Tag Archives: Extraction

Better late than never – GSOC 2019 recap & outlook GSoC 2020

  • Pinky: Gee, Brain, what are we gonna do this year?
  • Brain: The same thing we do every year, Pinky. Taking over GSoC.

And, this is exactly what we did. We had been accepted as one of 206 open source organizations to participate in Google Summer of Code (GSoC) again. More than 25 students followed our call for project ideas. In the end, we chose six amazing students and their project proposals to work with during summer 2019. 
In the following post, we will show you some insights into the project ideas and how they turned out. Additionally, we will shed some light onto our amazing team of mentors who devoted a lot of time and expertise in mentoring our students. 

Meet the students and their projects

A Neural QA Model for DBpedia by Anand Panchbhai

With booming amount of information being continuously added to the internet, organising the facts and serving this information to the users becomes a very difficult task. Currently, DBpedia hosts billions of data points and corresponding relations in the RDF format. Accessing data on DBpedia via a SPARQL query is difficult for amateur users, who do not know how to write a query. This project tried to make this humongous linked data available to a larger user base in their natural languages (now restricted to English). The primary objective of the project was to translate natural language questions to a valid SPARQL query. Click here if you want to check his final code.

Multilingual Neural RDF Verbalizer for DBpedia by Dwaraknath Gnaneshwar

Presently, the generation of Natural Language from RDF data has gained substantial attention and has also been proven to support the creation of Natural Language Generation benchmarks. However, most models are aimed at generating coherent sentences in English, while other languages have enjoyed comparatively less attention from researchers. RDF data is usually in the form of triples, <subject, predicate, object>. Subject denotes the resource, the predicate denotes traits or aspects of the resource and expresses the relationship between subject and object. In this project, we aimed to create a multilingual Neural Verbalizer, ie, generating high-quality natural-language text from sets of RDF triples in multiple languages using one stand-alone, end-to-end trainable model. You can follow up on the progress and outcome of the project here. 

Predicate Detection using Word Embeddings for Question Answering over Linked Data by Yajing Bian

Knowledge-based question-answering system (KBQA) has demonstrated an ability to generate answers to natural language from information stored in a large-scale knowledge base. Generally, it completes the analysis challenge via three steps: identifying named entities, detecting predicates and generate SPARQL queries. In these three steps, predicate detection identifies the KB relation(s) a question refers to. To build a predicate detection structure, we identified all possible named entity first, then collected all predicates corresponding to the above entities. What follows is to calculate the similarity between problem and candidate predicates using a multi-granularity neural network model (MGNN). To find the globally optimal entity-predicate assignment, we use a joint model which is based on the result of entity linking and predicate detection process rather than considering the local predictions (i.e. most possible entity or predicate) as the final result. More details on the project are available here

A tool to generate RDF triples from DBpedia abstract by  Jayakrishna Sahit

The main aim of this project was to research and develop a tool in order to generate highly trustable RDF triples from DBpedia abstracts. In order to develop such a tool, we implemented algorithms which would take the output generated from the syntactic analyzer along with DBpedia spotlight’s named entity identifiers. Further information and the project’s results can be found here

A transformer of Attention Mechanism for Long-context QA by Stuart Chan

In this GSoC project, I choose to employ the language model of the transformer with an attention mechanism to automatically discover query templates for the neural question-answering knowledge-based model. The ultimate goal was to train the attention-based NSpM model on DBpedia with its evaluation against the QALD benchmark. Check here for more details on the project.

Workflow for linking External datasets by Jaydeep Chakraborty

The requirement of the project was to create a workflow for entity linking between DBpedia and external data sets. We aimed at an approach for ontology alignment through the use of an unsupervised mixed neural network. We explored reading and parsing the ontology and extracted all necessary information about concepts and instances. Additionally, we generated semantic vectors for each entity with different meta information like entity hierarchy, object property, data property, and restrictions and designed a User Interface based system which showed all necessary information about the workflow. Further info, download details and project results are available here

Meet our Mentors

First of all, a big shout out and thank you to all mentors and co-mentors who helped our students to succeed in their endeavours.

  • Aman Mehta, former GSoC student and current junior mentor, recently interned as a software engineer at Facebook, London.
  • Beyza Yaman, a senior mentor and organizational admin, Post-Doctoral Researcher based in ADAPT, Dublin City University, former Springer Nature-DBpedia intern and former research associate at the InfAI/University of Leipzig. She is responsible for the Turkish DBpedia and her field of interests are information retrieval, data extraction and integration over Linked Data.
  • Tommaso Soru, senior mentor and organizational admin. I’m a Machine Learning & AI enthusiast, Data Scientist at Data Lens Ltd in London and a PhD candidate at the University of Leipzig. 

“DBpedia is my window to the world of semantic data, not only for its intuitive interface but also because its knowledge is organised in a simple and uncomplicated way”

Tommaso Soru, GSoC 2019
  • Amandeep Srivastava, Junior Mentor and analyst at Goldman Sachs. He’s a huge fan of Christopher Nolan and likes to read fiction books in his free time.
  • Diego Moussalem, Senior mentor, Senior Researcher at Paderborn University, an active and vital member of the Portuguese DBpedia Chapter
  • Luca Virgili, currently a Computer Science PhD student at the Polytechnic University of Marche.He was a GSoC student for a year and a GSoC mentor for 2 years in DBpedia. 
  • Bharat Suri, former GSOC student, Junior Mentor, Masters degree in Computer Science at The Ohio State University

“I have thoroughly enjoyed both my years of GSoC with DBpedia and I plan to stay and help out in whichever way I can”

Bharat Suri, GSoC 2019
  • Mariano Rico, senior mentor,  Senior Doctor Researcher at Ontology Engineering Group, Universidad Politécnica de Madrid.
  • Nausheen Fatma, senior mentor, Data Scientist, Natural Language Processing, Machine Learning at Info Edge (naukri.com).
  • Ram G Athreya long-term GSoC mentor, Research Engineer at Viv Labs, Bay Area, San Francisco. 
  • Ricardo Usbeck, team leader ‘Conversational AI and Knowledge Graphs’ at Fraunhofer IAIS.
  • Rricha Jalota, former GSoC students, current senior mentor, developer in the Data Science Group at University of Paderborn, Germany 

“The reason why I love collaborating with DBpedia (apart from the fact that, it’s a powerhouse of knowledge-driven applications) is not only it gave me my first big break to the amazing field of NLP but also to the world of open-source!”

Rricha Jalota, GSoC 2019

In addition, we also like to thank the rest of our mentor team namely, Thiago Castro Ferreira, Aashay Singhal and Krishanu Konar, former GSoC student and current senior mentor, for their great work.  

Mentor Summit Recap 

This GSoC marked the 15th consecutive year of the program and was the 8th season in a row for DBpedia. As usual in each year we had two of our mentors, Rricha Jalota and Aashay Singhal joining the annual GSoC mentor summit. Selected mentors get the chance to meet each other and engage in a vital knowledge and expertise exchange around various GSoC related and non-related topics. Apart from more entertaining activities such as games, a scavenger hunt and a guided trip through Munich mentors also discussed pressing questions such as “why is it important to fail your students” or “how can we have our GSoC students stay and contribute for long”.

After GSoC is before the next GSoC

If you are interested in either mentoring a DBpedia GSoC project or if you want to contribute to a project of your own we are happy to have you on board. There are a few things to get you started.

Likewise, if you are an ambitious student who is interested in open source development and working with DBpedia you are more than welcome to either contribute your own project idea or apply for project ideas we offer starting in early 2020.

Stay tuned, frequently check Twitter or the DBpedia Forum to stay in touch and don’t miss your chance of becoming a crucial force in this endeavour as well as a vital member of the DBpedia community.

See you soon,

yours

DBpedia Association

The Release Circle – A Glimpse behind the Scenes

As you already know, with the new DBpedia strategy our mode of publishing releases changed.  The new DBpedia release process follows a three-step approach starting from the Extraction to ID-Management towards the Fusion, which finalizes the release process.  Our DBpedia releases are currently published on a monthly basis. In this post, we give you insight into the single steps of the release process and into what our developers actually do when preparing a DBpedia release.

Extraction  – Step one of the Release

The good news is, our new release mode is taking shape and noticeable picked up speed. Finally the 2018-08 and, additionally the 2018.09.12 and the 2018.10.16 Releases are now available in our LTS repository.

The 2018-08 Release was generated on the basis of the Wikipedia datasets extracted in early August and currently comprises 136 languages. The extraction release contains the raw extracted data generated by the DBpedia extraction-framework. The post-processing steps, such as data-deduplication or URI-normalization are omitted and moved to later parts of the release process. Thus, we can provide direct, transparent access to the generated data in every step. Until we manage two releases per month, our data is mostly based on the second Wikipedia datasets of the previous month. In line with that, the 2018.09.12 release is based on late August data and the recent 2018.10.16 Release is based on Wikipedia datasets extracted on September 20th. They all comprise 136 languages and contain a stable list of datasets since the 2018-08 release.

Our releases are now ready for parsing and external use. Additionally, there will be a new Wikidata-based release this week.

ID-Management – Step two of the Release

For a complete “new DBpedia” release the DBpedia ID-Management and Fusion of the data have to be added to the process. The Databus ID Management is a process to unify various different IRIs identifying the same entities coined from different data providers. Taking datasets with overlapping domains of interest from multiple data providers, the set of IRIs denoting the entities in the source datasets are determined heuristically (e.g. excluding RDF/OWL types/classes).

Afterwards, these selected IRIs a numeric primary key, the ‘Singleton ID’. The core of the ID Management process happens in the next step: Based on the large set of owl:sameAs assertions in the input data with high confidence, the connected components induced from the corresponding sameAs-graph is computed. In other words: The groups of all entities from the input datasets (transitively) reachable from one to another are determined. We dubbed these groups the sameAs-clusters. For each sameAs-cluster we pick one member as representant, which determines the ‘Cluster ID’ or ‘Global Identifier’ for all cluster members.

Apart from being an essential preparatory step for the Fusion, these Global Identifiers serve purpose in their own right as unified Linked Data identifiers for groups of Linked Data entities that should be viewed as equivalent or ‘the same thing’.

A processing workflow based on Apache Spark to perform the process described on above for large quantities of RDF input data is already in place and has been run successfully for a large set of DBpedia inputs consisting of:

 

Fusion – Step three of the Release

Based on the extraction and the ID-Management, the Data Fusion finalizes the last step of the  DBpedia release cycle. With the goal of improving data quality and data coverage, the process uses the DBpedia global IRI clusters to fuse and enrich the source datasets. The fused data contains all resource of the input datasets. The fusion process is based on a functional property decision to decide the number of selected values ( owl:FunctionalProperty determination ). Further, the value selection for this functional properties is based on a preference dependent on the originated source dataset. For example, preferred values for En-DBpedia over DE-DBpedia.

The enrichment improves entity-properties and -values coverage for resources only contained in the source data. Furthermore, we create provenance data to keep track of the origin of each triple. This provenance data is also used for the http-based http://global.dbpedia.org resource view.

At the moment the fused and enriched data is available for the generic, and mapping-based extractions. More datasets are still in progress.  The DBpedia-fusion data is uploading to http://downloads.dbpedia.org/repo/dev/fusion/

Please note we are still in the midst of the beta testing for our data release tool, so in case you do come across any errors, reporting them to us is much appreciated to fuel the testing process.

Further information regarding the releases progress can be found here: http://dev.dbpedia.org/

Next steps

We will add more releases to the repository on a monthly basis aiming for a bi-weekly release mode as soon as possible. In between the intervals, any mistakes or errors you find and report in this data can be fixed for the upcoming release. Currently, the generated metadata in the DataID-file is not stable. This will fluctuate, still needs to be improved and will change in the near future.  We are now working on the next release and will inform you as soon as it is published.

Yours DBpedia Association

This blog post was written with the help of our DBpedia developers Robert Bielinski, Markus Ackermann and Marvin Hofer who were responsible for the work done with respect to the DBpedia releases. We like to thank them for their great work.