Open Information Extraction: A Review of Baseline Techniques, Approaches, and Applications

With the abundant amount of available online and offline text data, there arises a crucial need to extract the relation between phrases and summarize the main content of each document in a few words. For this purpose, there have been many studies recently in Open Information Extraction (OIE). OIE improves upon relation extraction techniques by analyzing relations across different domains and avoids requiring hand-labeling pre-specified relations in sentences. This paper surveys recent approaches of OIE and its applications on Knowledge Graph (KG), text summarization, and Question Answering (QA). Moreover, the paper describes OIE basis methods in relation extraction. It briefly discusses the main approaches and the pros and cons of each method. Finally, it gives an overview about challenges, open issues, and future work opportunities for OIE, relation extraction, and OIE applications.Comment: 15 pages, 9 figure

Entities with quantities : extraction, search, and ranking

Quantities are more than numeric values. They denote measures of the world’s entities such as heights of buildings, running times of athletes, energy efficiency of car models or energy production of power plants, all expressed in numbers with associated units. Entity-centric search and question answering (QA) are well supported by modern search engines. However, they do not work well when the queries involve quantity filters, such as searching for athletes who ran 200m under 20 seconds or companies with quarterly revenue above $2 Billion. State-of-the-art systems fail to understand the quantities, including the condition (less than, above, etc.), the unit of interest (seconds, dollar, etc.), and the context of the quantity (200m race, quarterly revenue, etc.). QA systems based on structured knowledge bases (KBs) also fail as quantities are poorly covered by state-of-the-art KBs. In this dissertation, we developed new methods to advance the state-of-the-art on quantity knowledge extraction and search.Zahlen sind mehr als nur numerische Werte. Sie beschreiben Maße von Entitäten wie die Höhe von Gebäuden, die Laufzeit von Sportlern, die Energieeffizienz von Automodellen oder die Energieerzeugung von Kraftwerken - jeweils ausgedrückt durch Zahlen mit zugehörigen Einheiten. Entitätszentriete Anfragen und direktes Question-Answering werden von Suchmaschinen häufig gut unterstützt. Sie funktionieren jedoch nicht gut, wenn die Fragen Zahlenfilter beinhalten, wie z. B. die Suche nach Sportlern, die 200m unter 20 Sekunden gelaufen sind, oder nach Unternehmen mit einem Quartalsumsatz von über 2 Milliarden US-Dollar. Selbst moderne Systeme schaffen es nicht, Quantitäten, einschließlich der genannten Bedingungen (weniger als, über, etc.), der Maßeinheiten (Sekunden, Dollar, etc.) und des Kontexts (200-Meter-Rennen, Quartalsumsatz usw.), zu verstehen. Auch QA-Systeme, die auf strukturierten Wissensbanken (“Knowledge Bases”, KBs) aufgebaut sind, versagen, da quantitative Eigenschaften von modernen KBs kaum erfasst werden. In dieser Dissertation werden neue Methoden entwickelt, um den Stand der Technik zur Wissensextraktion und -suche von Quantitäten voranzutreiben. Unsere Hauptbeiträge sind die folgenden: • Zunächst präsentieren wir Qsearch [Ho et al., 2019, Ho et al., 2020] – ein System, das mit erweiterten Fragen mit Quantitätsfiltern umgehen kann, indem es Hinweise verwendet, die sowohl in der Frage als auch in den Textquellen vorhanden sind. Qsearch umfasst zwei Hauptbeiträge. Der erste Beitrag ist ein tiefes neuronales Netzwerkmodell, das für die Extraktion quantitätszentrierter Tupel aus Textquellen entwickelt wurde. Der zweite Beitrag ist ein neuartiges Query-Matching-Modell zum Finden und zur Reihung passender Tupel. • Zweitens, um beim Vorgang heterogene Tabellen einzubinden, stellen wir QuTE [Ho et al., 2021a, Ho et al., 2021b] vor – ein System zum Extrahieren von Quantitätsinformationen aus Webquellen, insbesondere Ad-hoc Webtabellen in HTML-Seiten. Der Beitrag von QuTE umfasst eine Methode zur Verknüpfung von Quantitäts- und Entitätsspalten, für die externe Textquellen genutzt werden. Zur Beantwortung von Fragen kontextualisieren wir die extrahierten Entitäts-Quantitäts-Paare mit informativen Hinweisen aus der Tabelle und stellen eine neue Methode zur Konsolidierung und verbesserteer Reihung von Antwortkandidaten durch Inter-Fakten-Konsistenz vor. • Drittens stellen wir QL [Ho et al., 2022] vor – eine Recall-orientierte Methode zur Anreicherung von Knowledge Bases (KBs) mit quantitativen Fakten. Moderne KBs wie Wikidata oder YAGO decken viele Entitäten und ihre relevanten Informationen ab, übersehen aber oft wichtige quantitative Eigenschaften. QL ist frage-gesteuert und basiert auf iterativem Lernen mit zwei Hauptbeiträgen, um die KB-Abdeckung zu verbessern. Der erste Beitrag ist eine Methode zur Expansion von Fragen, um einen größeren Pool an Faktenkandidaten zu erfassen. Der zweite Beitrag ist eine Technik zur Selbstkonsistenz durch Berücksichtigung der Werteverteilungen von Quantitäten

Wrapper Maintenance: A Machine Learning Approach

The proliferation of online information sources has led to an increased use of wrappers for extracting data from Web sources. While most of the previous research has focused on quick and efficient generation of wrappers, the development of tools for wrapper maintenance has received less attention. This is an important research problem because Web sources often change in ways that prevent the wrappers from extracting data correctly. We present an efficient algorithm that learns structural information about data from positive examples alone. We describe how this information can be used for two wrapper maintenance applications: wrapper verification and reinduction. The wrapper verification system detects when a wrapper is not extracting correct data, usually because the Web source has changed its format. The reinduction algorithm automatically recovers from changes in the Web source by identifying data on Web pages so that a new wrapper may be generated for this source. To validate our approach, we monitored 27 wrappers over a period of a year. The verification algorithm correctly discovered 35 of the 37 wrapper changes, and made 16 mistakes, resulting in precision of 0.73 and recall of 0.95. We validated the reinduction algorithm on ten Web sources. We were able to successfully reinduce the wrappers, obtaining precision and recall values of 0.90 and 0.80 on the data extraction task

Learning Tuple Probabilities

Learning the parameters of complex probabilistic-relational models from labeled training data is a standard technique in machine learning, which has been intensively studied in the subfield of Statistical Relational Learning (SRL), but---so far---this is still an under-investigated topic in the context of Probabilistic Databases (PDBs). In this paper, we focus on learning the probability values of base tuples in a PDB from labeled lineage formulas. The resulting learning problem can be viewed as the inverse problem to confidence computations in PDBs: given a set of labeled query answers, learn the probability values of the base tuples, such that the marginal probabilities of the query answers again yield in the assigned probability labels. We analyze the learning problem from a theoretical perspective, cast it into an optimization problem, and provide an algorithm based on stochastic gradient descent. Finally, we conclude by an experimental evaluation on three real-world and one synthetic dataset, thus comparing our approach to various techniques from SRL, reasoning in information extraction, and optimization

Cold-start universal information extraction

Who? What? When? Where? Why? are fundamental questions asked when gathering knowledge about and understanding a concept, topic, or event. The answers to these questions underpin the key information conveyed in the overwhelming majority, if not all, of language-based communication. At the core of my research in Information Extraction (IE) is the desire to endow machines with the ability to automatically extract, assess, and understand text in order to answer these fundamental questions. IE has been serving as one of the most important components for many downstream natural language processing (NLP) tasks, such as knowledge base completion, machine reading comprehension, machine translation and so on. The proliferation of the Web also intensifies the need of dealing with enormous amount of unstructured data from various sources, such as languages, genres and domains. When building an IE system, the conventional pipeline is to (1) ask expert linguists to rigorously define a target set of knowledge types we wish to extract by examining a large data set, (2) collect resources and human annotations for each type, and (3) design features and train machine learning models to extract knowledge elements. In practice, this process is very expensive as each step involves extensive human effort which is not always available, for example, to specify the knowledge types for a particular scenario, both consumers and expert linguists need to examine a lot of data from that domain and write detailed annotation guidelines for each type. Hand-crafted schemas, which define the types and complex templates of the expected knowledge elements, often provide low coverage and fail to generalize to new domains. For example, none of the traditional event extraction programs, such as ACE (Automatic Content Extraction) and TAC-KBP, include "donation'' and "evacuation'' in their schemas in spite of their potential relevance to natural disaster management users. Additionally, these approaches are highly dependent on linguistic resources and human labeled data tuned to pre-defined types, so they suffer from poor scalability and portability when moving to a new language, domain, or genre. The focus of this thesis is to develop effective theories and algorithms for IE which not only yield satisfactory quality by incorporating prior linguistic and semantic knowledge, but also greater portability and scalability by moving away from the high cost and narrow focus of large-scale manual annotation. This thesis opens up a new research direction called Cold-Start Universal Information Extraction, where the full extraction and analysis starts from scratch and requires little or no prior manual annotation or pre-defined type schema. In addition to this new research paradigm, we also contribute effective algorithms and models towards resolving the following three challenges: How can machines extract knowledge without any pre-defined types or any human annotated data? We develop an effective bottom-up and unsupervised Liberal Information Extraction framework based on the hypothesis that the meaning and underlying knowledge conveyed by linguistic expressions is usually embodied by their usages in language, which makes it possible to automatically induces a type schema based on rich contextual representations of all knowledge elements by combining their symbolic and distributional semantics using unsupervised hierarchical clustering. How can machines benefit from available resources, e.g., large-scale ontologies or existing human annotations? My research has shown that pre-defined types can also be encoded by rich contextual or structured representations, through which knowledge elements can be mapped to their appropriate types. Therefore, we design a weakly supervised Zero-shot Learning and a Semi-Supervised Vector Quantized Variational Auto-Encoder approach that frames IE as a grounding problem instead of classification, where knowledge elements are grounded into any types from an extensible and large-scale target ontology or induced from the corpora, with available annotations for a few types. How can IE approaches be extent to low-resource languages without any extra human effort? There are more than 6000 living languages in the real world while public gold-standard annotations are only available for a few dominant languages. To facilitate the adaptation of these IE frameworks to other languages, especially low resource languages, a Multilingual Common Semantic Space is further proposed to serve as a bridge for transferring existing resources and annotated data from dominant languages to more than 300 low resource languages. Moreover, a Multi-Level Adversarial Transfer framework is also designed to learn language-agnostic features across various languages

Information Extraction on Para-Relational Data.

Para-relational data (such as spreadsheets and diagrams) refers to a type of nearly relational data that shares the important qualities of relational data but does not present itself in a relational format. Para-relational data often conveys highly valuable information and is widely used in many different areas. If we can convert para-relational data into the relational format, many existing tools can be leveraged for a variety of interesting applications, such as data analysis with relational query systems and data integration applications. This dissertation aims to convert para-relational data into a high-quality relational form with little user assistance. We have developed four standalone systems, each addressing a specific type of para-relational data. Senbazuru is a prototype spreadsheet database management system that extracts relational information from a large number of spreadsheets. Anthias is an extension of the Senbazuru system to convert a broader range of spreadsheets into a relational format. Lyretail is an extraction system to detect long-tail dictionary entities on webpages. Finally, DiagramFlyer is a web-based search system that obtains a large number of diagrams automatically extracted from web-crawled PDFs. Together, these four systems demonstrate that converting para-relational data into the relational format is possible today, and also suggest directions for future systems.PhDComputer Science and EngineeringUniversity of Michigan, Horace H. Rackham School of Graduate Studieshttp://deepblue.lib.umich.edu/bitstream/2027.42/120853/1/chenzhe_1.pd

Knowledge-based Biomedical Data Science 2019

Knowledge-based biomedical data science (KBDS) involves the design and implementation of computer systems that act as if they knew about biomedicine. Such systems depend on formally represented knowledge in computer systems, often in the form of knowledge graphs. Here we survey the progress in the last year in systems that use formally represented knowledge to address data science problems in both clinical and biological domains, as well as on approaches for creating knowledge graphs. Major themes include the relationships between knowledge graphs and machine learning, the use of natural language processing, and the expansion of knowledge-based approaches to novel domains, such as Chinese Traditional Medicine and biodiversity.Comment: Manuscript 43 pages with 3 tables; Supplemental material 43 pages with 3 table