We are very pleased to announce that our group got a paper accepted at the Journal of Web Semantics on Managing the Evolution and Preservation of the Data Web (MEPDaW) issue.
The Journal of Web Semantics is an interdisciplinary journal based on research and applications of various subject areas that contribute to the development of a knowledge-intensive and intelligent service Web. These areas include knowledge technologies, ontology, agents, databases and the semantic grid, obviously, disciplines like information retrieval, language technology, human-computer interaction, and knowledge discovery are of major relevance as well. All aspects of Semantic Web development are covered. The publication of large-scale experiments and their analysis is also encouraged to clearly illustrate scenarios and methods that introduce semantics into existing Web interfaces, contents, and services. The journal emphasizes the publication of papers that combine theories, methods, and experiments from different subject areas in order to deliver innovative semantic methods and applications.
Here is the pre-print of the accepted paper with its abstract:
- “TISCO: Temporal Scoping of Facts” by Anisa Rula, Matteo Palmonari, Simone Rubinacci, Axel-Cyrille Ngonga Ngomo, Jens Lehmann, Andrea Maurino and Diego Esteves
Abstract: Some facts in the Web of Data are only valid within a certain time interval. However, most of the knowledge bases available on the Web of Data do not provide temporal information explicitly. Hence, the relationship between facts and time intervals is often lost. A few solutions are proposed in this field. Most of them are concentrated more in extracting facts with time intervals rather than trying to map facts with time intervals. This paper studies the problem of determining the temporal scopes of facts, that is, deciding the time intervals in which the fact is valid. We propose a generic approach which addresses this problem by curating temporal information of facts in the knowledge bases. Our proposed framework, Temporal Information Scoping (TISCO) exploits evidence collected from the Web of Data and the Web. The evidence is combined within a three-step approach which comprises matching, selection and merging. This is the first work employing matching methods that consider both a single fact or a group of facts at a time. We evaluate our approach against a corpus of facts as input and different parameter settings for the underlying algorithms. Our results suggest that we can detect temporal information for facts from DBpedia with an f-measure of up to 80%.
This research has been supported in part by the research grant number 17A209 from the University of Milano-Bicocca and by a scholarship from the University of Bonn
We are very pleased to announce that our group got 3 workshop papers accepted for presentation at EMNLP 2018 conference, that will be held on 1st of November 2018, Brussels, Belgium.
FEVER: The First Workshop on Fact Extraction and Verification: With billions of individual pages on the web providing information on almost every conceivable topic, we should have the ability to collect facts that answer almost every conceivable question. However, only a small fraction of this information is contained in structured sources (Wikidata, Freebase, etc.) – we are therefore limited by our ability to transform free-form text to structured knowledge. There is, however, another problem that has become the focus of a lot of recent research and media coverage: false information coming from unreliable sources. In an effort to jointly address both problems, a workshop promoting research in joint Fact Extraction and VERification (FEVER) has been proposed.
W-NUT: The 4th Workshop on Noisy User-generated Text: focuses on Natural Language Processing applied to noisy user-generated text, such as that found in social media, online reviews, crowdsourced data, web forums, clinical records and language learner essays.
Here are the accepted papers with their abstracts:
- Belittling the Source: Trustworthiness Indicators to Obfuscate Fake News on the Web by Diego Esteves, Aniketh Janardhan Reddy, Piyush Chawla and Jens Lehmann.
Abstract: With the growth of the internet, the number of fake-news online has been proliferating every year. The consequences of such phenomena are manifold, ranging from lousy decision-making process to bullying and violence episodes. Therefore, fact-checking algorithms became a valuable asset. To this aim, an important step to detect fake-news is to have access to a credibility score for a given information source. However, most of the widely used Web indicators have either been shut-down to the public (e.g., Google PageRank) or are not free for use (Alexa Rank). Further existing databases are short-manually curated lists of online sources, which do not scale. Finally, most of the research on the topic is theoretical-based or explore confidential data in a restricted simulation environment. In this paper we explore current research, highlight the challenges and propose solutions to tackle the problem of classifying websites into a credibility scale. The proposed model automatically extracts source reputation cues and computes a credibility factor, providing valuable insights which can help in belittling dubious and confirming trustful unknown websites. Experimental results outperform state of the art in the 2-classes and 5-classes setting.
Abstract: Named Entity Recognition (NER) is an important subtask of information extraction that seeks to locate and recognise named entities. Despite recent achievements, we still face limitations in correctly detecting and classifying entities, prominently in short and noisy text, such as Twitter. An important negative aspect in most of NER approaches is the high dependency on hand-crafted features and domain-specific knowledge, necessary to achieve state-of-the-art results. Thus, devising models to deal with such linguistically complex contexts is still challenging. In this paper, we propose a novel multi-level architecture that does not rely on any specific linguistic resource or encoded rule. Unlike traditional approaches, we use features extracted from images and text to classify named entities. Experimental tests against state-of-the-art NER for Twitter on the Ritter dataset present competitive results (0.59 F-measure), indicating that this approach may lead towards better NER models.
- DeFactoNLP: Fact Verification using Entity Recognition, TFIDF Vector Comparison and Decomposable Attention by Aniketh Janardhan Reddy and Gil Rocha and Diego Esteves.
Abstract: In this paper, we describe DeFactoNLP, the system we designed for the FEVER 2018 Shared Task. The aim of this task was to conceive a system that can not only automatically assess the veracity of a claim but also retrieve evidence supporting this assessment from Wikipedia. In our approach, the Wikipedia documents whose Term Frequency-Inverse Document Frequency (TFIDF) vectors are most similar to the vector of the claim and those documents whose names are similar to those of the named entities (NEs) mentioned in the claim are identified as the documents which might contain evidence. The sentences in these documents are then supplied to a textual entailment recognition module. This module calculates the probability of each sentence supporting the claim, contradicting the claim or not providing any relevant information to assess the veracity of the claim. Various features computed using these probabilities are finally used by a Random Forest classifier to determine the overall truthfulness of the claim. The sentences which support this classification are returned as evidence. Our approach achieved a 0.4277 evidence F1-score, a 0.5136 label accuracy and a 0.3833 FEVER score.
This research was partially supported by an EU H2020 grant provided for the WDAqua project (GA no. 642795) and by the DAAD under the “International promovieren in Deutschland fur alle” (IPID4all) project.
Looking forward to seeing you at The EMNLP/FEVER 2018.
We are very pleased to announce that our group got 2 papers accepted for presentation at The 21st International Conference on Knowledge Engineering and Knowledge Management (EKAW 2018) conference, which will be held on 12 – 16 November 2018 in Nancy, France.
The 21st International Conference on Knowledge Engineering and Knowledge Management is in concern with all aspects about eliciting, acquiring, modeling and managing knowledge, and the construction of knowledge-intensive systems and services for the semantic web, knowledge management, e-business, natural language processing, intelligent information integration, and so on. The special theme of EKAW 2018 is “Knowledge and AI”. We are indeed calling for papers that describe algorithms, tools, methodologies, and applications that exploit the interplay between knowledge and Artificial Intelligence techniques, with a special emphasis on knowledge discovery. Accordingly, EKAW 2018 will put a special emphasis on the importance of Knowledge Engineering and Knowledge Management with the help of AI as well as for AI.
Here is the list of accepted papers with their abstracts:
- “Divided we stand out! Forging Cohorts fOr Numeric Outlier Detection in large scale knowledge graphs (CONOD)” by Hajira Jabeen, Rajjat Dadwal, Gezim Sejdiu, and Jens Lehmann.
Abstract : With the recent advances in data integration and the concept of data lakes, massive pools of heterogeneous data are being curated as Knowledge Graphs (KGs). In addition to data collection, it is of utmost importance to gain meaningful insights from this composite data. However, given the graph-like representation, the multimodal nature, and large size of data, most of the traditional analytic approaches are no longer directly applicable. The traditional approaches could collect all values of a particular attribute, e.g. height, and try to perform anomaly detection for this attribute. However, it is conceptually inaccurate to compare one attribute representing different entities, e.g.~the height of buildings against the height of animals. Therefore, there is a strong need to develop fundamentally new approaches for the outlier detection in KGs. In this paper, we present a scalable approach, dubbed CONOD, that can deal with multimodal data and performs adaptive outlier detection against the cohorts of classes they represent, where a cohort is a set of classes that are similar based on a set of selected properties. We have tested the scalability of CONOD on KGs of different sizes, assessed the outliers using different inspection methods and achieved promising results.
Looking forward to seeing you at The EKAW 2018.
We are very pleased to announce that our group got one paper accepted for presentation at The SIGNLL Conference on Computational Natural Language Learning (CoNLL 2018) conference. CoNLL is a top-tier conference, yearly organized by SIGNLL (ACL’s Special Interest Group on Natural Language Learning). This year, CoNLL will be colocated with EMNLP 2018 and will be held on October 31 – November 1, 2018, Brussels, Belgium.
The aim of the CoNLL conference is to bring researchers and practitioners from both academia and industry, in the areas of deep learning, natural language processing, and learning. It is among the top-10 Natural language processing and Computational linguistics conferences.
Here is the accepted paper with its abstract:
- “Improving Response Selection in Multi-turn Dialogue Systems by Incorporating Domain Knowledge” by Debanjan Chaudhuri, Agustinus Kristiadi, Jens Lehmann and Asja Fischer.
Abstract : Building systems that can communicate with humans is a core problem in Artificial Intelligence. This work proposes a novel neural network architecture for response selection in an end-to-end multi-turn conversational dialogue setting. The architecture applies context level attention and incorporates additional external knowledge provided by descriptions of domain-specific words. It uses a bi-directional Gated Recurrent Unit (GRU) for encoding context and responses and learns to attend over the context words given the latent response representation and vice versa. In addition, it incorporates external domain specific information using another GRU for encoding the domain keyword descriptions. This allows better representation of domain-specific keywords in responses and hence improves the overall performance. Experimental results show that our model outperforms all other state-of-the-art methods for response selection in multi-turn conversations.
This research was supported by the KDDS project at Fraunhofer.
Looking forward to seeing you at The CoNLL 2018.
the Smart Data Analytics group is happy to announce AskNow 0.1 – the initial release of Question Answering Components and Tools over RDF Knowledge Graphs.
The following components with corresponding features are currently supported by AskNow:
- EARL 0.1 EARL performs entity linking and relation linking as a joint task. It uses machine learning in order to exploit the Connection Density between nodes in the knowledge graph. It relies on three base features and re-ranking steps in order to predict entities and relations.
ISWC 2018: https://arxiv.org/pdf/1801.03825.pdf
- SQG 0.1: This is a SPARQL Query Generator with modular architecture. SQG enables easy integration with other components for the construction of a fully functional QA pipeline. Currently entity relation, compound, count, and boolean questions are supported.
ESWC 2018: http://jens-lehmann.org/files/2018/eswc_qa_query_generation.pdf
- AskNow UI 0.1: The UI interface works as a platform for users to pose their questions to the AskNow QA system. The UI displays the answers based on whether the answer is an entity or a list of entities, boolean or literal. For entities it shows the abstracts from DBpedia.
- SemanticParsingQA 0.1: The Semantic Parsing-based Question Answering system is built on the integration of EARL, SQG and AskNowUI.
View this announcement on Twitter: https://twitter.com/AskNowQA/status/1040205350853599233
The AskNow Development Team
We are very pleased to announce that our group got 2 workshop papers accepted for presentation at The Federated Artificial Intelligence Meeting (FAIM) → NAMPI workshop co-organized with ICML, IJCAI/ECAI, AAMAS. The workshop took place in Stockholm, Sweden on the 15th of July 2018.
The aim of the NAMPI workshop was to bring researchers and practitioners from both academia and industry, in the areas of deep learning, program synthesis, probabilistic programming, programming languages, inductive programming and reinforcement learning, together to exchange ideas on the future of program induction with a special focus on neural network models and abstract machines. Through this workshop we look to identify common challenges, exchange ideas among and lessons learned from the different fields, as well as establish a (set of) standard evaluation benchmark(s) for approaches that learn with abstraction and/or reason with induced programs.
Here are the accepted papers with their abstracts:
- Neural Machine Translation for Query Construction and Composition by Tommaso Soru, Edgard Marx, André Valdestilhas, Diego Esteves, Diego Moussallem and Gustavo Publio.
Abstract: Research on question answering with knowledge base has recently seen an increasing use of deep architectures. In this extended abstract, we study the application of the neural machine translation paradigm for question parsing. We employ a sequence-to-sequence model to learn graph patterns in the SPARQL graph query language and their compositions. Instead of inducing the programs through question-answer pairs, we expect a semi-supervised approach, where alignments between questions and queries are built through templates. We argue that the coverage of language utterances can be expanded using late notable works in natural language generation.
- ML-Schema: Exposing the Semantics of Machine Learning with Schemas and Ontologies by Gustavo Correa Publio, Diego Esteves, Agnieszka Ławrynowicz, Panče Panov, Larisa Soldatova, Tommaso Soru, Joaquin Vanschoren and Hamid Zafar.
Abstract: The ML-Schema, proposed by the W3C Machine Learning Schema Community Group, is a top-level ontology that provides a set of classes, properties, and restrictions for representing and interchanging information on machine learning algorithms, datasets, and experiments. It can be easily extended and specialized and it is also mapped to other more domain-specific ontologies developed in the area of machine learning and data mining. In this paper we overview existing state-of-the-art machine learning interchange formats and present the first release of ML-Schema, a canonical format resulted of more than seven years of experience among different research institutions. We argue that exposing semantics of machine learning algorithms, models, and experiments through a canonical format may pave the way to better interpretability and to realistically achieve the full interoperability of experiments regardless of platform or adopted workflow solution.
This work was partially supported by NEAR AI.
We are very pleased to announce that our group got 4 demo/poster papers accepted for presentation at ISWC 2018 : The 17th International Semantic Web Conference, which will be held on October 8 – 12, 2018 in Monterey, California, USA.
The International Semantic Web Conference (ISWC) is the premier international forum where Semantic Web / Linked Data researchers, practitioners, and industry specialists come together to discuss, advance, and shape the future of semantic technologies on the web, within enterprises and in the context of the public institution.
Here is the list of the accepted papers with their abstract:
- “STATisfy Me: What are my Stats?” by Gezim Sejdiu, Ivan Ermilov, Mohamed Nadjib Mami and Jens Lehmann
Abstract: The increasing adoption of the Linked Data format, RDF, over the last two decades has brought new opportunities.
It has also raised new challenges though, especially when it comes to managing and processing large amounts of RDF data. In particular, assessing the internal structure of a data set is important, since it enables users to understand the data better. One prominent way of assessment is computing statistics about the instances and schema of a data set. However, computing statistics of large RDF data is computationally expensive.
To overcome this challenging situation, we previously built DistLODStats, a framework for parallel calculation of 32 statistical criteria over large RDF datasets, based on Apache Spark. Running DistLODStats is, thus, done via submitting jobs to a Spark cluster. Often times, this process is done manually, either by connecting to the cluster machine or via a dedicated resource manager. This approach is inconvenient as it requires acquiring new software skills as well as the direct interaction of users with the cluster.
In order to make the use of DistLODStats easier, we propose in this paper an approach for triggering RDF statistics calculation remotely simply using HTTP requests. DistLODStats is built as a plugin into the larger SANSA Framework and makes use of Apache Livy, a novel lightweight solution for interacting with Spark cluster via a REST Interface.
- “Joint Entity and Relation Linking using EARL” by Debayan Banerjee, Mohnish Dubey, Debanjan Chaudhuri and Jens Lehmann
Abstract: In order to answer natural language questions over knowledge graphs,most processing pipelines involve entity and relation linking. Traditionally, entity linking and relation linking have been performed either as dependent sequential tasks or independent parallel tasks. In this demo paper, we present EARL, which performs entity linking and relation linking as a joint single task. The system determines the best semantic connection between all keywords of the question by referring to the knowledge graph. This is achieved by exploiting the connection density between entity candidates and relation candidates. EARL uses bloom filters for faster retrieval of connection density and uses an extended label vocabulary for higher recall to improve the overall accuracy
- “Generating SPARQL Query Containment Benchmarks using the SQCFramework” by Muhammad Saleem , Qaiser Mehmood, Claus Stadler, Jens Lehmann and Axel-Cyrille Ngonga Ngomo
Abstract: In this demo paper, we present the interface of the SQCFramework, a SPARQL query containment benchmark generation framework. SQCFramework is able to generate customized SPARQL containment benchmarks from real SPARQL query logs. To this end, the framework makes use of different clustering techniques. It is flexible enough to generate benchmarks of varying sizes and complexities according to user-defined criteria on important SPARQL features for query containment benchmarking. We evaluate the usability of the interface by using the standard system usability scale questionnaire. Our overall usability score of 82.33 suggests that the online interface is consistent, easy to use, and the various functions of the system are well integrated.
- “Synthesizing a Knowledge Graph of Data Scientist Job Offers with MINTE+” by Mikhail Galkin, Diego Collarana, Mayesha Tasnim and Maria-Esther Vidal
Abstract: Data Scientist is one of the most sought-after jobs of this decade. In order to analyze the job market in this domain, interested institutions have to integrate numerous job advertising coming from heterogeneous Web sources e.g., job portals, company websites, professional community platforms such as StackOverflow, GitHub, etc. In this demo, we show the application of the RDF Molecule-Based Integration Framework MINTE+ in the domain-specific application of job market analysis. The use of RDF molecules for knowledge representation is a core element of the framework gives MINTE+ enough flexibility to integrate job advertising from different web resources and countries. Attendees will observe how exploration and analysis of the data science job market in Europe can be facilitated by synthesizing at query time a consolidated knowledge graph of job advertising. The demo is available at: https://github.com/RDF-Molecules/MINTE/blob/master/README.md#live-demo
This work has received funding from the EU Horizon 2020 projects BigDataEurope (GA 644564) and QROWD (GA no. 723088), the Marie Skłodowska-Curie action WDAqua(GA No 642795), and HOBBIT (GA. 688227), and (project SlideWiki, grant no. 688095), and the German Ministry of Education and Research (BMBF) in the context of the projects LiDaKrA (Linked-Data-basierte Kriminalanalyse, grant no. 13N13627) and InDaSpacePlus (grant no. 01IS17031).
Looking forward to seeing you at The ISWC 2018.
We are very pleased to announce that our group got two papers and two poster papers accepted for presentation at SEMANTiCS 2018 conference which will take place in Vienna, Austria on 10th – 13th of September 2018.
SEMANTiCS is an established knowledge hub where technology professionals, industry experts, researchers and decision makers can learn about new technologies, innovations and enterprise implementations in the fields of Linked Data and Semantic AI. Since 2005, the conference series has focused on semantic technologies, which are today together with other methodologies such as NLP and machine learning the core of intelligent systems. The conference highlights the benefits of standards-based approaches.
Here is the list of accepted papers with their abstracts:
- “Profiting from Kitties on Ethereum: Leveraging Blockchain RDF with SANSA” by Damien Graux, Gezim Sejdiu, Hajira Jabeen, Jens Lehmann, Danning Sui, Dominik Muhs and Johannes Pfeffer (Poster & Demo Track)
Abstract: In this poster, we will present attendees how the recent state-of-the-art Semantic Web tool SANSA could be used to tackle blockchain specific challenges. In particular, the poster will focus on the use case of CryptoKitties: a popular Ethereum-based online game where users are able to trade virtual kitty pets in a secure way.
- “SPIRIT: A Semantic Transparency and Compliance Stack” by Patrick Westphal, Javier Fernández, Sabrina Kirrane and Jens Lehmann (Poster & Demo Track)
Abstract: The European General Data Protection Regulation (GDPR) sets new precedents for the processing of personal data. In this paper, we propose an architecture that provides an automated means to enable transparency with respect to personal data processing and sharing transactions and compliance checking with respect to data subject usage policies and GDPR legislative obligations.
- “SemSur: A Core Ontology for the Semantic Representation of Research Findings” by Said Fathalla, Sahar Vahdati, Sören Auer and Christoph Lange (Research & Innovation)
Abstract: The way how research is communicated using text publications has not changed much over the past decades. We have the vision that ultimately researchers will work on a common structured knowledge base comprising comprehensive semantic and machine-comprehensible descriptions of their research, thus making research contributions more transparent and comparable. We present the SemSur ontology for semantically capturing the information commonly found in survey and review articles. SemSur is able to represent scientific results and to publish them in a comprehensive knowledge graph, which provides an efficient overview of a research field, and to compare research findings withrelated works in a structured way, saving researchers a significant amount of time and effort. The new release of SemSur covers more domains, defines better alignment with external ontologies and rules for eliciting implicit knowledge. We discuss possible applications and present an evaluation of our approach with the retrospective, exemplary semantification of a survey. We demonstrate the utility of the SemSur ontology to answer queries about the different research contributions covered by the survey. SemSur is currently used and maintained at OpenResearch.org.
- “Cross-Lingual Ontology Enrichment Based on Multi-Agent Architecture” by Mohamed Ali, Said Fathalla, Shimaa Ibrahim, Mohamed Kholief, Yasser Hassan (Research & Innovation)
Abstract: The proliferation of ontologies and multilingual data available on the Web has motivated many researchers to contribute to multilingual and cross-lingual ontology enrichment. Cross-lingual ontology enrichment greatly facilitates ontology learning from multilingual text/ontologies in order to support collaborative ontology engineering process.This article proposes a cross-lingual ontology enrichment (CLOE) approach based on a multi-agent architecture in order to enrich ontologies from a multilingual text or ontology. This has several advantages: 1) an ontology is used to enrich another one, written in a different natural language, and 2) several ontologies could be enriched at the same time using a single chunk of text (Simultaneous Ontology Enrichment). A prototype for the proposed approach has been implemented in order to enrich several ontologies using English, Arabic and German text. Evaluation results are promising and showing that CLOE performs well in comparison with four state-of-the-art approaches.
Furthermore, we are pleased to inform that we got a talk accepted, which will be co-located with the Industry track.
Here is the accepted talk and its abstract :
- “Using the SANSA Stack on a 38 Billion Triple Ethereum Blockchain Dataset”
Abstract: SANSA is the first open source project that allows out of the box horizontally scalable analytics for large knowledge graphs. The talk will cover the main features of SANSA introducing its different layers namely, RDF, Query, Inference and Machine Learning. The talk also covers a large-scale Etherum blockchain use case at Alethio, a spinoff company of Consensys. Alethio is building an analytics dashboard that strives to provide transparency over what’s happening on the Ethereum p2p network, the transaction pool and the blockchain in order to provide “blockchain archaeology”. Their 6 billion triple dataset contains large-scale blockchain transaction data modelled as RDF according to the structure of the Ethereum ontology. Alethio chose to work with SANSA after experimenting with other existing engines. Specifically, the initial goal of Alethio was to load a 2TB EthOn dataset containing more than 6 billion triples and then performing several analytic queries on it with up to three inner joins.
SANSA has successfully provided a platform that allows running these queries.
Speaker: Hajira Jabeen
This work has received funding from the EU Horizon 2020 projects BigDataOcean (GA. 732310) and QROWD (GA no. 723088), the Marie Skłodowska-Curie action WDAqua (GA No 642795), and SPECIAL (GA. 731601).
Looking forward to seeing you at The SEMANTiCS 2018.
We are very pleased to announce that our group got a short paper accepted for presentation at ECML/PKDD 2018 (nectar track) : The European Conference on Machine Learning and Principles and Practice of Knowledge Discovery in Databases will take place in the Croke Park Conference Centre, Dublin, Ireland during the 10 – 14 September 2018.
This event is the premier European machine learning and data mining conference and builds upon over 16 years of successful events and conferences held across Europe. reland is delighted to host and to bring together participants to Croke Park- one of the iconic sporting venues but also providing a world-class conference facility.
Here is the accepted paper with its abstract:
- “Deep Query Ranking for Question Answering over Knowledge Bases” by Hamid Zafar, Giulio Napolitano, and Jens Lehmann (Nectar track)
Abstract: We study question answering systems over knowledge graphs which map an input natural language question into candidate formal queries. Often, a ranking mechanism is used to discern the queries with higher similarity to the given question. Considering the intrinsic complexity of the natural language, finding the most accurate formal counter-part is a challenging task. In our recent paper, we leveraged Tree-LSTM to exploit the syntactical structure of input question as well as the candidate formal queries to compute the similarities. An empirical study shows that taking the structural information of the input question and candidate query into account enhances the performance, when compared to the baseline system.
This research was supported by EU H2020 grants for the projects HOBBIT (GA no. 688227) and WDAqua (GA no. 642795) as well as by German Federal Ministry of Education and Research (BMBF) funding for the project SOLIDE (no. 13N14456).
Looking forward to seeing you at The TPDL 2018.
We are very pleased to announce that our group got two papers accepted in TPDL 2018 : The 22nd International Conference on Theory and Practice of Digital Libraries.
The TPDL is is a well-established scientific and technical forum on the broad topic of digital libraries, bringing together researchers, developers, content providers and users in digital libraries and digital content management. The 22nd TPDL will take place in Porto, Portugal on September 10-13, 2018. The general theme of TPDL 2018 is “Digital Libraries for Open Knowledge”. 2017-2018 are considered “Year of Open” and 2018 is “the TPDL of Open”. TPDL 2018 wants to gather all the communities engaged to make the knowledge more and more open, using the available technologies, standards and infrastructures, but reflecting about the new challenges, policies and other issues to make it happen. Thus, our activities in the context of scholarly communication matched very well.
Here is the list of the accepted papers with their abstract:
- “Unveiling Scholarly Communities over Knowledge Graphs” by Sahar Vahdati, Guillermo Palma, Rahul Jyoti Nath, Maria-Esther Vidal, Christoph Lange and Sören Auer
Abstract: Knowledge graphs represent the meaning of properties of real-world entities and relationships among them in a natural way. Exploiting semantics encoded in knowledge graphs enables the implementation of knowledge-driven tasks such as semantic retrieval, query processing, and question answering, as well as solutions to knowledge discovery tasks including pattern discovery and link prediction. In this paper, we tackle the problem of knowledge discovery in scholarly knowledge graphs, i.e., graphs that integrate scholarly data, and present KORONA, a knowledge-driven framework able to unveil scholarly communities for the prediction of scholarly networks. \koronaB implements a graph partition approach and relies on semantic similarity measures to determine relatedness between scholarly entities. As a proof of concept, we built a scholarly knowledge graph with data from researchers, conferences, and papers of the Semantic Web area, and apply \koronaB to uncover co-authorship networks. Results observed from our empirical evaluation suggest that exploiting semantics in scholarly knowledge graphs enables the identification of previously unknown relations between researchers. We furthermore point out how these observations can be generalized to other scholarly entities, e.g., articles or institutions, for the prediction of other scholarly patterns, e.g., co-citations or academic collaboration.
- “Metadata Analysis of Scholarly Events on of Computer Science, Physics, Engineering and Mathematics” by Said Fathalla, Sahar Vahdati, Sören Auer and Christoph Lange
Abstract: Although digitization has significantly eased publishing, finding a relevant and suitable channel of publishing still remains challenging. To obtain a better understanding of scholarly communication in different fields and the role of scientific events, metadata of scientific events of four research communities have analyzed: Computer Science, Physics, Engineering, and Mathematics. Our transferable analysis methodology is based on descriptive statistics as well as exploratory data analysis. Metadata used in this work have been collected from the OpenResearch.org community platform and SCImago as the main resources containing metadata of scientific events in a semantically structured way. The evaluation uses metrics such as continuity, geographical and time-wise distribution, field popularity and productivity as well as event progress ratio and rankings based on the SJR indicator and h5 indices.
Looking forward to seeing you at The TPDL 2018.