10 research outputs found
A Wikipedia Literature Review
This paper was originally designed as a literature review for a doctoral
dissertation focusing on Wikipedia. This exposition gives the structure of
Wikipedia and the latest trends in Wikipedia research
Expanding The NIF Ecosystem - Corpus Conversion, Parsing And Processing Using The NLP Interchange Format 2.0
This work presents a thorough examination and expansion of the NIF ecosystem
Web knowledge bases
Knowledge is key to natural language understanding. References to specific people, places and things in text are crucial to resolving ambiguity and extracting meaning. Knowledge Bases (KBs) codify this information for automated systems — enabling applications such as entity-based search and question answering. This thesis explores the idea that sites on the web may act as a KB, even if that is not their primary intent. Dedicated kbs like Wikipedia are a rich source of entity information, but are built and maintained at an ongoing cost in human effort. As a result, they are generally limited in terms of the breadth and depth of knowledge they index about entities. Web knowledge bases offer a distributed solution to the problem of aggregating entity knowledge. Social networks aggregate content about people, news sites describe events with tags for organizations and locations, and a diverse assortment of web directories aggregate statistics and summaries for long-tail entities notable within niche movie, musical and sporting domains. We aim to develop the potential of these resources for both web-centric entity Information Extraction (IE) and structured KB population. We first investigate the problem of Named Entity Linking (NEL), where systems must resolve ambiguous mentions of entities in text to their corresponding node in a structured KB. We demonstrate that entity disambiguation models derived from inbound web links to Wikipedia are able to complement and in some cases completely replace the role of resources typically derived from the KB. Building on this work, we observe that any page on the web which reliably disambiguates inbound web links may act as an aggregation point for entity knowledge. To uncover these resources, we formalize the task of Web Knowledge Base Discovery (KBD) and develop a system to automatically infer the existence of KB-like endpoints on the web. While extending our framework to multiple KBs increases the breadth of available entity knowledge, we must still consolidate references to the same entity across different web KBs. We investigate this task of Cross-KB Coreference Resolution (KB-Coref) and develop models for efficiently clustering coreferent endpoints across web-scale document collections. Finally, assessing the gap between unstructured web knowledge resources and those of a typical KB, we develop a neural machine translation approach which transforms entity knowledge between unstructured textual mentions and traditional KB structures. The web has great potential as a source of entity knowledge. In this thesis we aim to first discover, distill and finally transform this knowledge into forms which will ultimately be useful in downstream language understanding tasks
Semantic Analysis of Wikipedia's Linked Data Graph for Entity Detection and Topic Identification Applications
Semantic Web and Linked Data community is now the reality of the future of the Web. The standards and technologies defined in this field have opened a strong pathway towards a new era of knowledge management and representation for the computing world. The data structures and the semantic formats introduced by the Semantic Web standards offer a platform for all the data and knowledge providers in the world to present their information in a free, publicly available, semantically tagged, inter-linked, and machine-readable structure. As a result, the adaptation of the Semantic Web standards by data providers creates numerous opportunities for development of new applications which were not possible or, at best, hardly achievable using the current state of Web which is mostly consisted of unstructured or semi-structured data with minimal semantic metadata attached tailored mainly for human-readability.
This dissertation tries to introduce a framework for effective analysis of the Semantic Web data towards the development of solutions for a series of related applications. In order to achieve such framework, Wikipedia is chosen as the main knowledge resource largely due to the fact that it is the main and central dataset in Linked Data community. In this work, Wikipedia and its Semantic Web version DBpedia are used to create a semantic graph which constitutes the knowledgebase and the back-end foundation of the framework. The semantic graph introduced in this research consists of two main concepts: entities and topics. The entities act as the knowledge items while topics create the class hierarchy of the knowledge items. Therefore, by assigning entities to various topics, the semantic graph presents all the knowledge items in a categorized hierarchy ready for further processing.
Furthermore, this dissertation introduces various analysis algorithms over entity and topic graphs which can be used in a variety of applications, especially in natural language understanding and knowledge management fields. After explaining the details of the analysis algorithms, a number of possible applications are presented and potential solutions to these applications are provided. The main themes of these applications are entity detection, topic identification, and context acquisition. To demonstrate the efficiency of the framework algorithms, some of the applications are developed and comprehensively studied by providing detailed experimental results which are compared with appropriate benchmarks. These results show how the framework can be used in different configurations and how different parameters affect the performance of the algorithms
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 BrĂĽmmer. 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
Harnessing sense-level information for semantically augmented knowledge extraction
Nowadays, building accurate computational models for the semantics of language lies at the very core of Natural Language Processing and Artificial Intelligence. A first and foremost step in this respect consists in moving from word-based to sense-based approaches, in which operating explicitly at the level of word senses enables a model to produce more accurate and unambiguous results. At the same time, word senses create a bridge towards structured lexico-semantic resources, where the vast amount of available machine-readable information can help overcome the shortage of annotated data in many languages and domains of knowledge.
This latter phenomenon, known as the knowledge acquisition bottlneck, is a crucial problem that hampers the development of large-scale, data-driven approaches for many Natural Language Processing tasks, especially when lexical semantics is directly involved. One of these tasks is Information Extraction, where an effective model has to cope with data sparsity, as well as with lexical ambiguity that can arise at the level of both arguments and relational phrases. Even in more recent Information Extraction approaches where semantics is implicitly modeled, these issues have not yet been addressed in their entirety. On the other hand, however, having access to explicit sense-level information is a very demanding task on its own, which can rarely be performed with high accuracy on a large scale. With this in mind, in ths thesis we will tackle a two-fold objective: our first focus will be on studying fully automatic approaches to obtain high-quality sense-level information from textual corpora; then, we will investigate in depth where and how such sense-level information has the potential to enhance the extraction of knowledge from open text.
In the first part of this work, we will explore three different disambiguation scenar- ios (semi-structured text, parallel text, and definitional text) and devise automatic disambiguation strategies that are not only capable of scaling to different corpus sizes and different languages, but that actually take advantage of a multilingual and/or heterogeneous setting to improve and refine their performance. As a result, we will obtain three sense-annotated resources that, when tested experimentally with a baseline system in a series of downstream semantic tasks (i.e. Word Sense Disam- biguation, Entity Linking, Semantic Similarity), show very competitive performances on standard benchmarks against both manual and semi-automatic competitors.
In the second part we will instead focus on Information Extraction, with an emphasis on Open Information Extraction (OIE), where issues like sparsity and lexical ambiguity are especially critical, and study how to exploit at best sense-level information within the extraction process. We will start by showing that enforcing a deeper semantic analysis in a definitional setting enables a full-fledged extraction pipeline to compete with state-of-the-art approaches based on much larger (but noisier) data. We will then demonstrate how working at the sense level at the end of an extraction pipeline is also beneficial: indeed, by leveraging sense-based techniques, very heterogeneous OIE-derived data can be aligned semantically, and unified with respect to a common sense inventory. Finally, we will briefly shift the focus to the more constrained setting of hypernym discovery, and study a sense-aware supervised framework for the task that is robust and effective, even when trained on heterogeneous OIE-derived hypernymic knowledge
Learning of a multilingual bitaxonomy of Wikipedia and its application to semantic predicates
The ability to extract hypernymy information on a large scale is becoming increasingly important in natural language processing, an area of the artificial intelligence which deals with the processing and understanding of natural language. While initial studies extracted this type of information from textual corpora by means of lexico-syntactic patterns, over time researchers moved to alternative, more structured sources of knowledge, such as Wikipedia. After the first attempts to extract is-a information fromWikipedia categories, a full line of research gave birth to numerous knowledge bases containing information which, however, is either incomplete or irremediably bound to English.
To this end we put forward MultiWiBi, the first approach to the construction of a multilingual bitaxonomy which exploits the inner connection between Wikipedia pages and Wikipedia categories to induce a wide-coverage and fine-grained integrated taxonomy. A series of experiments show state-of-the-art results against all the available taxonomic resources available in the literature, also with respect to two novel measures of comparison.
Another dimension where existing resources usually fall short is their degree of multilingualism. While knowledge is typically language agnostic, currently resources are able to extract relevant information only in languages providing highquality tools. In contrast, MultiWiBi does not leave any language behind: we show how to taxonomize Wikipedia in an arbitrary language and in a way that is fully independent of additional resources. At the core of our approach lies, in fact, the idea that the English version of Wikipedia can be linguistically exploited as a pivot to project the taxonomic information extracted from English to any other Wikipedia language in order to have a bitaxonomy in a second, arbitrary language; as a result, not only concepts which have an English equivalent are covered, but also those concepts which are not lexicalized in the source language.
We also present the impact of having the taxonomized encyclopedic knowledge offered by MultiWiBi embedded into a semantic model of predicates (SPred) which crucially leverages Wikipedia to generalize collections of related noun phrases to infer a probability distribution over expected semantic classes. We applied SPred to a word sense disambiguation task and show that, when MultiWiBi is plugged in to replace an internal component, SPred’s generalization power increases as well as its precision and recall.
Finally, we also published MultiWiBi as linked data, a paradigm which fosters interoperability and interconnection among resources and tools through the publication of data on the Web, and developed a public interface which lets the users navigate through MultiWiBi’s taxonomic structure in a graphical, captivating manner
Entity-Oriented Search
This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms
Rule Mining for Semantifying Wikilinks
ABSTRACT Wikipedia-centric Knowledge Bases (KBs) such as YAGO and DBpedia store the hyperlinks between articles in Wikipedia using wikilink relations. While wikilinks are signals of semantic connection between entities, the meaning of such connection is most of the times unknown to KBs, e.g., for 89% of wikilinks in DBpedia no other relation between the entities is known. The task of discovering the exact relations that hold between the endpoints of a wikilink is called wikilink semantification. In this paper, we apply rule mining techniques on the already semantified wikilinks to propose relations for the unsemantified wikilinks in a subset of DBpedia. By mining highly supported and confident logical rules from KBs, we can semantify wikilinks with very high precision