38 research outputs found
Benchmarking the continuous behavior of stream processing techniques on the problem of enumerating weakly connected components
The problem of enumerating subgraphs satisfying some properties (e.g., patterns and mo- tifs) corresponds to listing such subgraphs. In general, because of the large/huge graphs re- lating objects in different domains (biology, mobile networks, social networks, genetics, etc.), waiting until achieving the whole enumeration of subgraphs satisfying some properties can be too expensive and unmanageable for some families of problems. Thus, the possibility of incrementally enumerating these subgraphs (i.e., a subgraph is returned as soon as they are identified) rises as a promising approach for some families of problems (e.g., network analysis and motif identification). In effect, following a search approach over the listed subgraphs allows stopping the enumeration when it is convenient. The implementation of incremental enumer- ation solutions suits well to stream processing paradigms and frameworks. In this work, as a proof of concept, we conduct a comparative study of the continuous behavior of two stream processing-based solutions for the problem of incrementally enumerating weakly connected components of (bounded) undirected graphs. One of these solutions is implemented using the Go programming language following the Dynamic Pipeline Paradigm, and the other solution is implemented on top of the DataSet API provided by the Apache Flink Framework. Finally, we analyze and report experiments against large graphs having up to 2, 746 connected compo- nents. Obtained results satisfy our expectations. To assess the incremental delivery of results, we measure the the diefficiency metrics, i.e., the continuous efficiency of the implementation of the algorithm for generating incremental results.El problema de l'enumeració dels subgrafs que satisfan determinades propietats (per exem- ple patrons i disenys) correspon a la mostra d'aquests subgrafs. En general, a causa dels grafs grans/enormes que relacionen objectes en diferents dominis (biologia, xarxes mòbils, xarxes socials, genètica, etc.), esperar fins a aconseguir l'enumeració sencera dels subgrafs que satisfacin certes propietats pot ser massa costós i immanejable per a algunes famílies de problemes. Per tant, la possibilitat d'enumerar incrementalment aquests subgrafs (es dir, enumerar els com- ponents a mesura que es calculen o s'identifiquen) sorgeix com una alternativa prometedora per a algunes famílies de problemes (per exemple, en l'an'alisi de xarxes i en l'identificació de patrons). En efecte, seguir un enfocament de cerca sobre els subgrafs enumerats permet aturar l'enumeració quan sigui convenient. La implementacióde solucions d'enumeració incremental s'adapta bé als frameworks i paradigmes de processament de fluxos de dades. En aquest tre- ball, com a prova de concepte, realitzem un estudi comparatiu del comportament continu de dues solucions basades en el processament de fluxos de dades per al problema de l'enumeració incremental de components d'ebilment connexes de grafs (acotats) no dirigits. Una d'aques- tes solucions s'implementa utilitzant el llenguatge de programació Go seguint el Paradigma de Dynamic Pipeline, i l'altra solució s'implementa sobre l'API de DataSet proporcionada per l'Apache Flink Framework. Finalment, analitzem i informem sobre experiments realitzats amb grafs grans que contenen fins a 2746 components connexes. Els resultats obtinguts satisfan les nostres expectatives. Per avaluar el lliurament incremental de resultats, mesurem les mètriques de dieficiència, es a dir, l'eficiència continua de la implementació de l'algorisme per generar resultats incrementals
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
Semantic Systems. In the Era of Knowledge Graphs
This open access book constitutes the refereed proceedings of the 16th International Conference on Semantic Systems, SEMANTiCS 2020, held in Amsterdam, The Netherlands, in September 2020. The conference was held virtually due to the COVID-19 pandemic
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
Measuring the performance of continuous query processing approaches with dief@t and dief@k
In this work, we present two experimental metrics named dief@t and dief@k which are able to capture and quantify the behavior of any system that produces results incrementally. We demonstrate the effectiveness of dief@t and dief@k on a generic SPARQL query engine able to produce results incrementally. Attendees will observe how both metrics are able to capture complementary information about the continuous behavior of the studied query engine. Moreover, valuable insights about the engine configurations that allow for continuously producing more answers over time will be observed. The demo is available at http://km.aifb.kit.edu/services/dief-app/
Corporate social responsibility of hotels in Bali – Indonesia: practices, drivers and barriers
Guided by the theory of planned behaviour (Ajzen 1991), this thesis investigates CSR Practices (CSRP) and factors that explain CSRP of hotels in Bali – Indonesia. A mixed methods approach is used. The findings of this thesis indicate that CSRP of most local hotels are less advanced. Hotel managers are influenced more by external forces than internal factors in making CSR decisions. The owners and certification organisations play a significant role in shaping CSRP