Jäger, Joscha

Sack, Harald

Tietz, Tabea

Waitelonis, Jörg

KITopen

refer: a Linked Data based Text Annotation andRecommender System for WordpressTabea Tietz1, Jörg Waitelonis1, Joscha Jäger2, and Harald Sack11 Hasso-Plattner-Intitute, Prof.-Dr.-Helmert-Str. 2-3, 14482 Potsdam, Germanyfirstname.lastname@hpi.de,2 yovisto GmbH, August-Bebel-Str. 25-53, 14482 Potsdam, Germanyfirstname@yovisto.com1 IntroductionWhen searching for an arbitrary subject in weblogs or archives, users often don’tget the information they are really looking for, because they are overwhelmedwith an overflow of information while sometimes the presented information is tooscarce to make any use of it. Without further knowledge about the context orbackground of the intended subject users are easily frustrated because they eithercannot handle the amount of information or they might give up because theycannot make sense of the topic at all. Furthermore, authors of online-platformsoften deal with the issue to provide useful recommendations of other articles andto motivate the readers to explore more of the available but often hidden contentof their blog or archive.In the demo presentation, we present refer, a semantic annotation and visu-alization system integrated into the Wordpress platform. refer enables contentcreators to (semi-)automatically annotate their texts with DBpedia resources aspart of the original writing process and visualize them automatically. With referusers are encouraged to take an active part in discovering a platform’s informa-tion content interactively. They can discover background information as well asrelationships among persons, places, events, and anything related to the subjectin current focus and are inspired to navigate the previously hidden informationon a platform.Related systems include Pundit [2] and WYSIWYM [1]. The original conceptof the presented user interface and a first prototype have already been presentedin [4].2 refer Componentsrefer consists of the following tools and visualizations, integrated into Wordpress(cf. Fig. 1). An Annotator is implemented as an extension of the Wordpresstext editing interface to create semantic text annotations based on DBpedia. Tocreate annotations, the author selects a text fraction in the Wordpress editingenvironment. The author can further choose between manual and automated an-notation by means of automated Named Entity Linking (NEL). For automatedannotation, refer deploys KEA-NEL [6], which implements entity linking withDBpedia entities [5]. Nevertheless, there are still errors that have to be revisedmanually. The user has to be supported in selecting the correct entity from thelarge knowledge-base which is a challenge especially for highly ambiguous tex-tual mentions. Further, utilities are applied to rank and organize the candidatelists according to e. g. string similarity with the entity mention, or popularity ofthe entity [3] to increase the users’ annotation performance. The refer Annotatorprovides two configurable user interface modes: (1) The Modal Annotator istriggered via a button in the TinyMCE control panel, whereby the suggestedentities are displayed in a modal dialog window (cf. Fig. 1.1) in a table-basedlayout. Entities are divided into the four categories Person (green), Place (blue),Event (yellow) and Thing (purple). The text abstract and entity IRI is displayedon mouseover. The selected annotation is encoded in RDFa markup, which isadded to the according text fragment. (2) The Inline Annotator (cf. Fig. 1.2)is triggered automatically via highlighting a text fragment and opens a circu-lar category menu which allows the user to instantly show suggestions fromthe respective category (Person, Place, Event, Thing). By selecting a category,the suggested entities are displayed. In order to provide more context withinthe relatively small space, these entities are divided into dynamically retrievedsub-categories. While the Inline Annotator provides fast and simple means ofsemantic text annotation without losing the context of the written text, theModal Annotator provides a larger overview and enables a better comparison ofentities.Infoboxes are used to visualize annotations in the article view, and anno-tated entities are indicated directly in the article text. On mouseover, an infoboxas in Fig. 1.3 is shown right below the annotated text fragment, which containsbasic information about the entity, e.g. a label, a thumbnail, and additional datain a table-layout. The infobox allows the user to gather basic facts about an en-tity and relations to other entities. While some basic information can be derivedjust from the webpage‘s RDFa microdata and is displayed instantly, additionalcontent is asynchronously loaded from the web service. When the text fragmentor any of the infobox entities are clicked, the Relation Browser slides down fromthe top of the page with the selected entity in focus.The Relation Browser and Recommender visualizes relationships be-tween annotations as well as suggestions for further reading. It allows users tonavigate and explore relations among entities. It can be opened by the user eithervia click on the refer icon bar on top of the page or by selecting an entity of inter-est in the article text, (e.g. Jules Verne). The entity Jules Verne then becomesthe focus-entity. Its DBpedia abstract and image are displayed on the bottom ofthe Relation Browser and its background color depends on its category (Person,Place, Event, Thing). Based on the focus-entity, related entities (derived fromDBpedia and all annotations available on the platform) are displayed in a fourcolumn grid. The pagination bars on the right hand side of each column allowthe users to browse through all entities in a category. When hovering one of thedisplayed entities (e.g. Jacques Cousteau in Fig. 1.4), relations to the focus-entity2and to further entities in the grid-view (e.g. Oceanography) are visualized by lineconnectors. A label (property) indicates the direction and type of connection.If there are more entity-relations than displayed in the first overview, connec-tions to hidden entities are indicated by dotted lines, which can be activatedvia hovering a small ’plus’ icon inside the entity box. A click on an entity inthe grid-view replaces the focus-entity with the selected item and refreshes therelated entities in all categories. A ranked list of recommended blogposts for theentity in focus is displayed on the bottom-left. The recommendations compriseblogposts that cover the focus-entity and entities related to the focus entity. Themore entities are related with the entity in focus, the higher is the rank of therecommended article in the list.3 SummaryWe have presented refer as a tool to help blog authors to enhance their contentwith the help of Linked Open Data and to engage the readers to further explorethe available content as well as to provide background information from DBpediaand Wikipedia. A proof-of-concept is provided as being integrated into a dailyweblog3. The Wordpress-plugin of refer is publicly available for download4, anda screencast5 demonstrates the annotator interfaces.Acknowledgement: This work has been funded by the German Government,Federal Ministry of Education an Research under project number 03WKCJ4D.References1. A. Khalili and S. Auer. WYSIWYM – integrated visualization, exploration andauthoring of semantically enriched unstructured content. Semantic Web Journal, 1,2014.2. C. Morbidoni and A. Piccioli. The Semantic Web: ESWC 2015 Satellite Events,chapter Curating a Document Collection via Crowdsourcing with Pundit 2.0, pages102–106. Springer, 2015.3. J. Osterhoff, J. Waitelonis, and H. Sack. Widen the Peepholes! Entity-Based Auto-Suggestion as a rich and yet immediate Starting Point for Exploratory Search. InProc. of 2nd Workshop Interaction and Visualization in the Web of Data (IVDW2012), 2012.4. T. Tietz, J. Waitelonis, J. Jäger, and H. Sack. Smart media navigator: Visualizingrecommendations based on linked data. In 13th Int. Semantic Web Conf., 2014.5. R. Usbeck et al. Gerbil: General entity annotator benchmarking framework. InProc. of the 24th Int. Conf. on World Wide Web, pages 1133–1143. ACM, 2015.6. J. Waitelonis and H. Sack. Named entity linking in #tweets with kea. In 6thworkshop on ’Making Sense of Microposts’, Named Entity Recognition and Linking(NEEL) Challenge, at the 25th Int. World Wide Web Conf., 2016.3 http://blog.yovisto.com4 http://refer.cx/5 http://s16a.org/sites/default/files/refer/refer_Screencast.mov3Fig. 1. refer components: Modal Annotator (1), Inline Annotator (2), Infobox (3),Relation Browser and Recommender (4)4

refer: a Linked Data based Text Annotation and Recommender System for Wordpress

https://publikationen.bibliothek.kit.edu/1000092955/26839377

refer: a Linked Data based Text Annotation and Recommender System for Wordpress

Abstract

Similar works

Full text

Available Versions

KITopen