Towards Understanding and Improving Multilingual Collaborative Ontology Development in Wikidata
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What Do Wikidata and Wikipedia Have in Common? an Analysis of Their Use of External References
What do Wikidata and Wikipedia Have in Common? An Analysis of their Use of External References Alessandro Piscopo Pavlos Vougiouklis Lucie-Aimée Kaffee University of Southampton University of Southampton University of Southampton United Kingdom United Kingdom United Kingdom [email protected] [email protected] [email protected] Christopher Phethean Jonathon Hare Elena Simperl University of Southampton University of Southampton University of Southampton United Kingdom United Kingdom United Kingdom [email protected] [email protected] [email protected] ABSTRACT one hundred thousand. These users have gathered facts about Wikidata is a community-driven knowledge graph, strongly around 24 million entities and are able, at least theoretically, linked to Wikipedia. However, the connection between the to further expand the coverage of the knowledge graph and two projects has been sporadically explored. We investigated continuously keep it updated and correct. This is an advantage the relationship between the two projects in terms of the in- compared to a project like DBpedia, where data is periodically formation they contain by looking at their external references. extracted from Wikipedia and must first be modified on the Our findings show that while only a small number of sources is online encyclopedia in order to be corrected. directly reused across Wikidata and Wikipedia, references of- Another strength is that all the data in Wikidata can be openly ten point to the same domain. Furthermore, Wikidata appears reused and shared without requiring any attribution, as it is to use less Anglo-American-centred sources. These results released under a CC0 licence1. -
Wikipedia Knowledge Graph with Deepdive
The Workshops of the Tenth International AAAI Conference on Web and Social Media Wiki: Technical Report WS-16-17 Wikipedia Knowledge Graph with DeepDive Thomas Palomares Youssef Ahres [email protected] [email protected] Juhana Kangaspunta Christopher Re´ [email protected] [email protected] Abstract This paper is organized as follows: first, we review the related work and give a general overview of DeepDive. Sec- Despite the tremendous amount of information on Wikipedia, ond, starting from the data preprocessing, we detail the gen- only a very small amount is structured. Most of the informa- eral methodology used. Then, we detail two applications tion is embedded in unstructured text and extracting it is a non trivial challenge. In this paper, we propose a full pipeline that follow this pipeline along with their specific challenges built on top of DeepDive to successfully extract meaningful and solutions. Finally, we report the results of these applica- relations from the Wikipedia text corpus. We evaluated the tions and discuss the next steps to continue populating Wiki- system by extracting company-founders and family relations data and improve the current system to extract more relations from the text. As a result, we extracted more than 140,000 with a high precision. distinct relations with an average precision above 90%. Background & Related Work Introduction Until recently, populating the large knowledge bases relied on direct contributions from human volunteers as well With the perpetual growth of web usage, the amount as integration of existing repositories such as Wikipedia of unstructured data grows exponentially. Extract- info boxes. These methods are limited by the available ing facts and assertions to store them in a struc- structured data and by human power. -
Knowledge Graphs on the Web – an Overview Arxiv:2003.00719V3 [Cs
January 2020 Knowledge Graphs on the Web – an Overview Nicolas HEIST, Sven HERTLING, Daniel RINGLER, and Heiko PAULHEIM Data and Web Science Group, University of Mannheim, Germany Abstract. Knowledge Graphs are an emerging form of knowledge representation. While Google coined the term Knowledge Graph first and promoted it as a means to improve their search results, they are used in many applications today. In a knowl- edge graph, entities in the real world and/or a business domain (e.g., people, places, or events) are represented as nodes, which are connected by edges representing the relations between those entities. While companies such as Google, Microsoft, and Facebook have their own, non-public knowledge graphs, there is also a larger body of publicly available knowledge graphs, such as DBpedia or Wikidata. In this chap- ter, we provide an overview and comparison of those publicly available knowledge graphs, and give insights into their contents, size, coverage, and overlap. Keywords. Knowledge Graph, Linked Data, Semantic Web, Profiling 1. Introduction Knowledge Graphs are increasingly used as means to represent knowledge. Due to their versatile means of representation, they can be used to integrate different heterogeneous data sources, both within as well as across organizations. [8,9] Besides such domain-specific knowledge graphs which are typically developed for specific domains and/or use cases, there are also public, cross-domain knowledge graphs encoding common knowledge, such as DBpedia, Wikidata, or YAGO. [33] Such knowl- edge graphs may be used, e.g., for automatically enriching data with background knowl- arXiv:2003.00719v3 [cs.AI] 12 Mar 2020 edge to be used in knowledge-intensive downstream applications. -
Towards a Korean Dbpedia and an Approach for Complementing the Korean Wikipedia Based on Dbpedia
Towards a Korean DBpedia and an Approach for Complementing the Korean Wikipedia based on DBpedia Eun-kyung Kim1, Matthias Weidl2, Key-Sun Choi1, S¨orenAuer2 1 Semantic Web Research Center, CS Department, KAIST, Korea, 305-701 2 Universit¨at Leipzig, Department of Computer Science, Johannisgasse 26, D-04103 Leipzig, Germany [email protected], [email protected] [email protected], [email protected] Abstract. In the first part of this paper we report about experiences when applying the DBpedia extraction framework to the Korean Wikipedia. We improved the extraction of non-Latin characters and extended the framework with pluggable internationalization components in order to fa- cilitate the extraction of localized information. With these improvements we almost doubled the amount of extracted triples. We also will present the results of the extraction for Korean. In the second part, we present a conceptual study aimed at understanding the impact of international resource synchronization in DBpedia. In the absence of any informa- tion synchronization, each country would construct its own datasets and manage it from its users. Moreover the cooperation across the various countries is adversely affected. Keywords: Synchronization, Wikipedia, DBpedia, Multi-lingual 1 Introduction Wikipedia is the largest encyclopedia of mankind and is written collaboratively by people all around the world. Everybody can access this knowledge as well as add and edit articles. Right now Wikipedia is available in 260 languages and the quality of the articles reached a high level [1]. However, Wikipedia only offers full-text search for this textual information. For that reason, different projects have been started to convert this information into structured knowledge, which can be used by Semantic Web technologies to ask sophisticated queries against Wikipedia. -
Improving Knowledge Base Construction from Robust Infobox Extraction
Improving Knowledge Base Construction from Robust Infobox Extraction Boya Peng∗ Yejin Huh Xiao Ling Michele Banko∗ Apple Inc. 1 Apple Park Way Sentropy Technologies Cupertino, CA, USA {yejin.huh,xiaoling}@apple.com {emma,mbanko}@sentropy.io∗ Abstract knowledge graph created largely by human edi- tors. Only 46% of person entities in Wikidata A capable, automatic Question Answering have birth places available 1. An estimate of 584 (QA) system can provide more complete million facts are maintained in Wikipedia, not in and accurate answers using a comprehen- Wikidata (Hellmann, 2018). A downstream appli- sive knowledge base (KB). One important cation such as Question Answering (QA) will suf- approach to constructing a comprehensive knowledge base is to extract information from fer from this incompleteness, and fail to answer Wikipedia infobox tables to populate an ex- certain questions or even provide an incorrect an- isting KB. Despite previous successes in the swer especially for a question about a list of enti- Infobox Extraction (IBE) problem (e.g., DB- ties due to a closed-world assumption. Previous pedia), three major challenges remain: 1) De- work on enriching and growing existing knowl- terministic extraction patterns used in DBpe- edge bases includes relation extraction on nat- dia are vulnerable to template changes; 2) ural language text (Wu and Weld, 2007; Mintz Over-trusting Wikipedia anchor links can lead to entity disambiguation errors; 3) Heuristic- et al., 2009; Hoffmann et al., 2011; Surdeanu et al., based extraction of unlinkable entities yields 2012; Koch et al., 2014), knowledge base reason- low precision, hurting both accuracy and com- ing from existing facts (Lao et al., 2011; Guu et al., pleteness of the final KB. -
Falcon 2.0: an Entity and Relation Linking Tool Over Wikidata
Falcon 2.0: An Entity and Relation Linking Tool over Wikidata Ahmad Sakor Kuldeep Singh [email protected] [email protected] L3S Research Center and TIB, University of Hannover Cerence GmbH and Zerotha Research Hannover, Germany Aachen, Germany Anery Patel Maria-Esther Vidal [email protected] [email protected] TIB, University of Hannover L3S Research Center and TIB, University of Hannover Hannover, Germany Hannover, Germany ABSTRACT Management (CIKM ’20), October 19–23, 2020, Virtual Event, Ireland. ACM, The Natural Language Processing (NLP) community has signifi- New York, NY, USA, 8 pages. https://doi.org/10.1145/3340531.3412777 cantly contributed to the solutions for entity and relation recog- nition from a natural language text, and possibly linking them to 1 INTRODUCTION proper matches in Knowledge Graphs (KGs). Considering Wikidata Entity Linking (EL)- also known as Named Entity Disambiguation as the background KG, there are still limited tools to link knowl- (NED)- is a well-studied research domain for aligning unstructured edge within the text to Wikidata. In this paper, we present Falcon text to its structured mentions in various knowledge repositories 2.0, the first joint entity and relation linking tool over Wikidata. It (e.g., Wikipedia, DBpedia [1], Freebase [4] or Wikidata [28]). Entity receives a short natural language text in the English language and linking comprises two sub-tasks. The first task is Named Entity outputs a ranked list of entities and relations annotated with the Recognition (NER), in which an approach aims to identify entity proper candidates in Wikidata. The candidates are represented by labels (or surface forms) in an input sentence. -
Wembedder: Wikidata Entity Embedding Web Service
Wembedder: Wikidata entity embedding web service Finn Årup Nielsen Cognitive Systems, DTU Compute Technical University of Denmark Kongens Lyngby, Denmark [email protected] ABSTRACT as the SPARQL-based Wikidata Query Service (WDQS). General I present a web service for querying an embedding of entities in functions for finding related items or generating fixed-length fea- the Wikidata knowledge graph. The embedding is trained on the tures for machine learning are to my knowledge not available. Wikidata dump using Gensim’s Word2Vec implementation and a simple graph walk. A REST API is implemented. Together with There is some research that combines machine learning and Wiki- the Wikidata API the web service exposes a multilingual resource data [9, 14], e.g., Mousselly-Sergieh and Gurevych have presented for over 600’000 Wikidata items and properties. a method for aligning Wikidata items with FrameNet based on Wikidata labels and aliases [9]. Keywords Wikidata, embedding, RDF, web service. Property suggestion is running live in the Wikidata editing inter- face, where it helps editors recall appropriate properties for items during manual editing, and as such a form of recommender sys- 1. INTRODUCTION tem. Researchers have investigated various methods for this pro- The Word2Vec model [7] spawned an interest in dense word repre- cess [17]. sentation in a low-dimensional space, and there are now a consider- 1 able number of “2vec” models beyond the word level. One recent Scholia at https://tools.wmflabs.org/scholia/ is avenue of research in the “2vec” domain uses knowledge graphs our web service that presents scholarly profiles based on data in [13]. -
Communications Wikidata: from “An” Identifier to “The” Identifier Theo Van Veen
Communications Wikidata: From “an” Identifier to “the” Identifier Theo van Veen ABSTRACT Library catalogues may be connected to the linked data cloud through various types of thesauri. For name authority thesauri in particular I would like to suggest a fundamental break with the current distributed linked data paradigm: to make a transition from a multitude of different identifiers to using a single, universal identifier for all relevant named entities, in the form of the Wikidata identifier. Wikidata (https://wikidata.org) seems to be evolving into a major authority hub that is lowering barriers to access the web of data for everyone. Using the Wikidata identifier of notable entities as a common identifier for connecting resources has significant benefits compared to traversing the ever-growing linked data cloud. When the use of Wikidata reaches a critical mass, for some institutions, Wikidata could even serve as an authority control mechanism. INTRODUCTION Library catalogs, at national as well as institutional levels, make use of thesauri for authority control of named entities, such as persons, locations, and events. Authority records in thesauri contain information to distinguish between entities with the same name, combine pseudonyms and name variants for a single entity, and offer additional contextual information. Links to a thesaurus from within a catalog often take the form of an authority control number, and serve as identifiers for an entity within the scope of the catalog. Authority records in a catalog can be part of the linked data cloud when including links to thesauri such as VIAF (https://viaf.org/), ISNI (http://www.isni.org/), or ORCID (https://orcid.org/). -
Introduction to Wikidata Linked Data Working Group – 2018-10-01 Steve Baskauf Dbpedia 2007 Community Effort to Extract Structured Data from Wikipedia
Introduction to Wikidata Linked Data Working Group – 2018-10-01 Steve Baskauf DBpedia 2007 Community effort to extract structured data from Wikipedia Wikidata 2012 Wikimedia Foundation effort used to build Wikipedia itself. Originated from Freebase Freebase 2007 Developed by commercial company Metaweb, purchased by Google What is Wikidata? • It is the source of structured data in Wikipedia • It is editable by users and is not subject to as strict notability requirements as Wikipedia • Its data can be searched via SPARQL (at https://query.wikidata.org/) and retrieved programmatically by machines Wikidata is a knowledge graph Graph representation of metadata about one person. See https://www.wikidata.org/wiki/Q40670042 "Steven J. Baskauf"@en skos:prefLabel wd:Q5 (human) wdt:P31 (instance of) <http://www.wikidata.org/entity/Q40670042> wd:Q40670042 (Steven J. Baskauf) wd:Q17501985 (Steven) wdt:P735 (given name) wdt:P496 (ORCID iD) "0000-0003-4365-3135" <http://www.wikidata.org/prop/direct-normalized/P496> wdtn:P496 (ORCID iD) <https://orcid.org/0000-0003-4365-3135> Important features of Wikidata • Wikidata has an extremely controlled interface for adding data -> data are very consistently modelled ("A balance must be found between expressive power and complexity/usability." *) • Wikidata is designed to be language-independent -> opaque identifiers and multilingual labels for everything (language-tagged strings) • The Wikidata data model is abstract. It is NOT defined in terms of RDF, but it can be expressed as RDF. This allows search using SPARQL. • All things in Wikidata are "items". Items are things you might write a Wikipedia article about. Wikidata does not distinguish between classes and instances of classes. -
Learning a Large Scale of Ontology from Japanese Wikipedia
2010 IEEE/WIC/ACM International Conference on Web Intelligence and Intelligent Agent Technology Learning a Large Scale of Ontology from Japanese Wikipedia Susumu Tamagawa∗, Shinya Sakurai∗, Takuya Tejima∗, Takeshi Morita∗, Noriaki Izumiy and Takahira Yamaguchi∗ ∗Keio University 3-14-1 Hiyoshi, Kohoku-ku, Yokohama-shi, Kanagawa 223-8522, Japan Email: fs tamagawa, [email protected] yNational Institute of Advanced Industrial Science and Technology 2-3-26 Aomi, Koto-ku, Tokyo 135-0064, Japan Abstract— Here is discussed how to learn a large scale II. RELATED WORK of ontology from Japanese Wikipedia. The learned on- tology includes the following properties: rdfs:subClassOf Auer et al.’s DBpedia [4] constructed a large information (IS-A relationships), rdf:type (class-instance relationships), database to extract RDF from semi-structured information owl:Object/DatatypeProperty (Infobox triples), rdfs:domain resources of Wikipedia. They used information resource (property domains), and skos:altLabel (synonyms). Experimen- tal case studies show us that the learned Japanese Wikipedia such as infobox, external link, categories the article belongs Ontology goes better than already existing general linguistic to. However, properties and classes are built manually and ontologies, such as EDR and Japanese WordNet, from the there are only 170 classes in the DBpedia. points of building costs and structure information richness. Fabian et al. [5] proposed YAGO which enhanced Word- Net by using the Conceptual Category. Conceptual Category is a category of Wikipedia English whose head part has the form of plural noun like “ American singers of German I. INTRODUCTION origin ”. They defined a Conceptual Category as a class, and defined the articles which belong to the Conceptual It is useful to build up large-scale ontologies for informa- Category as instances of the class. -
Conversational Question Answering Using a Shift of Context
Conversational Question Answering Using a Shift of Context Nadine Steinmetz Bhavya Senthil-Kumar∗ Kai-Uwe Sattler TU Ilmenau TU Ilmenau TU Ilmenau Germany Germany Germany [email protected] [email protected] [email protected] ABSTRACT requires context information. For humans, this also works when Recent developments in conversational AI and Speech recogni- the context is slightly changing to a different topic but having tion have seen an explosion of conversational systems such as minor touch points to the previous course of conversation. With Google Assistant and Amazon Alexa which can perform a wide this paper, we present an approach that is able to handle slight range of tasks such as providing weather information, making ap- shifts of context. Instead of maintaining within a certain node pointments etc. and can be accessed from smart phones or smart distance within the knowledge graph, we analyze each follow-up speakers. Chatbots are also widely used in industry for answering question for context shifts. employee FAQs or for providing call center support. Question The remainder of the paper is organized as follows. In Sect. 2 Answering over Linked Data (QALD) is a field that has been we discuss related approaches from QA and conversational QA. intensively researched in the recent years and QA systems have The processing of questions and the resolving of context is de- been successful in implementing a natural language interface to scribed in Sect. 3. We have implemented this approach in a pro- DBpedia. However, these systems expect users to phrase their totype which is evaluated using a newly created benchmark. -
Gateway Into Linked Data: Breaking Silos with Wikidata
Gateway into Linked Data: Breaking Silos with Wikidata PCC Wikidata Pilot Project @ Texas State University Mary Aycock, Database and Metadata Management Librarian Nicole Critchley, Assistant Archivist, University Archives Amanda Scott, Wittliff Collections Cataloging Assistant https://digital.library.txstate.edu/handle/10877/13529 It started about 10 years ago... ● Linked data started exploding as a topic at library conferences ● Data that was also machine-readable ● Excitement was in the air about the possibility of breaking our databases (catalog, repositories) out of their library-based silos If you learn it, will they build it? ● Libraries bought ILS vendors’ (often pricey) packages offering to convert MARC records to linked data, but results seemed underwhelming ● Many of us channeled our motivation into learning linked data concepts: RDF, Triples, SPARQL ● However, technical infrastructure and abundance of human resources not available to “use it” in most libraries Aimed at the Perplexed Librarian “Yet despite this activity, linked data has become something of a punchline in the GLAM community. For some, linked data is one of this era’s hottest technology buzzwords; but others see it as vaporware, a much-hyped project that will ultimately never come to fruition. The former may be true, but the latter certainly is not.” Carlson, S., Lampert, C., Melvin, D., & Washington, A. (2020). Linked Data for the Perplexed Librarian. ALA Editions. And then a few years ago, the word became… What does it have to offer? ● Stability: Created in 2012 as part of the Wikimedia Foundation ● 2014: Google retired Freebase in favor of Wikidata ● Ready-made infrastructure (Wikibase) & ontology ● Shared values of knowledge creation ● Easy to use graphical interface ● Hub of identifiers If they build it, you can learn..