Wikipedia Infoboxes Research Articles

Hierarchical Encoder with Auxiliary Supervision for Neural Table-to-Text Generation: Learning Better Representation for Tables

Generating natural language descriptions for the structured tables which consist of multiple attribute-value tuples is a convenient way to help people to understand the tables. Most neural table-to-text models are based on the encoder-decoder framework. However, it is hard for a vanilla encoder to learn the accurate semantic representation of a complex table. The challenges are two-fold: firstly, the table-to-text datasets often contain large number of attributes across different domains, thus it is hard for the encoder to incorporate these heterogeneous resources. Secondly, the single encoder also has difficulties in modeling the complex attribute-value structure of the tables. To this end, we first propose a two-level hierarchical encoder with coarse-to-fine attention to handle the attribute-value structure of the tables. Furthermore, to capture the accurate semantic representations of the tables, we propose 3 joint tasks apart from the prime encoder-decoder learning, namely auxiliary sequence labeling task, text autoencoder and multi-labeling classification, as the auxiliary supervisions for the table encoder. We test our models on the widely used dataset WIKIBIO which contains Wikipedia infoboxes and related descriptions. The dataset contains complex tables as well as large number of attributes across different domains. We achieve the state-of-the-art performance on both automatic and human evaluation metrics.

Using Big Data Technique for Building Edit Alert System for Wikipedia Infoboxes Based on Map-Reduce Method

Using Big Data Technique for Building Edit Alert System for Wikipedia Infoboxes Based on Map-Reduce Method

Data Change Exploration Using Time Series Clustering

Analysis of static data is one of the best studied research areas. However, data changes over time. These changes may reveal patterns or groups of similar values, properties, and entities. We study changes in large, publicly available data repositories by modelling them as time series and clustering these series by their similarity. In order to perform change exploration on real-world data we use the publicly available revision data of Wikipedia Infoboxes and weekly snapshots of IMDB. The changes to the data are captured as events, which we call change records. In order to extract temporal behavior we count changes in time periods and propose a general transformation framework that aggregates groups of changes to numerical time series of different resolutions. We use these time series to study different application scenarios of unsupervised clustering. Our explorative results show that changes made to collaboratively edited data sources can help find characteristic behavior, distinguish entities or properties and provide insight into the respective domains.

Using microtasks to crowdsource DBpedia entity classification: A study in workflow design

DBpedia is at the core of the Linked Open Data Cloud and widely used in research and applications. However, it is far from being perfect. Its content suffers from many flaws, as a result of factual errors inherited from Wikipedia or incomplete mappings from Wikipedia infobox to DBpedia ontology. In this work we focus on one class of such problems, un-typed entities. We propose a hierarchical tree-based approach to categorize DBpedia entities according to the DBpedia ontology using human computation and paid microtasks. We analyse the main dimensions of the crowdsourcing exercise in depth in order to come up with suggestions for workflow design and study three different workflows with automatic and hybrid prediction mechanisms to select possible candidates for the most specific category from the DBpedia ontology. To test our approach, we run experiments on CrowdFlower using a gold standard dataset of 120 previously unclassified entities. In our studies human-computation driven approaches generally achieved higher precision at lower cost when compared to workflows with automatic predictors. However, each of the tested workflows has its merit and none of them seems to perform exceptionally well on the entities that the DBpedia Extraction Framework fails to classify. We discuss these findings and their potential implications for the design of effective crowdsourced entity classification in DBpedia and beyond.

User-friendly temporal queries on historical knowledge bases

DBpedia and other RFD-encoded Knowledge Bases (KB)s give users access to encyclopedic knowledge via SPARQL queries. As the world evolves, the KBs are updated, and the history of entities and their properties becomes of great interest. Thus, we need powerful tools and friendly interfaces to query histories and flash-back to the past. Here, we propose (i) a point-based temporal extension of SPARQL, called SPARQLT, which enables simple and concise expression of temporal queries, and (ii) an extension of Wikipedia Infoboxes to support user-friendly by-example temporal queries implemented by mapping them into SPARQLT. Our main-memory RDF-TX system supports such queries efficiently using Multi-Version B+ trees, compressed indexes, and query optimization techniques, which achieve performance and scalability, as demonstrated by experiments on historical datasets including Cliopedia derived from Wikipedia dumps. We finally discuss how provenance information can be used to add valid-time features to these transaction-time KBs.

DBpedia – A large-scale, multilingual knowledge base extracted from Wikipedia

The DBpedia community project extracts structured, multilingual knowledge from Wikipedia and makes it freely available on the Web using Semantic Web and Linked Data technologies. The project extracts knowledge from 111 different language editions of Wikipedia. The largest DBpedia knowledge base which is extracted from the English edition of Wikipedia consists of over 400 million facts that describe 3.7 million things. The DBpedia knowledge bases that are extracted from the other 110 Wikipedia editions together consist of 1.46 billion facts and describe 10 million additional things. The DBpedia project maps Wikipedia infoboxes from 27 different language editions to a single shared ontology consisting of 320 classes and 1,650 properties. The mappings are created via a world-wide crowd-sourcing effort and enable knowledge from the different Wikipedia editions to be combined. The project publishes releases of all DBpedia knowledge bases for download and provides SPARQL query access to 14 out of the 111 language editions via a global network of local DBpedia chapters. In addition to the regular releases, the project maintains a live knowledge base which is updated whenever a page in Wikipedia changes. DBpedia sets 27 million RDF links pointing into over 30 external data sources and thus enables data from these sources to be used together with DBpedia data. Several hundred data sets on the Web publish RDF links pointing to DBpedia themselves and make DBpedia one of the central interlinking hubs in the Linked Open Data (LOD) cloud. In this system report, we give an overview of the DBpedia community project, including its architecture, technical implementation, maintenance, internationalisation, usage statistics and applications.

Text-Mining, Structured Queries, and Knowledge Management on Web Document Corpora

Wikipedia's InfoBoxes play a crucial role in advanced applications and provide the main knowledge source for DBpedia and the powerful structured queries it supports. However, InfoBoxes, which were created by crowdsourcing for human rather than computer consumption, suffer from incompleteness, inconsistencies, and inaccuracies. To overcome these problems, we have developed (i) the IBminer system that extracts InfoBox information by text-mining Wikipedia pages, (ii) the IKBStore system that integrates the information derived by IBminer with that of DBpedia, YAGO2,WikiData,WordNet, and other sources, and (iii) SWiPE and InfoBox Editor (IBE) that provide a user-friendly interfaces for querying and revising the knowledge base. Thus, IBminer uses a deep NLP-based approach to extract from text a semantic representation structure called TextGraph from which the system detects patterns and derives subject-attribute-value relations, as well as domain-specific synonyms for the knowledge base. IKBStore and IBE complement the powerful, user-friendly, by-example structured queries of SWiPE by supporting the validation and provenance history for the information contained in the knowledge base, along with the ability of upgrading its knowledge when this is found incomplete, incorrect, or outdated.

Exploiting entities for query expansion

A substantial fraction of web search queries contain references to entities, such as persons, organizations, and locations. This signi cant presence of named entities in queries provides an opportunity for web search engines to improve their understanding of the user's information need. In this thesis, we investigate the entity-oriented query expansion process. Particularly, we propose two novel and effective query expansion approaches that exploit semantic sources of evidence to devise discriminative term features, and machine learning techniques to effectively combine these features in order to rank candidate expansion terms. As a result, not only do we select effective expansion terms, but we also weigh these terms according to their predicted effectiveness. In addition, since our query expansion approaches consider Wikipedia infoboxes as a source of candidate expansion terms, a frequent obstacle is that only about 20% of Wikipedia articles have an infobox. To overcome this problem we propose WAVE, a self-supervised approach to autonomously generate infoboxes for Wikipedia articles. First, we propose UQEE, an unsupervised entity-oriented query expansion approach, which effectively selects expansion terms using taxonomic features devised by the semantic structure implicitly provided by infobox templates. We show that query expansion using infoboxes presents a better trade-off between retrieval performance and query latency. Moreover, we demonstrate that the automatically generated infoboxes provided by WAVE are as effective as manually generated infoboxes for query expansion. Lastly, we propose L2EE, a learning to rank approach for entity-oriented query expansion, which considers semantic evidence encoded in the content of Wikipedia article fields, and automatically labels training examples proportionally to their observed retrieval effectiveness. Experiments on three TREC web test collections attest the effectiveness of L2EE, with significant gains compared to UQEE and state-of-the-art pseudo-relevance feedback and entity-oriented pseudo-relevance feedback approaches.

Identifying and Extracting Named Entities from Wikipedia Database Using Entity Infoboxes

An approach for named entity classification based on Wikipedia article infoboxes is described in this paper. It identifies the three fundamental named entity types, namely; Person, Location and Organization. An entity classification is accomplished by matching entity attributes extracted from the relevant entity article infobox against core entity attributes built from Wikipedia Infobox Templates. Experimental results showed that the classifier can achieve a high accuracy and F-measure scores of 97%. Based on this approach, a database of around 1.6 million 3-typed named entities is created from 20140203 Wikipedia dump. Experiments on CoNLL2003 shared task named entity recognition (NER) dataset disclosed the system's outstanding performance in comparison to three different state- of-the-art systems.

Automatic Detection of Outdated Information in Wikipedia Infoboxes

An infobox of a Wikipedia article generally contains key facts in the article and is organized as attribute-value pairs. Infoboxes not only allow read- ers to rapidly gather the most important information about some aspects of the articles in which they appear, but also provide a source for many knowledge ba- ses derived from Wikipedia. However, not all the values of infobox attributes are updated frequently and accurately. In this paper, we propose a method to au- tomatically detect outdated attribute values in Wikipedia infoboxes by using facts extracted from the general Web. Our method uses the pattern-based fact extraction approach. The patterns for fact extraction are automatically learned using a number of available seeds in related Wikipedia infoboxes. We have tested and evaluated our system on a set of 100 well-established com- panies in the NASDAQ-100 index on their employee numbers, presented by the num_employees attribute value in their Wikipedia article infoboxes. The achieved accuracy is 77% and our test result also reveals that 82% of the companies do not have their latest numbers of employees in their Wikipedia article infoboxes.