Knowledge Organization Systems (KOS) are typically used as background knowledge for document inde... more Knowledge Organization Systems (KOS) are typically used as background knowledge for document indexing in information retrieval. They have to be maintained and adapted constantly to reflect changes in the domain and the terminology. In this thesis, approaches are provided that support the maintenance of hierarchical knowledge organization systems, like thesauri, classifications, or taxonomies, by making information about the usage of KOS concepts available to the maintainer. The central contribution is the ICE-Map Visualization, a treemap-based visualization on top of a generalized statistical framework that is able to visualize almost arbitrary usage information. The proper selection of an existing KOS for available documents and the evaluation of a KOS for different indexing techniques by means of the ICE-Map Visualization is demonstrated. For the creation of a new KOS, an approach based on crowdsourcing is presented that uses feedback from Amazon Mechanical Turk to relate terms hierarchically. The extension of an existing KOS with new terms derived from the documents to be indexed is performed with a machine-learning approach that relates the terms to existing concepts in the hierarchy. The features are derived from text snippets in the result list of a web search engine. For the splitting of overpopulated concepts into new subconcepts, an interactive clustering approach is presented that is able to propose names for the new subconcepts. The implementation of a framework is described that integrates all approaches of this thesis and contains the reference implementation of the ICE-Map Visual- ization. It is extendable and supports the implementation of evaluation methods that build on other evaluations. Additionally, it supports the visualization of the results and the implementation of new visualizations. An important building block for practical applications is the simple linguistic indexer that is presented as minor contribution. It is knowledge-poor and works without any training. This thesis applies computer science approaches in the domain of information science. The introduction describes the foundations in information science; in the conclusion, the focus is set on the relevance for practical applications, especially regarding the handling of different qualities of KOSs due to automatic and semi-automatic maintenance.
For data practitioners embracing the world of RDF and
Linked Data, the openness and flexi... more For data practitioners embracing the world of RDF and Linked Data, the openness and flexibility is a mixed blessing. For them, data validation according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in the DCMI RDF Application Profiles Task Group and in cooperation with the W3C Data Shapes Working Group, we published by today 81 types of constraints that are required by various stakeholders for data applications. These constraint types form the basis to investigate the role that reasoning and different semantics play in practical data validation, why reasoning is beneficial for RDF validation, and how to overcome the major shortcomings when validating RDF data by performing reasoning prior to validation. For each constraint type, we examine (1) if reasoning may im- prove data quality, (2) how efficient in terms of runtime val- idation is performed with and without reasoning, and (3) if validation results depend on underlying semantics which differs between reasoning and validation. Using these findings, we determine for the most common constraint languages which constraint types they enable to express and give di- rections for the further development of constraint languages.
The ICE-Map Visualization was developed to graphically an-
alyze the distribution of indexing res... more The ICE-Map Visualization was developed to graphically an- alyze the distribution of indexing results within a given Knowledge Organization System (KOS) hierarchy and allows the user to explore the document sets and the KOSs at the same time. In this paper, we demonstrate the use of the ICE-Map Visualization in combination with a simple automatic indexer to visualize the semantic overlap between a KOS and a set of documents.
Das Semantic Web – bzw. Linked Data – hat das Potenzial, die Verfugbarkeit von Daten und Wissen, ... more Das Semantic Web – bzw. Linked Data – hat das Potenzial, die Verfugbarkeit von Daten und Wissen, sowie den Zugriff darauf zu revolutionieren. Einen grosen Beitrag dazu konnen Wissensorganisationssysteme wie Thesauri leisten, die die Daten inhaltlich erschliesen und strukturieren. Leider sind immer noch viele dieser Systeme lediglich in Buchform oder in speziellen Anwendungen verfugbar. Wie also lassen sie sich fur das Semantic Web nutzen? Das Simple Knowledge Organization System (SKOS) bietet eine Moglichkeit, die Wissensorganisationssysteme in eine Form zu “ubersetzen”, die im Web zitiert und mit anderen Resourcen verknupft werden kann.
For research institutes, data libraries, and data archives, RDF data validation according to pred... more For research institutes, data libraries, and data archives, RDF data validation according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in the DCMI RDF Application Profiles Task Group and in cooperation with the W3C Data Shapes Working Group, we identified and published by today 81 types of constraints that are required by various stakeholders for data applications. In this paper, in collaboration with several domain experts we formulate 115 constraints on three different vocabularies (DDI-RDF, QB, and SKOS) and classify them according to (1) the severity of an occurring violation and (2) the complexity of the constraint expression in common constraint languages. We evaluate the data quality of 15,694 data sets (4.26 billion triples) of research data for the social, behavioral, and economic sciences obtained from 33 SPARQL endpoints. Based on the results, we formulate several findings to direct the further development of constraint languages.
International Conference on Dublin Core and Metadata Applications, Oct 8, 2014
Description Set Profiles (DSP) are used to formulate constraints on valid data within a Dublin Co... more Description Set Profiles (DSP) are used to formulate constraints on valid data within a Dublin Core Application Profile. For RDF, SPARQL is generally seen as the method of choice to validate data according to certain constraints, although it is not ideal for their formulation. In contrast, DSPs are comparatively easy to understand, but lack an implementation to validate RDF data. In this paper, we use SPIN as basic validation framework and present a general approach how domain specific constraint languages like DSP can be executed on RDF data using SPARQL as an intermediate language.
From 2012 to 2015 together with other Linked Data community members and experts from the social, ... more From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioral, and economic sciences (SBE), we developed diverse vocabularies to represent SBE metadata and tabular data in RDF. The DDI-RDF Discovery Vocabulary (DDI-RDF) is designed to support the dissemination, management, and reuse of unit-record data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (QB) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data and its metadata. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format, i.e., tabular data. The data could either be represented in records with character-separated values (CSV) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria-specified in terms of RDF constraints. In this paper, we evaluate the data quality of 15,694 data sets (4.26 billion triples) of research data for the social, behavioral, and economic sciences obtained from 33 SPARQL endpoints. We checked 115 constraints on three different and representative SBE vocabularies (DDI-RDF, QB, and SKOS) by means of the RDF Validator, a validation environment which is available at http://purl.org/net/rdfval-demo.
Ontology matching consists of finding correspondences between semantically related entities of tw... more Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, consensus. OAEI 2012 offered 7 tracks with 9 test cases followed by 21 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2012 campaign. This paper improves on the "Preliminary results" initially published in the on-site proceedings of the ISWC workshop on Ontology Matching (OM-2012). The only official results of the campaign, however, are on the OAEI web site.
HAL (Le Centre pour la Communication Scientifique Directe), Oct 18, 2016
Ontology matching consists of finding correspondences between semantically related entities of tw... more Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing The official results of the campaign are on the OAEI web site. ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, or consensus. OAEI 2016 offered 9 tracks with 22 test cases, and was attended by 21 participants. This paper is an overall presentation of the OAEI 2016 campaign.
HAL (Le Centre pour la Communication Scientifique Directe), Oct 20, 2014
Ontology matching consists of finding correspondences between semantically related entities of tw... more Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2014 offered 7 tracks with 9 test cases followed by 14 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2014 campaign. This paper improves on the "Preliminary results" initially published in the on-site proceedings of the ISWC workshop on Ontology Matching (OM-2014). The only official results of the campaign, however, are on the OAEI web site.
To ensure high quality of and trust in both metadata and data, their representation in RDF must s... more To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria-specified in terms of RDF constraints. From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioral, and economic sciences (SBE), we developed diverse vocabularies to represent SBE metadata and rectangular data in RDF. The DDI-RDF Discovery Vocabulary (DDI-RDF) is designed to support the dissemination, management, and reuse of unit-record data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (QB) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data and its metadata. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format, i.e., tabular data. The data could either be represented in records with character-separated values (CSV) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. In this paper, we describe RDF constraints to validate metadata on unitrecord data (DDI-RDF), aggregated data (QB), thesauri (SKOS), and statistical classifications (XKOS) and to validate tabular data (PHDD)all of them represented in RDF. We classified these constraints according to the severity of occurring constraint violations. This technical report is updated continuously as modifying, adding, and deleting constraints remains ongoing work.
To ensure high quality of and trust in both metadata and data, their representation in RDF must s... more To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria-specified in terms of RDF constraints. From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioral, and economic sciences (SBE), we developed diverse vocabularies to represent SBE metadata and rectangular data in RDF. The DDI-RDF Discovery Vocabulary (DDI-RDF) is designed to support the dissemination, management, and reuse of unit-record data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (QB) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data and its metadata. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format, i.e., tabular data. The data could either be represented in records with character-separated values (CSV) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. In this paper, we describe RDF constraints to validate metadata on unitrecord data (DDI-RDF), aggregated data (QB), thesauri (SKOS), and statistical classifications (XKOS) and to validate tabular data (PHDD)all of them represented in RDF. We classified these constraints according to the severity of occurring constraint violations. This technical report is updated continuously as modifying, adding, and deleting constraints remains ongoing work.
International Conference on Dublin Core and Metadata Applications, Sep 2, 2013
Provenance tracking for Linked Data requires the identification of Linked Data resources. Annotat... more Provenance tracking for Linked Data requires the identification of Linked Data resources. Annotating Linked Data on the level of single statements requires the identification of these statements. The concept of a Provenance Context is introduced as the basis for a consistent data model for Linked Data that incorporates current best-practices and creates identity for every published Linked Dataset. A comparison of this model with the Dublin Core Abstract Model is provided to gain further understanding, how Linked Data affects the traditional view on metadata and to what extent our approach could help to mediate. Finally, a linking mechanism based on RDF reification is developed to annotate single statements within a Provenance Context.
The number of scientific publications nowadays is rapidly increasing, causing information overloa... more The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different ...
Nowadays there is a growing trend in many scientific disciplines to support researchers by provid... more Nowadays there is a growing trend in many scientific disciplines to support researchers by providing enhanced information access through linking of publications and underlying datasets, so as to support research with infrastructure to enhance reproducibility and reusability of research results. In this research note, we present an overview of an ongoing research project, named VADIS (VAriable Detection, Interlinking and Summarization), that aims at developing technology and infrastructure for enhanced information access in the Social Sciences via search and summarization of publications on the basis of automatic identification and indexing of survey variables in text. We provide an overview of the overarching vision underlying our project, its main components, and related challenges, as well as a thorough discussion of how these are meant to address the limitations of current information access systems for publications in the Social Sciences. We show how this goal can be concretely implemented in an end-user system by presenting a search prototype, which is based on user requirements collected from qualitative interviews with empirical Social Science researchers.
In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on ... more In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual sentences from scholarly documents in full text. Two teams made a total of 9 submissions to the shared task leaderboard. While none of the teams improve on the baseline systems, we still draw insights from their submissions. Furthermore, we provide a detailed evaluation. Data and baselines for our shared task are freely available at https://github. com/vadis-project/sv-ident.
Zenodo (CERN European Organization for Nuclear Research), May 21, 2022
Conferences are considered as one of the core media of scholarly communication in many research d... more Conferences are considered as one of the core media of scholarly communication in many research disciplines. The articles presented via such opportunities are usually published as proceedings. Generally, the titles of proceedings are composed of a limited subset of English terms that uniquely describe an event. Most proceedings title are available in digital form these days-be it as part of the publishing or when referenced e.g. in citations. Therefore, the titles of proceedings are a potential source for automatically extracting metadata about events. This paper presents a semantic parser to extract conference metadata from English titles of proceedings of scientific events. The parser we present has been designed and tested based on over 43,640 proceedings titles extracted from four complementary, highquality sources. To improve the flexibility and simplicity of the parser, a combined parser / dictionary based approach is applied.
Journal of the Association for Information Science and Technology
Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) prog... more Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate‐level and undergraduate‐level DS education? Answers to these questions will not only distinguish an iField approach to ...
Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries
The number of scientific publications nowadays is rapidly increasing, causing information overloa... more The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Consequently, recent work on applying text mining technologies for scholarly publications has investigated the application of automatic text summarization technologies, including extreme summarization, for this domain. However, previous work has concentrated only on monolingual settings, primarily in English. In this paper, we fill this research gap and present an abstractive cross-lingual summarization dataset for four different languages in the scholarly domain, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage 'summarize and translate' approach and a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero-and few-shot scenarios. CCS Concepts • Computing methodologies → Natural language processing; Natural language generation; Language resources.
Summary: - RDF is a simple, graph-based data model for metadata on the web - RDF has an XML synta... more Summary: - RDF is a simple, graph-based data model for metadata on the web - RDF has an XML syntax for: - Exchanging RDF Models - Embedding RDF Models into web pages - Advantages over XML - Data model is agnostic to syntactic variations - Information from different models and locations can easily be linked - Some important operations are trivial (i.e. merging two models) - RDF Schema defines special resources and predicates for defining vocabularies - Vokabular: Class, SubClassOf, domain, range - Implicit information can be derived using simple derivation rules - There is no clear separation between model and schema, schema elements can be part of an RDF mode
Knowledge Organization Systems (KOS) are typically used as background knowledge for document inde... more Knowledge Organization Systems (KOS) are typically used as background knowledge for document indexing in information retrieval. They have to be maintained and adapted constantly to reflect changes in the domain and the terminology. In this thesis, approaches are provided that support the maintenance of hierarchical knowledge organization systems, like thesauri, classifications, or taxonomies, by making information about the usage of KOS concepts available to the maintainer. The central contribution is the ICE-Map Visualization, a treemap-based visualization on top of a generalized statistical framework that is able to visualize almost arbitrary usage information. The proper selection of an existing KOS for available documents and the evaluation of a KOS for different indexing techniques by means of the ICE-Map Visualization is demonstrated. For the creation of a new KOS, an approach based on crowdsourcing is presented that uses feedback from Amazon Mechanical Turk to relate terms hierarchically. The extension of an existing KOS with new terms derived from the documents to be indexed is performed with a machine-learning approach that relates the terms to existing concepts in the hierarchy. The features are derived from text snippets in the result list of a web search engine. For the splitting of overpopulated concepts into new subconcepts, an interactive clustering approach is presented that is able to propose names for the new subconcepts. The implementation of a framework is described that integrates all approaches of this thesis and contains the reference implementation of the ICE-Map Visual- ization. It is extendable and supports the implementation of evaluation methods that build on other evaluations. Additionally, it supports the visualization of the results and the implementation of new visualizations. An important building block for practical applications is the simple linguistic indexer that is presented as minor contribution. It is knowledge-poor and works without any training. This thesis applies computer science approaches in the domain of information science. The introduction describes the foundations in information science; in the conclusion, the focus is set on the relevance for practical applications, especially regarding the handling of different qualities of KOSs due to automatic and semi-automatic maintenance.
For data practitioners embracing the world of RDF and
Linked Data, the openness and flexi... more For data practitioners embracing the world of RDF and Linked Data, the openness and flexibility is a mixed blessing. For them, data validation according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in the DCMI RDF Application Profiles Task Group and in cooperation with the W3C Data Shapes Working Group, we published by today 81 types of constraints that are required by various stakeholders for data applications. These constraint types form the basis to investigate the role that reasoning and different semantics play in practical data validation, why reasoning is beneficial for RDF validation, and how to overcome the major shortcomings when validating RDF data by performing reasoning prior to validation. For each constraint type, we examine (1) if reasoning may im- prove data quality, (2) how efficient in terms of runtime val- idation is performed with and without reasoning, and (3) if validation results depend on underlying semantics which differs between reasoning and validation. Using these findings, we determine for the most common constraint languages which constraint types they enable to express and give di- rections for the further development of constraint languages.
The ICE-Map Visualization was developed to graphically an-
alyze the distribution of indexing res... more The ICE-Map Visualization was developed to graphically an- alyze the distribution of indexing results within a given Knowledge Organization System (KOS) hierarchy and allows the user to explore the document sets and the KOSs at the same time. In this paper, we demonstrate the use of the ICE-Map Visualization in combination with a simple automatic indexer to visualize the semantic overlap between a KOS and a set of documents.
Das Semantic Web – bzw. Linked Data – hat das Potenzial, die Verfugbarkeit von Daten und Wissen, ... more Das Semantic Web – bzw. Linked Data – hat das Potenzial, die Verfugbarkeit von Daten und Wissen, sowie den Zugriff darauf zu revolutionieren. Einen grosen Beitrag dazu konnen Wissensorganisationssysteme wie Thesauri leisten, die die Daten inhaltlich erschliesen und strukturieren. Leider sind immer noch viele dieser Systeme lediglich in Buchform oder in speziellen Anwendungen verfugbar. Wie also lassen sie sich fur das Semantic Web nutzen? Das Simple Knowledge Organization System (SKOS) bietet eine Moglichkeit, die Wissensorganisationssysteme in eine Form zu “ubersetzen”, die im Web zitiert und mit anderen Resourcen verknupft werden kann.
For research institutes, data libraries, and data archives, RDF data validation according to pred... more For research institutes, data libraries, and data archives, RDF data validation according to predefined constraints is a much sought-after feature, particularly as this is taken for granted in the XML world. Based on our work in the DCMI RDF Application Profiles Task Group and in cooperation with the W3C Data Shapes Working Group, we identified and published by today 81 types of constraints that are required by various stakeholders for data applications. In this paper, in collaboration with several domain experts we formulate 115 constraints on three different vocabularies (DDI-RDF, QB, and SKOS) and classify them according to (1) the severity of an occurring violation and (2) the complexity of the constraint expression in common constraint languages. We evaluate the data quality of 15,694 data sets (4.26 billion triples) of research data for the social, behavioral, and economic sciences obtained from 33 SPARQL endpoints. Based on the results, we formulate several findings to direct the further development of constraint languages.
International Conference on Dublin Core and Metadata Applications, Oct 8, 2014
Description Set Profiles (DSP) are used to formulate constraints on valid data within a Dublin Co... more Description Set Profiles (DSP) are used to formulate constraints on valid data within a Dublin Core Application Profile. For RDF, SPARQL is generally seen as the method of choice to validate data according to certain constraints, although it is not ideal for their formulation. In contrast, DSPs are comparatively easy to understand, but lack an implementation to validate RDF data. In this paper, we use SPIN as basic validation framework and present a general approach how domain specific constraint languages like DSP can be executed on RDF data using SPARQL as an intermediate language.
From 2012 to 2015 together with other Linked Data community members and experts from the social, ... more From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioral, and economic sciences (SBE), we developed diverse vocabularies to represent SBE metadata and tabular data in RDF. The DDI-RDF Discovery Vocabulary (DDI-RDF) is designed to support the dissemination, management, and reuse of unit-record data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (QB) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data and its metadata. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format, i.e., tabular data. The data could either be represented in records with character-separated values (CSV) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria-specified in terms of RDF constraints. In this paper, we evaluate the data quality of 15,694 data sets (4.26 billion triples) of research data for the social, behavioral, and economic sciences obtained from 33 SPARQL endpoints. We checked 115 constraints on three different and representative SBE vocabularies (DDI-RDF, QB, and SKOS) by means of the RDF Validator, a validation environment which is available at http://purl.org/net/rdfval-demo.
Ontology matching consists of finding correspondences between semantically related entities of tw... more Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, consensus. OAEI 2012 offered 7 tracks with 9 test cases followed by 21 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2012 campaign. This paper improves on the "Preliminary results" initially published in the on-site proceedings of the ISWC workshop on Ontology Matching (OM-2012). The only official results of the campaign, however, are on the OAEI web site.
HAL (Le Centre pour la Communication Scientifique Directe), Oct 18, 2016
Ontology matching consists of finding correspondences between semantically related entities of tw... more Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing The official results of the campaign are on the OAEI web site. ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation, or consensus. OAEI 2016 offered 9 tracks with 22 test cases, and was attended by 21 participants. This paper is an overall presentation of the OAEI 2016 campaign.
HAL (Le Centre pour la Communication Scientifique Directe), Oct 20, 2014
Ontology matching consists of finding correspondences between semantically related entities of tw... more Ontology matching consists of finding correspondences between semantically related entities of two ontologies. OAEI campaigns aim at comparing ontology matching systems on precisely defined test cases. These test cases can use ontologies of different nature (from simple thesauri to expressive OWL ontologies) and use different modalities, e.g., blind evaluation, open evaluation and consensus. OAEI 2014 offered 7 tracks with 9 test cases followed by 14 participants. Since 2010, the campaign has been using a new evaluation modality which provides more automation to the evaluation. This paper is an overall presentation of the OAEI 2014 campaign. This paper improves on the "Preliminary results" initially published in the on-site proceedings of the ISWC workshop on Ontology Matching (OM-2014). The only official results of the campaign, however, are on the OAEI web site.
To ensure high quality of and trust in both metadata and data, their representation in RDF must s... more To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria-specified in terms of RDF constraints. From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioral, and economic sciences (SBE), we developed diverse vocabularies to represent SBE metadata and rectangular data in RDF. The DDI-RDF Discovery Vocabulary (DDI-RDF) is designed to support the dissemination, management, and reuse of unit-record data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (QB) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data and its metadata. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format, i.e., tabular data. The data could either be represented in records with character-separated values (CSV) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. In this paper, we describe RDF constraints to validate metadata on unitrecord data (DDI-RDF), aggregated data (QB), thesauri (SKOS), and statistical classifications (XKOS) and to validate tabular data (PHDD)all of them represented in RDF. We classified these constraints according to the severity of occurring constraint violations. This technical report is updated continuously as modifying, adding, and deleting constraints remains ongoing work.
To ensure high quality of and trust in both metadata and data, their representation in RDF must s... more To ensure high quality of and trust in both metadata and data, their representation in RDF must satisfy certain criteria-specified in terms of RDF constraints. From 2012 to 2015 together with other Linked Data community members and experts from the social, behavioral, and economic sciences (SBE), we developed diverse vocabularies to represent SBE metadata and rectangular data in RDF. The DDI-RDF Discovery Vocabulary (DDI-RDF) is designed to support the dissemination, management, and reuse of unit-record data, i.e., data about individuals, households, and businesses, collected in form of responses to studies and archived for research purposes. The RDF Data Cube Vocabulary (QB) is a W3C recommendation for expressing data cubes, i.e. multi-dimensional aggregate data and its metadata. Physical Data Description (PHDD) is a vocabulary to model data in rectangular format, i.e., tabular data. The data could either be represented in records with character-separated values (CSV) or fixed length. The Simple Knowledge Organization System (SKOS) is a vocabulary to build knowledge organization systems such as thesauri, classification schemes, and taxonomies. XKOS is a SKOS extension to describe formal statistical classifications. In this paper, we describe RDF constraints to validate metadata on unitrecord data (DDI-RDF), aggregated data (QB), thesauri (SKOS), and statistical classifications (XKOS) and to validate tabular data (PHDD)all of them represented in RDF. We classified these constraints according to the severity of occurring constraint violations. This technical report is updated continuously as modifying, adding, and deleting constraints remains ongoing work.
International Conference on Dublin Core and Metadata Applications, Sep 2, 2013
Provenance tracking for Linked Data requires the identification of Linked Data resources. Annotat... more Provenance tracking for Linked Data requires the identification of Linked Data resources. Annotating Linked Data on the level of single statements requires the identification of these statements. The concept of a Provenance Context is introduced as the basis for a consistent data model for Linked Data that incorporates current best-practices and creates identity for every published Linked Dataset. A comparison of this model with the Dublin Core Abstract Model is provided to gain further understanding, how Linked Data affects the traditional view on metadata and to what extent our approach could help to mediate. Finally, a linking mechanism based on RDF reification is developed to annotate single statements within a Provenance Context.
The number of scientific publications nowadays is rapidly increasing, causing information overloa... more The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Recent work has tried to address this problem by developing methods for automated summarization in the scholarly domain, but concentrated so far only on monolingual settings, primarily English. In this paper, we consequently explore how state-of-the-art neural abstract summarization models based on a multilingual encoder–decoder architecture can be used to enable cross-lingual extreme summaries of scholarly texts. To this end, we compile a new abstractive cross-lingual summarization dataset for the scholarly domain in four different languages, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different ...
Nowadays there is a growing trend in many scientific disciplines to support researchers by provid... more Nowadays there is a growing trend in many scientific disciplines to support researchers by providing enhanced information access through linking of publications and underlying datasets, so as to support research with infrastructure to enhance reproducibility and reusability of research results. In this research note, we present an overview of an ongoing research project, named VADIS (VAriable Detection, Interlinking and Summarization), that aims at developing technology and infrastructure for enhanced information access in the Social Sciences via search and summarization of publications on the basis of automatic identification and indexing of survey variables in text. We provide an overview of the overarching vision underlying our project, its main components, and related challenges, as well as a thorough discussion of how these are meant to address the limitations of current information access systems for publications in the Social Sciences. We show how this goal can be concretely implemented in an end-user system by presenting a search prototype, which is based on user requirements collected from qualitative interviews with empirical Social Science researchers.
In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on ... more In this paper, we provide an overview of the SV-Ident shared task as part of the 3rd Workshop on Scholarly Document Processing (SDP) at COLING 2022. In the shared task, participants were provided with a sentence and a vocabulary of variables, and asked to identify which variables, if any, are mentioned in individual sentences from scholarly documents in full text. Two teams made a total of 9 submissions to the shared task leaderboard. While none of the teams improve on the baseline systems, we still draw insights from their submissions. Furthermore, we provide a detailed evaluation. Data and baselines for our shared task are freely available at https://github. com/vadis-project/sv-ident.
Zenodo (CERN European Organization for Nuclear Research), May 21, 2022
Conferences are considered as one of the core media of scholarly communication in many research d... more Conferences are considered as one of the core media of scholarly communication in many research disciplines. The articles presented via such opportunities are usually published as proceedings. Generally, the titles of proceedings are composed of a limited subset of English terms that uniquely describe an event. Most proceedings title are available in digital form these days-be it as part of the publishing or when referenced e.g. in citations. Therefore, the titles of proceedings are a potential source for automatically extracting metadata about events. This paper presents a semantic parser to extract conference metadata from English titles of proceedings of scientific events. The parser we present has been designed and tested based on over 43,640 proceedings titles extracted from four complementary, highquality sources. To improve the flexibility and simplicity of the parser, a combined parser / dictionary based approach is applied.
Journal of the Association for Information Science and Technology
Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) prog... more Many disciplines, including the broad Field of Information (iField), offer Data Science (DS) programs. There have been significant efforts exploring an individual discipline's identity and unique contributions to the broader DS education landscape. To advance DS education in the iField, the iSchool Data Science Curriculum Committee (iDSCC) was formed and charged with building and recommending a DS education framework for iSchools. This paper reports on the research process and findings of a series of studies to address important questions: What is the iField identity in the multidisciplinary DS education landscape? What is the status of DS education in iField schools? What knowledge and skills should be included in the core curriculum for iField DS education? What are the jobs available for DS graduates from the iField? What are the differences between graduate‐level and undergraduate‐level DS education? Answers to these questions will not only distinguish an iField approach to ...
Proceedings of the 22nd ACM/IEEE Joint Conference on Digital Libraries
The number of scientific publications nowadays is rapidly increasing, causing information overloa... more The number of scientific publications nowadays is rapidly increasing, causing information overload for researchers and making it hard for scholars to keep up to date with current trends and lines of work. Consequently, recent work on applying text mining technologies for scholarly publications has investigated the application of automatic text summarization technologies, including extreme summarization, for this domain. However, previous work has concentrated only on monolingual settings, primarily in English. In this paper, we fill this research gap and present an abstractive cross-lingual summarization dataset for four different languages in the scholarly domain, which enables us to train and evaluate models that process English papers and generate summaries in German, Italian, Chinese and Japanese. We present our new X-SCITLDR dataset for multilingual summarization and thoroughly benchmark different models based on a state-of-the-art multilingual pre-trained model, including a two-stage 'summarize and translate' approach and a direct cross-lingual model. We additionally explore the benefits of intermediate-stage training using English monolingual summarization and machine translation as intermediate tasks and analyze performance in zero-and few-shot scenarios. CCS Concepts • Computing methodologies → Natural language processing; Natural language generation; Language resources.
Summary: - RDF is a simple, graph-based data model for metadata on the web - RDF has an XML synta... more Summary: - RDF is a simple, graph-based data model for metadata on the web - RDF has an XML syntax for: - Exchanging RDF Models - Embedding RDF Models into web pages - Advantages over XML - Data model is agnostic to syntactic variations - Information from different models and locations can easily be linked - Some important operations are trivial (i.e. merging two models) - RDF Schema defines special resources and predicates for defining vocabularies - Vokabular: Class, SubClassOf, domain, range - Implicit information can be derived using simple derivation rules - There is no clear separation between model and schema, schema elements can be part of an RDF mode
Despite many efforts for making data about scholarly publications available on the Web of Data, i... more Despite many efforts for making data about scholarly publications available on the Web of Data, information about academic conferences is still contained in (at best) free-text format. Availability of this data in a structured format would enable more efficient decision making for researchers, libraries, publishers, funding and evaluation bodies. This demo paper showcases the Springer Linked Open Data (LOD) conference portal (available at http://lod.springer.com). We cover the architecture, vocabularies and features of the portal and present usage scenarios.
Proceedings of the 5th Workshop on Argument Mining, 2018
Argumentation is arguably one of the central features of scientific language. We present ArguminS... more Argumentation is arguably one of the central features of scientific language. We present ArguminSci, an easy-to-use tool that analyzes argumentation and other rhetorical aspects of scientific writing, which we collectively dub scitorics. The main aspect we focus on is the fine-grained argumentative analysis of scientific text through identification of argument components. The functionality of ArguminSci is accessible via three interfaces: as a command line tool, via a RESTful application programming interface, and as a web application.
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Research by Kai Eckert
The central contribution is the ICE-Map Visualization, a treemap-based visualization on top of a generalized statistical framework that is able to visualize almost arbitrary usage information. The proper selection of an existing KOS for available documents and the evaluation of a KOS for different indexing techniques by means of the ICE-Map Visualization is demonstrated.
For the creation of a new KOS, an approach based on crowdsourcing is presented that uses feedback from Amazon Mechanical Turk to relate terms hierarchically. The extension of an existing KOS with new terms derived from the documents to be indexed is performed with a machine-learning approach that relates the terms to existing concepts in the hierarchy. The features are derived from text snippets in the result list of a web search engine. For the splitting of overpopulated
concepts into new subconcepts, an interactive clustering approach is presented that is able to propose names for the new subconcepts. The implementation of a framework is described that integrates all approaches of this thesis and contains the reference implementation of the ICE-Map Visual-
ization. It is extendable and supports the implementation of evaluation methods that build on other evaluations. Additionally, it supports the visualization of the results and the implementation of new visualizations. An important building block for practical applications is the simple linguistic indexer that is presented as minor contribution. It is knowledge-poor and works without any training.
This thesis applies computer science approaches in the domain of information science. The introduction describes the foundations in information science; in the conclusion, the focus is set on the relevance for practical applications, especially
regarding the handling of different qualities of KOSs due to automatic and semi-automatic maintenance.
Linked Data, the openness and flexibility is a mixed blessing. For them, data validation according to predefined constraints is a much sought-after feature, particularly as this
is taken for granted in the XML world. Based on our work
in the DCMI RDF Application Profiles Task Group
and in cooperation with the W3C Data Shapes Working Group, we published by today 81 types of constraints that are required
by various stakeholders for data applications. These constraint types form the basis to investigate the role that reasoning and different semantics play in practical data validation, why reasoning is beneficial for RDF validation, and
how to overcome the major shortcomings when validating
RDF data by performing reasoning prior to validation. For
each constraint type, we examine (1) if reasoning may im-
prove data quality, (2) how efficient in terms of runtime val-
idation is performed with and without reasoning, and (3) if
validation results depend on underlying semantics which differs between reasoning and validation. Using these findings,
we determine for the most common constraint languages
which constraint types they enable to express and give di-
rections for the further development of constraint languages.
alyze the distribution of indexing results within a given Knowledge Organization System (KOS) hierarchy and allows the user to explore the document sets and the KOSs at the same time. In this paper, we demonstrate the use of the ICE-Map Visualization in combination with a simple
automatic indexer to visualize the semantic overlap between a KOS and a set of documents.
Papers by Kai Eckert
The central contribution is the ICE-Map Visualization, a treemap-based visualization on top of a generalized statistical framework that is able to visualize almost arbitrary usage information. The proper selection of an existing KOS for available documents and the evaluation of a KOS for different indexing techniques by means of the ICE-Map Visualization is demonstrated.
For the creation of a new KOS, an approach based on crowdsourcing is presented that uses feedback from Amazon Mechanical Turk to relate terms hierarchically. The extension of an existing KOS with new terms derived from the documents to be indexed is performed with a machine-learning approach that relates the terms to existing concepts in the hierarchy. The features are derived from text snippets in the result list of a web search engine. For the splitting of overpopulated
concepts into new subconcepts, an interactive clustering approach is presented that is able to propose names for the new subconcepts. The implementation of a framework is described that integrates all approaches of this thesis and contains the reference implementation of the ICE-Map Visual-
ization. It is extendable and supports the implementation of evaluation methods that build on other evaluations. Additionally, it supports the visualization of the results and the implementation of new visualizations. An important building block for practical applications is the simple linguistic indexer that is presented as minor contribution. It is knowledge-poor and works without any training.
This thesis applies computer science approaches in the domain of information science. The introduction describes the foundations in information science; in the conclusion, the focus is set on the relevance for practical applications, especially
regarding the handling of different qualities of KOSs due to automatic and semi-automatic maintenance.
Linked Data, the openness and flexibility is a mixed blessing. For them, data validation according to predefined constraints is a much sought-after feature, particularly as this
is taken for granted in the XML world. Based on our work
in the DCMI RDF Application Profiles Task Group
and in cooperation with the W3C Data Shapes Working Group, we published by today 81 types of constraints that are required
by various stakeholders for data applications. These constraint types form the basis to investigate the role that reasoning and different semantics play in practical data validation, why reasoning is beneficial for RDF validation, and
how to overcome the major shortcomings when validating
RDF data by performing reasoning prior to validation. For
each constraint type, we examine (1) if reasoning may im-
prove data quality, (2) how efficient in terms of runtime val-
idation is performed with and without reasoning, and (3) if
validation results depend on underlying semantics which differs between reasoning and validation. Using these findings,
we determine for the most common constraint languages
which constraint types they enable to express and give di-
rections for the further development of constraint languages.
alyze the distribution of indexing results within a given Knowledge Organization System (KOS) hierarchy and allows the user to explore the document sets and the KOSs at the same time. In this paper, we demonstrate the use of the ICE-Map Visualization in combination with a simple
automatic indexer to visualize the semantic overlap between a KOS and a set of documents.