Web-scale profiling of semantic annotations in HTML pages

The vision of the Semantic Web was coined by Tim Berners-Lee almost two decades ago. The idea describes an extension of the existing Web in which “information is given well-defined meaning, better enabling computers and people to work in cooperation” [Berners-Lee et al., 2001]. Semantic annotations in HTML pages are one realization of this vision which was adopted by large numbers of web sites in the last years. Semantic annotations are integrated into the code of HTML pages using one of the three markup languages Microformats, RDFa, or Microdata. Major consumers of semantic annotations are the search engine companies Bing, Google, Yahoo!, and Yandex. They use semantic annotations from crawled web pages to enrich the presentation of search results and to complement their knowledge bases. However, outside the large search engine companies, little is known about the deployment of semantic annotations: How many web sites deploy semantic annotations? What are the topics covered by semantic annotations? How detailed are the annotations? Do web sites use semantic annotations correctly? Are semantic annotations useful for others than the search engine companies? And how can semantic annotations be gathered from the Web in that case? The thesis answers these questions by profiling the web-wide deployment of semantic annotations. The topic is approached in three consecutive steps: In the first step, two approaches for extracting semantic annotations from the Web are discussed. The thesis evaluates first the technique of focused crawling for harvesting semantic annotations. Afterward, a framework to extract semantic annotations from existing web crawl corpora is described. The two extraction approaches are then compared for the purpose of analyzing the deployment of semantic annotations in the Web. In the second step, the thesis analyzes the overall and markup language-specific adoption of semantic annotations. This empirical investigation is based on the largest web corpus that is available to the public. Further, the topics covered by deployed semantic annotations and their evolution over time are analyzed. Subsequent studies examine common errors within semantic annotations. In addition, the thesis analyzes the data overlap of the entities that are described by semantic annotations from the same and across different web sites. The third step narrows the focus of the analysis towards use case-specific issues. Based on the requirements of a marketplace, a news aggregator, and a travel portal the thesis empirically examines the utility of semantic annotations for these use cases. Additional experiments analyze the capability of product-related semantic annotations to be integrated into an existing product categorization schema. Especially, the potential of exploiting the diverse category information given by the web sites providing semantic annotations is evaluated

SemTab 2019: Resources to Benchmark Tabular Data to Knowledge Graph Matching Systems

Tabular data to Knowledge Graph matching is the process of assigning semantic tags from knowledge graphs (e.g., Wikidata or DBpedia) to the elements of a table. This task is a challenging problem for various reasons, including the lack of metadata (e.g., table and column names), the noisiness, heterogeneity, incompleteness and ambiguity in the data. The results of this task provide significant insights about potentially highly valuable tabular data, as recent works have shown, enabling a new family of data analytics and data science applications. Despite significant amount of work on various flavors of this problem, there is a lack of a common framework to conduct a systematic evaluation of state-of-the-art systems. The creation of the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab) aims at filling this gap. In this paper, we report about the datasets, infrastructure and lessons learned from the first edition of the SemTab challenge

Retrieval, crawling and fusion of entity-centric data on the web

While the Web of (entity-centric) data has seen tremendous growth over the past years, take-up and re-use is still limited. Data vary heavily with respect to their scale, quality, coverage or dynamics, what poses challenges for tasks such as entity retrieval or search. This chapter provides an overview of approaches to deal with the increasing heterogeneity of Web data. On the one hand, recommendation, linking, profiling and retrieval can provide efficient means to enable discovery and search of entity-centric data, specifically when dealing with traditional knowledge graphs and linked data. On the other hand, embedded markup such as Microdata and RDFa has emerged a novel, Web-scale source of entitycentric knowledge. While markup has seen increasing adoption over the last few years, driven by initiatives such as schema.org, it constitutes an increasingly important source of entity-centric data on the Web, being in the same order of magnitude as the Web itself with regards to dynamics and scale. To this end, markup data lends itself as a data source for aiding tasks such as knowledge base augmentation, where data fusion techniques are required to address the inherent characteristics of markup data, such as its redundancy, heterogeneity and lack of links. Future directions are concerned with the exploitation of the complementary nature of markup data and traditional knowledge graphs. The final publication is available at Springer via http://dx.doi.org/ 10.1007/978-3-319-53640-8_1

CHORUS Deliverable 2.1: State of the Art on Multimedia Search Engines

Based on the information provided by European projects and national initiatives related to multimedia search as well as domains experts that participated in the CHORUS Think-thanks and workshops, this document reports on the state of the art related to multimedia content search from, a technical, and socio-economic perspective. The technical perspective includes an up to date view on content based indexing and retrieval technologies, multimedia search in the context of mobile devices and peer-to-peer networks, and an overview of current evaluation and benchmark inititiatives to measure the performance of multimedia search engines. From a socio-economic perspective we inventorize the impact and legal consequences of these technical advances and point out future directions of research

CHORUS Deliverable 2.2: Second report - identification of multi-disciplinary key issues for gap analysis toward EU multimedia search engines roadmap

After addressing the state-of-the-art during the first year of Chorus and establishing the existing landscape in multimedia search engines, we have identified and analyzed gaps within European research effort during our second year. In this period we focused on three directions, notably technological issues, user-centred issues and use-cases and socio- economic and legal aspects. These were assessed by two central studies: firstly, a concerted vision of functional breakdown of generic multimedia search engine, and secondly, a representative use-cases descriptions with the related discussion on requirement for technological challenges. Both studies have been carried out in cooperation and consultation with the community at large through EC concertation meetings (multimedia search engines cluster), several meetings with our Think-Tank, presentations in international conferences, and surveys addressed to EU projects coordinators as well as National initiatives coordinators. Based on the obtained feedback we identified two types of gaps, namely core technological gaps that involve research challenges, and “enablers”, which are not necessarily technical research challenges, but have impact on innovation progress. New socio-economic trends are presented as well as emerging legal challenges

Web table integration and profiling for knowledge base augmentation

HTML tables on web pages ("web tables") have been used successfully as a data source for several applications. They can be extracted from web pages on a large-scale, resulting in corpora of millions of web tables. But, until today only little is known about the general distribution of topics and specific types of data that are contained in the tables that can be found on the Web. But this knowledge is essential to understanding the potential application areas and topical coverage of web tables as a data source. Such knowledge can be obtained through the integration of web tables with a knowledge base, which enables the semantic interpretation of their content and allows for their topical profiling. In turn, the knowledge base can be augmented by adding new statements from the web tables. This is challenging, because the data volume and variety are much larger than in traditional data integration scenarios, in which only a small number of data sources is integrated. The contributions of this thesis are methods for the integration of web tables with a knowledge base and the profiling of large-scale web table corpora through the application of these methods. For this profiling, two corpora of 147 million and 233 million web tables, respectively, are created and made publicly available. These corpora are two of only three that are openly available for research on web tables. Their data profile reveals that most web tables have only very few rows, with a median of 6 rows per web table, and between 35% and 52% of all columns contain non-textual values, such as numbers or dates. These two characteristics have been mostly ignored in the literature about web tables and are addressed by the methods presented in this thesis. The first method, T2K Match, is an algorithm for semantic table interpretation that annotates web tables with classes, properties, and entities from a knowledge base. Other than most algorithms for these tasks, it is not limited to the annotation of columns that contain the names of entities. Its application to a large-scale web table corpus results in the most fine-grained topical data profile of web tables at the time of writing, but also reveals that small web tables cannot be processed with high quality. For such small web tables, a method that stitches them into larger tables is presented and shown to drastically improve the quality of the results. The data profile further shows that the majority of the columns in the web tables, where classes and entities can be recognised, have no corresponding properties in the knowledge base. This makes them candidates for new properties that can be added to the knowledge base. The current methods for this task, however, suffer from the oversimplified assumption that web tables only contain binary relations. This results in the extraction of incomplete relations from the web tables as new properties and makes their correct interpretation impossible. To increase the completeness, a method is presented that generates additional data from the context of the web tables and synthesizes n-ary relations from all web tables of a web site. The application of this method to the second large-scale web table corpus shows that web tables contain a large number of n-ary relations. This means that the data contained in web tables is of higher complexity than previously assumed

XML in Motion from Genome to Drug

Information technology (IT) has emerged as a central to the solution of contemporary genomics and drug discovery problems. Researchers involved in genomics, proteomics, transcriptional profiling, high throughput structure determination, and in other sub-disciplines of bioinformatics have direct impact on this IT revolution. As the full genome sequences of many species, data from structural genomics, micro-arrays, and proteomics became available, integration of these data to a common platform require sophisticated bioinformatics tools. Organizing these data into knowledgeable databases and developing appropriate software tools for analyzing the same are going to be major challenges. XML (eXtensible Markup Language) forms the backbone of biological data representation and exchange over the internet, enabling researchers to aggregate data from various heterogeneous data resources. The present article covers a comprehensive idea of the integration of XML on particular type of biological databases mainly dealing with sequence-structure-function relationship and its application towards drug discovery. This e-medical science approach should be applied to other scientific domains and the latest trend in semantic web applications is also highlighted

Reducing the labeling effort for entity resolution using distant supervision and active learning

Entity resolution is the task of identifying records in one or more data sources which refer to the same real-world object. It is often treated as a supervised binary classification task in which a labeled set of matching and non-matching record pairs is used for training a machine learning model. Acquiring labeled data for training machine learning models is expensive and time-consuming, as it typically involves one or more human annotators who need to manually inspect and label the data. It is thus considered a major limitation of supervised entity resolution methods. In this thesis, we research two approaches, relying on distant supervision and active learning, for reducing the labeling effort involved in constructing training sets for entity resolution tasks with different profiling characteristics. Our first approach investigates the utility of semantic annotations found in HTML pages as a source of distant supervision. We profile the adoption growth of semantic annotations over multiple years and focus on product-related schema.org annotations. We develop a pipeline for cleansing and grouping semantically annotated offers describing the same products, thus creating the WDC Product Corpus, the largest publicly available training set for entity resolution. The high predictive performance of entity resolution models trained on offer pairs from the WDC Product Corpus clearly demonstrates the usefulness of semantic annotations as distant supervision for product-related entity resolution tasks. Our second approach focuses on active learning techniques, which have been widely used for reducing the labeling effort for entity resolution in related work. Yet, we identify two research gaps: the inefficient initialization of active learning and the lack of active learning methods tailored to multi-source entity resolution. We address the first research gap by developing an unsupervised method for initializing and further assisting the complete active learning workflow. Compared to active learning baselines that use random sampling or transfer learning for initialization, our method guarantees high anytime performance within a limited labeling budget for tasks with different profiling characteristics. We address the second research gap by developing ALMSER, the first active learning method which uses signals inherent to multi-source entity resolution tasks for query selection and model training. Our evaluation results indicate that exploiting such signals for query selection alone has a varying effect on model performance across different multi-source entity resolution tasks. We further investigate this finding by analyzing the impact of the profiling characteristics of multi-source entity resolution tasks on the performance of active learning methods which use different signals for query selection

Fuzzy rule based profiling approach for enterprise information seeking and retrieval

With the exponential growth of information available on the Internet and various organisational intranets there is a need for profile based information seeking and retrieval (IS&R) systems. These systems should be able to support users with their context-aware information needs. This paper presents a new approach for enterprise IS&R systems using fuzzy logic to develop task, user and document profiles to model user information seeking behaviour. Relevance feedback was captured from real users engaged in IS&R tasks. The feedback was used to develop a linear regression model for predicting document relevancy based on implicit relevance indicators. Fuzzy relevance profiles were created using Term Frequency and Inverse Document Frequency (TF/IDF) analysis for the successful user queries. Fuzzy rule based summarisation was used to integrate the three profiles into a unified index reflecting the semantic weight of the query terms related to the task, user and document. The unified index was used to select the most relevant documents and experts related to the query topic. The overall performance of the system was evaluated based on standard precision and recall metrics which show significant improvements in retrieving relevant documents in response to user queries