that we have
languages and protocols for modeling and publishing content, for querying, and for
efficiently describing datasets and repositories (metadata), it is time for natural
language to regain its due space and become a first-class citizen in the Web of Data.
lexical resources need not only to be represented as Linked Data, but to comply with
standard, unifying vocabularies upon which they can be discovered, chosen, evaluated
and finally queried upon need. At the same time, NLP systems and components should
pull their head out of their esoteric corner, exploit the stream of innovation brought by
software provisioning mechanisms such as Maven Repositories and OBR, breed it with
Linked Open Data (LOD) principles, and become classifiable, discoverable and
interactive elements in the Semantic Web. With such an elaborated web-of-language
architecture, those same tasks mentioned above, which today require lot of pre-
processing and fine-tuning, should be carried out more easily (if not on-the-fly) thanks
to coordinating modules/agents sensitive to the lexical information present in
ontologies, datasets, lexical resources and in NLP software and services.
In this position paper, I will provide by first a quick outlook into the last years of
language and ontologies and see what the community has achieved by now. I will then
discuss open points, and try to draw conclusions, based on my perspective and
contributions to this research field, towards the future of a more Language-Aware
Semantic Web.
2 A bit of history…
The first events in the area of ontologies and lexical resources were the OntoLex series
of workshops [1,2]. OntoLex covered, tout court, all research dealing with the
representation of lexical resources (or linguistic content to a larger extent) in the
Semantic Web world, with the relationships between formal ontologies and lexical
semantics in general, and more specifically in the construction of lexical knowledge
bases. The workshop also dealt with various applications of lexical semantics to
information retrieval, information extraction and related fields.
With the end of the OntoLex experience (Ontolex 2010, [10]), other events inherited
its mission. Among the various initiatives, we mention the series of workshops “Linked
Data in Linguistics (LDL)” [11], which is focused on discussing principles, case
studies, and best practices for representing, publishing and linking linguistic data
collections, and infrastructure such as corpora, dictionaries, lexical nets, translation
memories, thesauri. The workshops on natural language Processing and Linked Open
Data (NLP&LOD, [12]) focus on all the problematics related to the adoption of NLP
techniques in (and for) the LOD world. These include precision and quality of
information, the definition of standard vocabularies for both the syntactic and semantic
targets of extraction (such as Part of Speech vocabularies) and for describing NLP
chains as well. Finally, this same series of workshops, the Multilingual Semantic Web
[13] focus on the multilingual aspects of the Web of Data, and on the social, political
and economic implications that multilingual scenarios imply.
In parallel with events, many proposals have come out in these years, in terms of
models and vocabularies for representing lexical resources, for a better lexical
representation of ontologies, and for trying to standardize the plethora of efforts that
have been conducted in the NLP area, in the context of linked data.
A number of models have been proposed to enrich ontologies with information about
how vocabulary elements are expressed in different natural languages, including the
Linguistic Watermark framework [14,15], LexOnto [16], LingInfo [17], LIR [18],
LexInfo [19] and more recently lemon [20] and Lime [21].
Due to these efforts, concrete proposals emerged and were refined into new ones
through experience and discussion, and community group emerged to concretize
forthcoming standards for language-related aspects of the Web of Data.
The OntoLex W3C Community Group1 has the goal of providing an agreed-upon
standard by building on the aforementioned models, the designers of which are all
involved in the community group. Additionally, linguists have acknowledged [22] the
benefits that the adoption of the Semantic Web technologies could bring to the
publication and integration of language resources. As such, the Open Linguistics
Working Group2 of the Open Knowledge Foundation is contributing to the development
of a LOD (Linked Open Data) (sub)cloud of linguistic resources3.
These complementary efforts by Semantic Web practitioners and linguists are in fact
converging: the ontology lexicon model being defined in OntoLex provides a principled
way [23] to encode even notable resources such as the Princeton WordNet [24,25] and
other similar ones for other languages. At the same time, the Lexical Linked Data Cloud
[26] is providing a parallel Linked Open Data cloud aimed at linguistics and natural
language processing, by the same principles of the traditional LOD. To complete the
whole picture, and with an eye on computation and not only on resources, the NLP
Interchange Format (NIF, [27]) provides an RDF/OWL-based format that allows to
combine and chain several NLP tools in a flexible, light-weight way.
1 http://www.w3.org/community/ontolex/
2 http://linguistics.okfn.org/
3 http://nlp2rdf.lod2.eu/OWLG/llod/llod.svg
3 Is language so necessary in what is called “Web of Data” ?
By seeing all of these efforts from the very neutral perspective of someone approaching
the world of the Web of Data, one question would naturally arise: if the Semantic Web
is all about machine-understandable data, why should we care at all about language?
Can a more linguistically-aware Web contribute to the Web of Data, which is meant to
be food for machines, or will it be doomed to consume itself, in a niche of language-
oriented tasks? What can we do with it?
We could admit that large part of the research done in the area is more biased by the
(linguistic) background of the researchers working on it, than driven by the general
interest in the problems it tries to tackle. What puts in the same category all of the works
and efforts described until now, is the scientific and cultural background of the
communities working on them. As “Semantic Web” and “Linked Data” are not properly
“foundations of science” but applied sectors of fields of computer science such as
knowledge representation, knowledge and data management, and many others..,
researchers who contributed to any aspect of natural language in the Semantic Web
have their foundational background as well. Specifically, they flowed into the semantic
river as Computational Linguists (or pure Linguists), or, in any case, language experts
who wanted to give their contribution to the Semantic Web cause.
The risk of proposing solutions to issues which do not characterize the addressed
scenario is high, and this has probably occurred more than occasionally. For sure, a lot
of effort has been spent in addressing specific issues seen with the glasses of traditional
computational linguists. Without mentioning the specific works, any short look on the
literature of the Semantic Web area will return a plethora of articles on converting the
nth lexical resource to the kth OWL porting, or on leveraging existing “less noble”
resources to ontologies. Such is the case of the frenzied plethora of articles on lifting
the so-called “folksonomies” to ontologies (“folksonomies” which were strangely more
recent than the concept of ontology itself). All efforts that, by applying well known
techniques for knowledge synthesis, but lacking an overall perspective on the matter,
left neither new theoretical foundations nor concrete results (e.g. in term of resources)
for the future to come.
On the other side however, as it often happens in research, problems take their time
to be acknowledged, or may require foundations to be laid before they even start to
make sense. The efforts described in the previous section all aimed at providing general
frameworks for describing lexical resources, common loci for finding their descriptions
on the Web and metadata for properly selecting them. As well as much desired results
for trying to convey order in the plethora of models and processes which years of NLP
research have produced at the expenses of any engineering attempt in the field.
Together with the above, a few application scenarios have proven the usefulness of a
more aware representation of natural language content in the Web of Data. In the next
section I will describe the importance of such scenarios in the context of the whole Web
of Data, and how they can be improved by a more systematic inclusion of natural
language in the Semantic Web architecture.
4 Scenarios
I describe here three scenarios that prove the advantages brought by a more language-
aware approach to the Web of Data. These are, respectively:
Linguistic Enrichment of Ontologies
Ontology Alignment
Knowledge Acquisition
4.1 Linguistic Enrichment of Ontologies
It may seem redundant and self-referential to prove the importance of a more
linguistically-aware Web, by showing a scenario for linguistic enrichment.
Fact is that the provision of natural language descriptors (for how simple and de-
structured these can be) for ontological entities is a primary necessity which comes far
before the more elaborated features that we are trying to highlight. From the dawn of
computer programming, providing “meaningful and evocative names” for functions
(and later for classes/objects and methods) is a primary necessity for, to say the least,
improving the readability of source code. In this sense, ontologies and their RDF code
are no different from computer programs, and they need human understandable
descriptors in order to be maintained and evolved. Furthermore, in the case of
ontologies, the connection between concepts and their referents [28] even more so
needs proper symbols for being recognized and understood.
A complete ontology development process should thus consider the formal aspects
of conceptual knowledge representation, as well as guarantee that the same knowledge
be recognizable amongst its multiple expressions that are available on real data. Thus,
the importance of properly enriching ontologies with lexical content does not need
further support, while what we need to prove is that more attention to the representation
of language can improve the quality (and time to realization) of this necessary practice.
Ontology Development tools should support this task, providing users with
dedicated interfaces for efficient carrying it out. It appears obvious that the reuse of
linguistic resources creates added value, as they can contribute additional descriptors,
as well as suggesting additional knowledge which was not considered in the conceptual
development phase. Views over lexical resources must be integrated with classic views
over knowledge data such as class trees, property and instance lists, offering a set of
functionalities for linguistically enriching concepts and, possibly, for building new
ontological knowledge starting from linguistic one.
By considering past experiences [4,29,30] with knowledge based applications
dealing with concepts and their lexicalizations, a few basic functionalities for browsing
linguistic resources have emerged to be mandatory:
Searching for term definitions (glosses)
Asking for synonyms
Separating different senses of the same term
Exploring genus and differentia
Exploring resource-specific semantic relations
Fig. 1 Exploiting lexical resources in Ontoling in order to linguistically enrich ontologies
as well as some others for ontology editing:
Adding synonyms (or translations, for bilingual resources) as additional labels for
identifying concepts
Adding glosses to concepts description (documentation)
Using notions from linguistic resources to create new concepts
However, one question emerges: how are these tools supposed to interact with
lexical resources? In years of literature in NLP, many systems have adopted resource-
based approaches in order to perform given tasks, however the “knowledge” about
these resources, i.e., their semantics, were always “hardwired”, “embedded” within that
same systems that were exploiting their content. Besides doing the same work again
and again, these systems were lacking at the same time the capability to scale up to a
wider scenario including similar resources, or being adaptively capable of reusing
different ones…in different ways. In a word, they lacked a common lexical model, upon
which to see each (lexical) resource not as a world-per-se, but as the emanation of a
more general theory.
In [31] I have shown a general model for describing lexical resources that can be
used as a driver in different tasks, and for the linguistic enrichment of ontologies in
particular, by binding the enrichment functions to those same abstract descriptors, and
by adaptively shaping enrichment processes according to the (description of the) lexical
resource being exploited. This greatly enhances the possible application of the system,
as much as facilitates its adaptivity to different scenarios. In that work, the usefulness
of model for lexical resources is extended to adaptive algorithms for semi-automatic
enrichment as well.
4.2 (Semi-)Automatic Ontology Alignment
Ontology Matching [32] is the task of finding (semantic) correspondences between a
pair of ontologies. When any form of semantic agreement is missing – and this is the
scenario of ontology alignment, as reconciling semantic heterogeneity is its purpose –
the first common resource is generally natural language. The reconciliation of different
ontologies is thus driven by their linguistic grounding, then more advanced methods
have been devised, combining structural, extensional and semantic features. Some
approaches exploit background knowledge, such as the Web, Wikipedia, domain
corpora, lexical resources and upper-ontologies.
Since 2004, the OAEI4 organizes annual campaigns for the evaluation of automatic
ontology matchers. The general scenario for the contests is simple: two datasets, and
the objective to align their elements as much as possible. The contest then comprises
different challenges, differentiating by the type of dataset (an ontology, a thesaurus
instance data), the natural language(s) involved (same language, two different
languages, sets of languages available for each dataset), the elements to match etc.
While the participants to the contest are focused on developing more and more
complex techniques for alignment (and the results can obviously be considered
scientific as they are run on repeatable experimental settings), the question whether it
is important to improve performances on worst case scenarios – while the conditions
of these scenarios could be improved at the Web level – arises. Currently, there is no
widely adopted metadata vocabulary providing resuming information about the
available natural languages, their coverage of the analyzed resources and the modeling
vocabulary adopted to represent this information (are terms expressed as rdfs:labels,
through SKOS, SKOS-XL, or other advanced models such as those presented in section
2). While metadata can obviously be recomputed as a first processing step in a deep
alignment procedure, chances that these alignments could be applied “on the fly” are
severely reduced (in quality, if not on feasibility at all). If we want the Web of Data to
succeed, it is obvious that proper publication of data should be encouraged by providing
quality indicators, such as the Five Stars Open Data reference5. Metadata should be a
fundamental part of the Web, and vocabularies such as VoID provide useful
information that all publishers should provide. While it is important to foster research
in robust procedures considering worst cases, it should be also important to understand
what would be the benefit of a better acknowledgement of the available information in
a repository, and how this can be exploited.
By analyzing the conclusions from each of the workshops and related literature, we
can gather some interesting facts:
─ We are probably close to the achievable limits (discussion in OAEI 2013)
─ There is however still an incremental growth by tuning existing techniques [33]
─ State of the art systems can fail, if they do not understand their input (as seen in
all recent OAEI with SKOS challenges)
4 http://oaei.ontologymatching.org/
5 http://5stardata.info/
Fig. 2 UML Use Case for Linguistic Agents supporting Semantic Coordination
─ Performance in evaluations does not correlate straightforwardly with success in
real-world scenarios [34]
─ In many real-world scenarios, even simple string matching techniques applied to
lexically rich datasets may support effective semi-automatic processes [35]. As
often happens when human resources are available: overall recall is more
important than a high precision, as humans can always filter out spurious results
while would feel uncertain if they had doubts about the system filtering out
potentially good results.
In particular, in the library track of OAEI, dealing with the alignment of the thesauri
TheSoz6 and STW7:
6 http://www.gesis.org/en/services/tools-standards/social-science-thesaurus/
7 http://zbw.eu/stw/
─ Most of the participants in OAEI do not understand SKOS. Thus, thesauri need
to be sweeten into ontologies by means of a lossy and incorrect mapping [36]
─ As of 2014 results [37], “the baselines with preferred labels are still very good
and can only be beaten by one system”. Furthermore, the use of SKOS
annotations increases the performance of 7%
It thus seems that the quality of algorithms adopted is an order of magnitude less
important than properly filtering, cleaning, managing and, most importantly, being able
to exploit the linguistic information available in ontologies and datasets. Simply put,
the competing systems are currently not squeezing all the juice out of this very
important information, losing much of what would be easily available, while pushing
hard on disentangling un-understandable URIs and trying to grasp some knowledge
from their structural organization.
Unfortunately, as I remarked, this information is not explicitly available through any
dedicated vocabulary and most of these tools require fine-tuning by users, where they
should be able to self-configure. In a few words: machines need humans to tell them
how to read machine-understandable data!
As I explained in [38], linked open data, in order to become really operable by
machines with increasing levels of automation, needs to “talk about itself”, and this
obviously has to cover the language expressiveness of its content, in order to facilitate
alignment process which, as discussed above, would gain a lot from this asset. In that
work, I suggest bootstrapping techniques for allowing agents on the Web to start
linguistic coordination activities (see fig. 2) as a primary step of semantic coordination.
The word “agents” is intended with a wide interpretation, encompassing active agents,
such as software agents browsing the linked data and taking decisions on the basis of
the gathered results, passive agents such as SPARQL endpoints, and intermediate ones
such as semantic web services, which react to requests, but can have complex business
logics to satisfy their tasks.
In the context of the OntoLex community group, the scenario envisioned in [38] has
been made largely supported, thanks to the effort spent on Lime [21] for providing a
definitive metadata vocabulary, as part of the whole OntoLex suite of vocabularies, for
representing both lexical resources, and the lexical asset of ontologies, thesauri and
datasets in general. I hope that, with a larger adoption of such metadata, the vision of
agents able to cope autonomously with their heterogeneities, will be more detailed.
A recent attempt at exploiting information from Lime and from other available
metadata (such as the Vocabulary of Interlinked Datasets VoID [39]) is also reported
in [40].
4.3 Content Acquisition and Ontology Evolution
The acquisition of knowledge from unstructured content has maturated dramatically –
especially in the latest years – on the engineering aspects other than on the techniques
for content analysis. The TIPSTER [41] model has provided a reference architecture
for text processing, which has been implemented by systems such as GATE [42] and
has later influenced the OASIS8 standard UIMA [43].
Following the advent of RDF, a few attempts at empowering information extraction
architectures with data projection mechanisms have been presented. Some examples
are the RDF UIMA CAS (Common Analysis Structure) Consumer [44], Clerezza-
UIMA integration [45], and Apache Stanbol [46] and CODA [47].
Another important achievement has been the proposal for a NLP Interchange Format
(NIF, [48]), an RDF/OWL-based format that aims to achieve interoperability between
natural language Processing (NLP) tools, language resources and annotations. Such
approaches have been sometimes seen as too constraining by traditional NLP scientist:
naturally, studies in NLP have produced a plethora of different models and approaches,
which are difficult to put under a common umbrella, that is why all previous attempts
have focused on more model-agnostic solutions. However, again, the linked data
approach has proven to be successful: whereas UIMA and GATE provided neutral
feature-structure based solution, but have never seen the proliferation of vocabularies
for storing data according to different models (e.g. different set of part-of-speech tags),
the introduction of the linked data approach allows for different models to coexist and
to be reused.
However, again, in the same way as a linked data approach can benefit NLP, the
benefit can be reciprocal. Until now, NLP has been a very closed world: even when
getting out of the scientific field and moving to industry, NLP has been seen as a sort
of highly specialized sector where experts work out of ordinary software
performance/quality parameters and interoperability needs. This has explained the
limited success that it had until now, especially in smaller organization which are have
not the power to deal with the complexity that NLP processes require.
In my vision, those same NLP components the behavior of which is advertised as
linked data (in environments such as NIF), should be made available as open,
downloadable and pluggable components that can be dynamically agglomerated, in
order to assemble complete NLP chains. Much in the spirit of provisioning systems
such as Maven9, that are however focused (or, at least, commonly used for the large
part) on the provisioning of software libraries and on dependency resolution for
Integrated Development Environments, NLP components should be provisioned as
interconnectable pieces of a larger puzzle. Their metadata, and metadata coming from
linguistic resources and from ontologies as well, should all create a global ecosystem
in which it will be possible to target knowledge to be acquired/improved, sources of
information, potential supporting resources and the software needed to provide them.
5 Conclusions
In this article, which followed my keynote speech at the 4th Multilingual Semantic Web
Workshop (collocated with the 12th edition of the Extended Semantic Web Conference,
in Portoroz, Slovenia), I showed a few practical applications of a more linguistically-
8 Organization for the Advancement of Structured Information Standards
9 https://maven.apache.org/
aware Semantic Web, in terms of enriched possibilities for well known tasks, such as
lexical documentation, ontology alignment and content acquisition, which ultimately
can contribute to the growth of the Web itself.
The objective is to remark, once more, that despite (to reuse the words of Tim
Berners-Lee about his own creation) the “concept of machine-understandable
documents does not imply some magical artificial intelligence which allows machines
to comprehend human mumblings”, still this machine-understandability has its limits,
dictated by open and environments and heterogeneous needs. Allowing machines to
trade their knowledge when they have no better agreement on it, or to support humans
in extracting information from textual content, brings about a marriage with the sole
form of knowledge exchange that has endured hundreds of years of evolution: natural
language.
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