24 research outputs found
A Survey of the First 20 Years of Research on Semantic Web and Linked Data
International audienceThis paper is a survey of the research topics in the field of Semantic Web, Linked Data and Web of Data. This study looks at the contributions of this research community over its first twenty years of existence. Compiling several bibliographical sources and bibliometric indicators , we identify the main research trends and we reference some of their major publications to provide an overview of that initial period. We conclude with some perspectives for the future research challenges.Cet article est une étude des sujets de recherche dans le domaine du Web sémantique, des données liées et du Web des données. Cette étude se penche sur les contributions de cette communauté de recherche au cours de ses vingt premières années d'existence. En compilant plusieurs sources bibliographiques et indicateurs bibliométriques, nous identifions les principales tendances de la recherche et nous référençons certaines de leurs publications majeures pour donner un aperçu de cette période initiale. Nous concluons avec une discussion sur les tendances et perspectives de recherche
Active learning of link specifications using decision tree learning
In this work we presented an implementation that uses decision trees to learn highly accurate link specifications. We compared our approach with three state-of-the-art classifiers on nine datasets and showed, that our approach gives comparable results in a reasonable amount of time. It was also shown, that we outperform the state-of-the-art on four datasets by up to 30%, but are still behind slightly on average. The effect of user feedback on the active learning variant was inspected pertaining to the number of iterations needed to deliver good results. It was shown that we can get FScores
above 0.8 with most datasets after 14 iterations
A General Framework for Representing, Reasoning and Querying with Annotated Semantic Web Data
We describe a generic framework for representing and reasoning with annotated
Semantic Web data, a task becoming more important with the recent increased
amount of inconsistent and non-reliable meta-data on the web. We formalise the
annotated language, the corresponding deductive system and address the query
answering problem. Previous contributions on specific RDF annotation domains
are encompassed by our unified reasoning formalism as we show by instantiating
it on (i) temporal, (ii) fuzzy, and (iii) provenance annotations. Moreover, we
provide a generic method for combining multiple annotation domains allowing to
represent, e.g. temporally-annotated fuzzy RDF. Furthermore, we address the
development of a query language -- AnQL -- that is inspired by SPARQL,
including several features of SPARQL 1.1 (subqueries, aggregates, assignment,
solution modifiers) along with the formal definitions of their semantics
Federated Query Processing over Heterogeneous Data Sources in a Semantic Data Lake
Data provides the basis for emerging scientific and interdisciplinary data-centric applications with the potential of improving the quality of life for citizens. Big Data plays an important role in promoting both manufacturing and scientific development through industrial digitization and emerging interdisciplinary research. Open data initiatives have encouraged the publication of Big Data by exploiting the decentralized nature of the Web, allowing for the availability of heterogeneous data generated and maintained by autonomous data providers. Consequently, the growing volume of data consumed by different applications raise the need for effective data integration approaches able to process a large volume of data that is represented in different format, schema and model, which may also include sensitive data, e.g., financial transactions, medical procedures, or personal data. Data Lakes are composed of heterogeneous data sources in their original format, that reduce the overhead of materialized data integration. Query processing over Data Lakes require the semantic description of data collected from heterogeneous data sources. A Data Lake with such semantic annotations is referred to as a Semantic Data Lake. Transforming Big Data into actionable knowledge demands novel and scalable techniques for enabling not only Big Data ingestion and curation to the Semantic Data Lake, but also for efficient large-scale semantic data integration, exploration, and discovery. Federated query processing techniques utilize source descriptions to find relevant data sources and find efficient execution plan that minimize the total execution time and maximize the completeness of answers. Existing federated query processing engines employ a coarse-grained description model where the semantics encoded in data sources are ignored. Such descriptions may lead to the erroneous selection of data sources for a query and unnecessary retrieval of data, affecting thus the performance of query processing engine. In this thesis, we address the problem of federated query processing against heterogeneous data sources in a Semantic Data Lake. First, we tackle the challenge of knowledge representation and propose a novel source description model, RDF Molecule Templates, that describe knowledge available in a Semantic Data Lake. RDF Molecule Templates (RDF-MTs) describes data sources in terms of an abstract description of entities belonging to the same semantic concept. Then, we propose a technique for data source selection and query decomposition, the MULDER approach, and query planning and optimization techniques, Ontario, that exploit the characteristics of heterogeneous data sources described using RDF-MTs and provide a uniform access to heterogeneous data sources. We then address the challenge of enforcing privacy and access control requirements imposed by data providers. We introduce a privacy-aware federated query technique, BOUNCER, able to enforce privacy and access control regulations during query processing over data sources in a Semantic Data Lake. In particular, BOUNCER exploits RDF-MTs based source descriptions in order to express privacy and access control policies as well as their automatic enforcement during source selection, query decomposition, and planning. Furthermore, BOUNCER implements query decomposition and optimization techniques able to identify query plans over data sources that not only contain the relevant entities to answer a query, but also are regulated by policies that allow for accessing these relevant entities. Finally, we tackle the problem of interest based update propagation and co-evolution of data sources. We present a novel approach for interest-based RDF update propagation that consistently maintains a full or partial replication of large datasets and deal with co-evolution
Transforming into RDF and Interlinking Βig Geospatial Data
Στην εποχή των μεγάλων δεδομένων, μια μεγάλη ποσότητα γεωχωρικών δεδομένων είναι διαθέσιμη στο διαδίκτυο, προερχόμενη από κρατικές υπηρεσίες, εταιρίες και ερευνητικά έργα. Στις περισσότερες περιπτώσεις, αυτά τα δεδομένα δεν ακολουθούν το πρωτόκολλο των διασυνδεδεμένων δεδομένων και οι συνηθισμένοι μέθοδοι μετατροπείς τους έχουν αποδειχθεί ανεπαρκής, εξαιτίας του μεγάλου τους όγκου. Για αυτό τον λόγο, επεκτείνουμε το εργαλείο GeoTriples ώστε να μπορεί να μετατρέψει μεγάλα γεωχωρικά δεδομένα σε RDF γράφους, χρησιμοποιώντας το Apache Spark. Επιπλέον, μετατρέποντας τα δεδομένα σαν RDF τριπλέτες, μπορούμε να τα διασυνδέσουμε με άλλα υπάρχοντα συνδεδεμένα
δεδομένα και να εμπλουτίσουμε περαιτέρω το σύννεφο των Ανοικτών Διασυνδεδεμένων Δεδομένων (Linked Open Data cloud). Οπότε, σε αυτήν την εργασία παρουσιάζουμε επίσης κάποιους καινοτόμους αλγορίθμους για συνολική ή βαθμιαία διασύνδεση γεωχωρικών δεδομένων, αλλά και πως τους έχουμε παραλληλοποιήσει στο σύστημα DSJedAI, το οποίο δουλεύει πάνω στο Apache Spark. Στο τέλος, εκτελούμε αναλυτική αξιολόγηση των συστημάτων και αποδεικνύουμε ότι μπορούν να διαχειριστούν μεγάλα γεωχωρικά δεδομένα αποτελεσματικά.In the era of big data, a vast amount of geospatial data has become available from government agencies, businesses and research projects. In most cases, this data does not follow the linked data paradigm and the conventional methods for transforming it into linked data has been proved ineffective due to its large volume. For this purpose, we
extended GeoTriples, an open-source tool developed by our group, to be able to massively transform big geospatial data into RDF graphs, using Apache Spark. Furthermore, by transforming it into RDF, we can interlink it with other linked data and further populated the Linked Open Data Cloud. In this work, we also present novel algorithms for batch
and progressive Geospatial Interlinking, as well as how we have parallelized them in the system DSJedAI, that runs on top of Apache Spark. In the end, we perform a detailed evaluation of both systems and we show that they can operate on big geospatial data effectively
ネットワーク情報環境におけるメタデータの長期利用性向上のためのメタデータスキーマの来歴記述に関する研究
筑波大学 (University of Tsukuba)201
Integrating Natural Language Processing (NLP) and Language Resources Using Linked Data
This thesis is a compendium of scientific works and engineering
specifications that have been contributed to a large community of
stakeholders to be copied, adapted, mixed, built upon and exploited in
any way possible to achieve a common goal: Integrating Natural Language
Processing (NLP) and Language Resources Using Linked Data
The explosion of information technology in the last two decades has led
to a substantial growth in quantity, diversity and complexity of
web-accessible linguistic data. These resources become even more useful
when linked with each other and the last few years have seen the
emergence of numerous approaches in various disciplines concerned with
linguistic resources and NLP tools. It is the challenge of our time to
store, interlink and exploit this wealth of data accumulated in more
than half a century of computational linguistics, of empirical,
corpus-based study of language, and of computational lexicography in all
its heterogeneity.
The vision of the Giant Global Graph (GGG) was conceived by Tim
Berners-Lee aiming at connecting all data on the Web and allowing to
discover new relations between this openly-accessible data. This vision
has been pursued by the Linked Open Data (LOD) community, where the
cloud of published datasets comprises 295 data repositories and more
than 30 billion RDF triples (as of September 2011).
RDF is based on globally unique and accessible URIs and it was
specifically designed to establish links between such URIs (or
resources). This is captured in the Linked Data paradigm that postulates
four rules: (1) Referred entities should be designated by URIs, (2)
these URIs should be resolvable over HTTP, (3) data should be
represented by means of standards such as RDF, (4) and a resource should
include links to other resources.
Although it is difficult to precisely identify the reasons for the
success of the LOD effort, advocates generally argue that open licenses
as well as open access are key enablers for the growth of such a network
as they provide a strong incentive for collaboration and contribution by
third parties. In his keynote at BNCOD 2011, Chris Bizer argued that
with RDF the overall data integration effort can be “split between data
publishers, third parties, and the data consumer”, a claim that can be
substantiated by observing the evolution of many large data sets
constituting the LOD cloud.
As written in the acknowledgement section, parts of this thesis has
received numerous feedback from other scientists, practitioners and
industry in many different ways. The main contributions of this thesis
are summarized here:
Part I – Introduction and Background.
During his keynote at the Language Resource and Evaluation Conference in
2012, Sören Auer stressed the decentralized, collaborative, interlinked
and interoperable nature of the Web of Data. The keynote provides strong
evidence that Semantic Web technologies such as Linked Data are on its
way to become main stream for the representation of language resources.
The jointly written companion publication for the keynote was later
extended as a book chapter in The People’s Web Meets NLP and serves as
the basis for “Introduction” and “Background”, outlining some stages of
the Linked Data publication and refinement chain. Both chapters stress
the importance of open licenses and open access as an enabler for
collaboration, the ability to interlink data on the Web as a key feature
of RDF as well as provide a discussion about scalability issues and
decentralization. Furthermore, we elaborate on how conceptual
interoperability can be achieved by (1) re-using vocabularies, (2) agile
ontology development, (3) meetings to refine and adapt ontologies and
(4) tool support to enrich ontologies and match schemata.
Part II - Language Resources as Linked Data.
“Linked Data in Linguistics” and “NLP & DBpedia, an Upward Knowledge
Acquisition Spiral” summarize the results of the Linked Data in
Linguistics (LDL) Workshop in 2012 and the NLP & DBpedia Workshop in
2013 and give a preview of the MLOD special issue. In total, five
proceedings – three published at CEUR (OKCon 2011, WoLE 2012, NLP &
DBpedia 2013), one Springer book (Linked Data in Linguistics, LDL 2012)
and one journal special issue (Multilingual Linked Open Data, MLOD to
appear) – have been (co-)edited to create incentives for scientists to
convert and publish Linked Data and thus to contribute open and/or
linguistic data to the LOD cloud. Based on the disseminated call for
papers, 152 authors contributed one or more accepted submissions to our
venues and 120 reviewers were involved in peer-reviewing.
“DBpedia as a Multilingual Language Resource” and “Leveraging the
Crowdsourcing of Lexical Resources for Bootstrapping a Linguistic Linked
Data Cloud” contain this thesis’ contribution to the DBpedia Project in
order to further increase the size and inter-linkage of the LOD Cloud
with lexical-semantic resources. Our contribution comprises extracted
data from Wiktionary (an online, collaborative dictionary similar to
Wikipedia) in more than four languages (now six) as well as
language-specific versions of DBpedia, including a quality assessment of
inter-language links between Wikipedia editions and internationalized
content negotiation rules for Linked Data. In particular the work
described in created the foundation for a DBpedia Internationalisation
Committee with members from over 15 different languages with the common
goal to push DBpedia as a free and open multilingual language resource.
Part III - The NLP Interchange Format (NIF).
“NIF 2.0 Core Specification”, “NIF 2.0 Resources and Architecture” and
“Evaluation and Related Work” constitute one of the main contribution of
this thesis. The NLP Interchange Format (NIF) is an RDF/OWL-based format
that aims to achieve interoperability between Natural Language
Processing (NLP) tools, language resources and annotations. The core
specification is included in and describes which URI schemes and RDF
vocabularies must be used for (parts of) natural language texts and
annotations in order to create an RDF/OWL-based interoperability layer
with NIF built upon Unicode Code Points in Normal Form C. In , classes
and properties of the NIF Core Ontology are described to formally define
the relations between text, substrings and their URI schemes. contains
the evaluation of NIF.
In a questionnaire, we asked questions to 13 developers using NIF. UIMA,
GATE and Stanbol are extensible NLP frameworks and NIF was not yet able
to provide off-the-shelf NLP domain ontologies for all possible domains,
but only for the plugins used in this study. After inspecting the
software, the developers agreed however that NIF is adequate enough to
provide a generic RDF output based on NIF using literal objects for
annotations. All developers were able to map the internal data structure
to NIF URIs to serialize RDF output (Adequacy). The development effort
in hours (ranging between 3 and 40 hours) as well as the number of code
lines (ranging between 110 and 445) suggest, that the implementation of
NIF wrappers is easy and fast for an average developer. Furthermore the
evaluation contains a comparison to other formats and an evaluation of
the available URI schemes for web annotation.
In order to collect input from the wide group of stakeholders, a total
of 16 presentations were given with extensive discussions and feedback,
which has lead to a constant improvement of NIF from 2010 until 2013.
After the release of NIF (Version 1.0) in November 2011, a total of 32
vocabulary employments and implementations for different NLP tools and
converters were reported (8 by the (co-)authors, including Wiki-link
corpus, 13 by people participating in our survey and 11 more, of
which we have heard). Several roll-out meetings and tutorials were held
(e.g. in Leipzig and Prague in 2013) and are planned (e.g. at LREC
2014).
Part IV - The NLP Interchange Format in Use.
“Use Cases and Applications for NIF” and “Publication of Corpora using
NIF” describe 8 concrete instances where NIF has been successfully used.
One major contribution in is the usage of NIF as the recommended RDF
mapping in the Internationalization Tag Set (ITS) 2.0 W3C standard
and the conversion algorithms from ITS to NIF and back. One outcome
of the discussions in the standardization meetings and telephone
conferences for ITS 2.0 resulted in the conclusion there was no
alternative RDF format or vocabulary other than NIF with the required
features to fulfill the working group charter. Five further uses of NIF
are described for the Ontology of Linguistic Annotations (OLiA), the
RDFaCE tool, the Tiger Corpus Navigator, the OntosFeeder and
visualisations of NIF using the RelFinder tool. These 8 instances
provide an implemented proof-of-concept of the features of NIF.
starts with describing the conversion and hosting of the huge Google
Wikilinks corpus with 40 million annotations for 3 million web sites.
The resulting RDF dump contains 477 million triples in a 5.6 GB
compressed dump file in turtle syntax. describes how NIF can be used to
publish extracted facts from news feeds in the RDFLiveNews tool as
Linked Data.
Part V - Conclusions.
provides lessons learned for NIF, conclusions and an outlook on future
work. Most of the contributions are already summarized above. One
particular aspect worth mentioning is the increasing number of
NIF-formated corpora for Named Entity Recognition (NER) that have come
into existence after the publication of the main NIF paper Integrating
NLP using Linked Data at ISWC 2013. These include the corpora converted
by Steinmetz, Knuth and Sack for the NLP & DBpedia workshop and an
OpenNLP-based CoNLL converter by Brümmer. Furthermore, we are aware of
three LREC 2014 submissions that leverage NIF: NIF4OGGD - NLP
Interchange Format for Open German Governmental Data, N^3 – A Collection
of Datasets for Named Entity Recognition and Disambiguation in the NLP
Interchange Format and Global Intelligent Content: Active Curation of
Language Resources using Linked Data as well as an early implementation
of a GATE-based NER/NEL evaluation framework by Dojchinovski and Kliegr.
Further funding for the maintenance, interlinking and publication of
Linguistic Linked Data as well as support and improvements of NIF is
available via the expiring LOD2 EU project, as well as the CSA EU
project called LIDER, which started in November 2013. Based on the
evidence of successful adoption presented in this thesis, we can expect
a decent to high chance of reaching critical mass of Linked Data
technology as well as the NIF standard in the field of Natural Language
Processing and Language Resources.:CONTENTS
i introduction and background 1
1 introduction 3
1.1 Natural Language Processing . . . . . . . . . . . . . . . 3
1.2 Open licenses, open access and collaboration . . . . . . 5
1.3 Linked Data in Linguistics . . . . . . . . . . . . . . . . . 6
1.4 NLP for and by the Semantic Web – the NLP Inter-
change Format (NIF) . . . . . . . . . . . . . . . . . . . . 8
1.5 Requirements for NLP Integration . . . . . . . . . . . . 10
1.6 Overview and Contributions . . . . . . . . . . . . . . . 11
2 background 15
2.1 The Working Group on Open Data in Linguistics (OWLG) 15
2.1.1 The Open Knowledge Foundation . . . . . . . . 15
2.1.2 Goals of the Open Linguistics Working Group . 16
2.1.3 Open linguistics resources, problems and chal-
lenges . . . . . . . . . . . . . . . . . . . . . . . . 17
2.1.4 Recent activities and on-going developments . . 18
2.2 Technological Background . . . . . . . . . . . . . . . . . 18
2.3 RDF as a data model . . . . . . . . . . . . . . . . . . . . 21
2.4 Performance and scalability . . . . . . . . . . . . . . . . 22
2.5 Conceptual interoperability . . . . . . . . . . . . . . . . 22
ii language resources as linked data 25
3 linked data in linguistics 27
3.1 Lexical Resources . . . . . . . . . . . . . . . . . . . . . . 29
3.2 Linguistic Corpora . . . . . . . . . . . . . . . . . . . . . 30
3.3 Linguistic Knowledgebases . . . . . . . . . . . . . . . . 31
3.4 Towards a Linguistic Linked Open Data Cloud . . . . . 32
3.5 State of the Linguistic Linked Open Data Cloud in 2012 33
3.6 Querying linked resources in the LLOD . . . . . . . . . 36
3.6.1 Enriching metadata repositories with linguistic
features (Glottolog → OLiA) . . . . . . . . . . . 36
3.6.2 Enriching lexical-semantic resources with lin-
guistic information (DBpedia (→ POWLA) →
OLiA) . . . . . . . . . . . . . . . . . . . . . . . . 38
4 DBpedia as a multilingual language resource:
the case of the greek dbpedia edition. 39
4.1 Current state of the internationalization effort . . . . . 40
4.2 Language-specific design of DBpedia resource identifiers 41
4.3 Inter-DBpedia linking . . . . . . . . . . . . . . . . . . . 42
4.4 Outlook on DBpedia Internationalization . . . . . . . . 44
5 leveraging the crowdsourcing of lexical resources
for bootstrapping a linguistic linked data cloud 47
5.1 Related Work . . . . . . . . . . . . . . . . . . . . . . . . 48
5.2 Problem Description . . . . . . . . . . . . . . . . . . . . 50
5.2.1 Processing Wiki Syntax . . . . . . . . . . . . . . 50
5.2.2 Wiktionary . . . . . . . . . . . . . . . . . . . . . . 52
5.2.3 Wiki-scale Data Extraction . . . . . . . . . . . . . 53
5.3 Design and Implementation . . . . . . . . . . . . . . . . 54
5.3.1 Extraction Templates . . . . . . . . . . . . . . . . 56
5.3.2 Algorithm . . . . . . . . . . . . . . . . . . . . . . 56
5.3.3 Language Mapping . . . . . . . . . . . . . . . . . 58
5.3.4 Schema Mediation by Annotation with lemon . 58
5.4 Resulting Data . . . . . . . . . . . . . . . . . . . . . . . . 58
5.5 Lessons Learned . . . . . . . . . . . . . . . . . . . . . . . 60
5.6 Discussion and Future Work . . . . . . . . . . . . . . . 60
5.6.1 Next Steps . . . . . . . . . . . . . . . . . . . . . . 61
5.6.2 Open Research Questions . . . . . . . . . . . . . 61
6 nlp & dbpedia, an upward knowledge acquisition
spiral 63
6.1 Knowledge acquisition and structuring . . . . . . . . . 64
6.2 Representation of knowledge . . . . . . . . . . . . . . . 65
6.3 NLP tasks and applications . . . . . . . . . . . . . . . . 65
6.3.1 Named Entity Recognition . . . . . . . . . . . . 66
6.3.2 Relation extraction . . . . . . . . . . . . . . . . . 67
6.3.3 Question Answering over Linked Data . . . . . 67
6.4 Resources . . . . . . . . . . . . . . . . . . . . . . . . . . . 68
6.4.1 Gold and silver standards . . . . . . . . . . . . . 69
6.5 Summary . . . . . . . . . . . . . . . . . . . . . . . . . . . 70
iii the nlp interchange format (nif) 73
7 nif 2.0 core specification 75
7.1 Conformance checklist . . . . . . . . . . . . . . . . . . . 75
7.2 Creation . . . . . . . . . . . . . . . . . . . . . . . . . . . 76
7.2.1 Definition of Strings . . . . . . . . . . . . . . . . 78
7.2.2 Representation of Document Content with the
nif:Context Class . . . . . . . . . . . . . . . . . . 80
7.3 Extension of NIF . . . . . . . . . . . . . . . . . . . . . . 82
7.3.1 Part of Speech Tagging with OLiA . . . . . . . . 83
7.3.2 Named Entity Recognition with ITS 2.0, DBpe-
dia and NERD . . . . . . . . . . . . . . . . . . . 84
7.3.3 lemon and Wiktionary2RDF . . . . . . . . . . . 86
8 nif 2.0 resources and architecture 89
8.1 NIF Core Ontology . . . . . . . . . . . . . . . . . . . . . 89
8.1.1 Logical Modules . . . . . . . . . . . . . . . . . . 90
8.2 Workflows . . . . . . . . . . . . . . . . . . . . . . . . . . 91
8.2.1 Access via REST Services . . . . . . . . . . . . . 92
8.2.2 NIF Combinator Demo . . . . . . . . . . . . . .
92
8.3 Granularity Profiles . . . . . . . . . . . . . . . . . . . . .
93
8.4 Further URI Schemes for NIF . . . . . . . . . . . . . . .
95
8.4.1 Context-Hash-based URIs . . . . . . . . . . . . .
99
9 evaluation and related work 101
9.1 Questionnaire and Developers Study for NIF 1.0 . . . . 101
9.2 Qualitative Comparison with other Frameworks and
Formats . . . . . . . . . . . . . . . . . . . . . . . . . . . . 102
9.3 URI Stability Evaluation . . . . . . . . . . . . . . . . . . 103
9.4 Related URI Schemes . . . . . . . . . . . . . . . . . . . . 104
iv the nlp interchange format in use 109
10 use cases and applications for nif 111
10.1 Internationalization Tag Set 2.0 . . . . . . . . . . . . . . 111
10.1.1 ITS2NIF and NIF2ITS conversion . . . . . . . . . 112
10.2 OLiA . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 119
10.3 RDFaCE . . . . . . . . . . . . . . . . . . . . . . . . . . . 120
10.4 Tiger Corpus Navigator . . . . . . . . . . . . . . . . . . 121
10.4.1 Tools and Resources . . . . . . . . . . . . . . . . 122
10.4.2 NLP2RDF in 2010 . . . . . . . . . . . . . . . . . . 123
10.4.3 Linguistic Ontologies . . . . . . . . . . . . . . . . 124
10.4.4 Implementation . . . . . . . . . . . . . . . . . . . 125
10.4.5 Evaluation . . . . . . . . . . . . . . . . . . . . . . 126
10.4.6 Related Work and Outlook . . . . . . . . . . . . 129
10.5 OntosFeeder – a Versatile Semantic Context Provider
for Web Content Authoring . . . . . . . . . . . . . . . . 131
10.5.1 Feature Description and User Interface Walk-
through . . . . . . . . . . . . . . . . . . . . . . . 132
10.5.2 Architecture . . . . . . . . . . . . . . . . . . . . . 134
10.5.3 Embedding Metadata . . . . . . . . . . . . . . . 135
10.5.4 Related Work and Summary . . . . . . . . . . . 135
10.6 RelFinder: Revealing Relationships in RDF Knowledge
Bases . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
10.6.1 Implementation . . . . . . . . . . . . . . . . . . . 137
10.6.2 Disambiguation . . . . . . . . . . . . . . . . . . . 138
10.6.3 Searching for Relationships . . . . . . . . . . . . 139
10.6.4 Graph Visualization . . . . . . . . . . . . . . . . 140
10.6.5 Conclusion . . . . . . . . . . . . . . . . . . . . . . 141
11 publication of corpora using nif 143
11.1 Wikilinks Corpus . . . . . . . . . . . . . . . . . . . . . . 143
11.1.1 Description of the corpus . . . . . . . . . . . . . 143
11.1.2 Quantitative Analysis with Google Wikilinks Cor-
pus . . . . . . . . . . . . . . . . . . . . . . . . . . 144
11.2 RDFLiveNews . . . . . . . . . . . . . . . . . . . . . . . . 144
11.2.1 Overview . . . . . . . . . . . . . . . . . . . . . . 145
11.2.2 Mapping to RDF and Publication on the Web of
Data . . . . . . . . . . . . . . . . . . . . . . . . . 146
v conclusions 149
12 lessons learned, conclusions and future work 151
12.1 Lessons Learned for NIF . . . . . . . . . . . . . . . . . . 151
12.2 Conclusions . . . . . . . . . . . . . . . . . . . . . . . . . 151
12.3 Future Work . . . . . . . . . . . . . . . . . . . . . . . . . 15
EXPRESS: Resource-oriented and RESTful Semantic Web services
This thesis investigates an approach that simplifies the development of Semantic Web services (SWS) by removing the need for additional semantic descriptions.The most actively researched approaches to Semantic Web services introduce explicit semantic descriptions of services that are in addition to the existing semantic descriptions of the service domains. This increases their complexity and design overhead. The need for semantically describing the services in such approaches stems from their foundations in service-oriented computing, i.e. the extension of already existing service descriptions. This thesis demonstrates that adopting a resource-oriented approach based on REST will, in contrast to service-oriented approaches, eliminate the need for explicit semantic service descriptions and service vocabularies. This reduces the development efforts while retaining the significant functional capabilities.The approach proposed in this thesis, called EXPRESS (Expressing RESTful Semantic Services), utilises the similarities between REST and the Semantic Web, such as resource realisation, self-describing representations, and uniform interfaces. The semantics of a service is elicited from a resource’s semantic description in the domain ontology and the semantics of the uniform interface, hence eliminating the need for additional semantic descriptions. Moreover, stub-generation is a by-product of the mapping between entities in the domain ontology and resources.EXPRESS was developed to test the feasibility of eliminating explicit service descriptions and service vocabularies or ontologies, to explore the restrictions placed on domain ontologies as a result, to investigate the impact on the semantic quality of the description, and explore the benefits and costs to developers. To achieve this, an online demonstrator that allows users to generate stubs has been developed. In addition, a matchmaking experiment was conducted to show that the descriptions of the services are comparable to OWL-S in terms of their ability to be discovered, while improving the efficiency of discovery. Finally, an expert review was undertaken which provided evidence of EXPRESS’s simplicity and practicality when developing SWS from scratch