Entity Synonym Discovery via Multipiece Bilateral Context Matching

Being able to automatically discover synonymous entities in an open-world setting benefits various tasks such as entity disambiguation or knowledge graph canonicalization. Existing works either only utilize entity features, or rely on structured annotations from a single piece of context where the entity is mentioned. To leverage diverse contexts where entities are mentioned, in this paper, we generalize the distributional hypothesis to a multi-context setting and propose a synonym discovery framework that detects entity synonyms from free-text corpora with considerations on effectiveness and robustness. As one of the key components in synonym discovery, we introduce a neural network model SYNONYMNET to determine whether or not two given entities are synonym with each other. Instead of using entities features, SYNONYMNET makes use of multiple pieces of contexts in which the entity is mentioned, and compares the context-level similarity via a bilateral matching schema. Experimental results demonstrate that the proposed model is able to detect synonym sets that are not observed during training on both generic and domain-specific datasets: Wiki+Freebase, PubMed+UMLS, and MedBook+MKG, with up to 4.16% improvement in terms of Area Under the Curve and 3.19% in terms of Mean Average Precision compared to the best baseline method.Comment: In IJCAI 2020 as a long paper. Code and data are available at https://github.com/czhang99/SynonymNe

Automatic Synonym Discovery with Knowledge Bases

Recognizing entity synonyms from text has become a crucial task in many entity-leveraging applications. However, discovering entity synonyms from domain-specific text corpora (e.g., news articles, scientific papers) is rather challenging. Current systems take an entity name string as input to find out other names that are synonymous, ignoring the fact that often times a name string can refer to multiple entities (e.g., "apple" could refer to both Apple Inc and the fruit apple). Moreover, most existing methods require training data manually created by domain experts to construct supervised-learning systems. In this paper, we study the problem of automatic synonym discovery with knowledge bases, that is, identifying synonyms for knowledge base entities in a given domain-specific corpus. The manually-curated synonyms for each entity stored in a knowledge base not only form a set of name strings to disambiguate the meaning for each other, but also can serve as "distant" supervision to help determine important features for the task. We propose a novel framework, called DPE, to integrate two kinds of mutually-complementing signals for synonym discovery, i.e., distributional features based on corpus-level statistics and textual patterns based on local contexts. In particular, DPE jointly optimizes the two kinds of signals in conjunction with distant supervision, so that they can mutually enhance each other in the training stage. At the inference stage, both signals will be utilized to discover synonyms for the given entities. Experimental results prove the effectiveness of the proposed framework

When Things Matter: A Data-Centric View of the Internet of Things

With the recent advances in radio-frequency identification (RFID), low-cost wireless sensor devices, and Web technologies, the Internet of Things (IoT) approach has gained momentum in connecting everyday objects to the Internet and facilitating machine-to-human and machine-to-machine communication with the physical world. While IoT offers the capability to connect and integrate both digital and physical entities, enabling a whole new class of applications and services, several significant challenges need to be addressed before these applications and services can be fully realized. A fundamental challenge centers around managing IoT data, typically produced in dynamic and volatile environments, which is not only extremely large in scale and volume, but also noisy, and continuous. This article surveys the main techniques and state-of-the-art research efforts in IoT from data-centric perspectives, including data stream processing, data storage models, complex event processing, and searching in IoT. Open research issues for IoT data management are also discussed

Toward Entity-Aware Search

As the Web has evolved into a data-rich repository, with the standard "page view," current search engines are becoming increasingly inadequate for a wide range of query tasks. While we often search for various data "entities" (e.g., phone number, paper PDF, date), today's engines only take us indirectly to pages. In my Ph.D. study, we focus on a novel type of Web search that is aware of data entities inside pages, a significant departure from traditional document retrieval. We study the various essential aspects of supporting entity-aware Web search. To begin with, we tackle the core challenge of ranking entities, by distilling its underlying conceptual model Impression Model and developing a probabilistic ranking framework, EntityRank, that is able to seamlessly integrate both local and global information in ranking. We also report a prototype system built to show the initial promise of the proposal. Then, we aim at distilling and abstracting the essential computation requirements of entity search. From the dual views of reasoning--entity as input and entity as output, we propose a dual-inversion framework, with two indexing and partition schemes, towards efficient and scalable query processing. Further, to recognize more entity instances, we study the problem of entity synonym discovery through mining query log data. The results we obtained so far have shown clear promise of entity-aware search, in its usefulness, effectiveness, efficiency and scalability

Entity Synonyms for Structured Web Search

Abstract—Nowadays, there are many queries issued to search engines targeting at finding values from structured data (e.g., movie showtime of a specific location). In such scenarios, there is often a mismatch between the values of structured data (how content creators describe entities) and the web queries (how different users try to retrieve them). Therefore, recognizing the alternative ways people use to reference an entity, is crucial for structured web search. In this paper, we study the problem of automatic generation of entity synonyms over structured data toward closing the gap between users and structured data. We propose an offline, data-driven approach that mines query logs for instances where content creators and web users apply a variety of strings to refer to the same webpages. This way, given a set of strings that reference entities, we generate an expanded set of equivalent strings (entity synonyms) for each entity. Our framework consists of three modules: candidate generation, candidate selection, and noise cleaning. We further study the cause of the problem through the identification of different entity synonym classes. The proposed method is verified with experiments on real-life data sets showing that we can significantly increase the coverage of structured web queries with good precision. Index Terms—Entity synonym, fuzzy matching, structured data, web query, query log. Ç

Automatic Extraction and Assessment of Entities from the Web

The search for information about entities, such as people or movies, plays an increasingly important role on the Web. This information is still scattered across many Web pages, making it more time consuming for a user to find all relevant information about an entity. This thesis describes techniques to extract entities and information about these entities from the Web, such as facts, opinions, questions and answers, interactive multimedia objects, and events. The findings of this thesis are that it is possible to create a large knowledge base automatically using a manually-crafted ontology. The precision of the extracted information was found to be between 75–90 % (facts and entities respectively) after using assessment algorithms. The algorithms from this thesis can be used to create such a knowledge base, which can be used in various research fields, such as question answering, named entity recognition, and information retrieval

Automatic population of knowledge bases with multimodal data about named entities

Knowledge bases are of great importance for Web search, recommendations, and many Information Retrieval tasks. However, maintaining them for not so popular entities is often a bottleneck. Typically, such entities have limited textual coverage and only a few ontological facts. Moreover, these entities are not well populated with multimodal data, such as images, videos, or audio recordings. The goals in this thesis are (1) to populate a given knowledge base with multimodal data about entities, such as images or audio recordings, and (2) to ease the task of maintaining and expanding the textual knowledge about a given entity, by recommending valuable text excerpts to the contributors of knowledge bases. The thesis makes three main contributions. The first two contributions concentrate on finding images of named entities with high precision, high recall, and high visual diversity. Our main focus are less popular entities, for which the image search engines fail to retrieve good results. Our methods utilize background knowledge about the entity, such as ontological facts or a short description, and a visual-based image similarity to rank and diversify a set of candidate images. Our third contribution is an approach for extracting text contents related to a given entity. It leverages a language-model-based similarity between a short description of the entity and the text sources, and solves a budget-constraint optimization program without any assumptions on the text structure. Moreover, our approach is also able to reliably extract entity related audio excerpts from news podcasts. We derive the time boundaries from the usually very noisy audio transcriptions.Wissensbasen wird bei der Websuche, bei Empfehlungsdiensten und vielen anderen Information Retrieval Aufgaben eine große Bedeutung zugeschrieben. Allerdings stellt sich deren Unterhalt für weniger populäre Entitäten als schwierig heraus. Üblicherweise ist die Anzahl an Texten über Entitäten dieser Art begrenzt, und es gibt nur wenige ontologische Fakten. Außerdem sind nicht viele multimediale Daten, wie zum Beispiel Bilder, Videos oder Tonaufnahmen, für diese Entitäten verfügbar. Die Ziele dieser Dissertation sind daher (1) eine gegebene Wissensbasis mit multimedialen Daten, wie Bilder oder Tonaufnahmen, über Entitäten anzureichern und (2) die Erleichterung der Aufgabe Texte über eine gegebene Entität zu verwalten und zu erweitern, indem den Beitragenden einer Wissensbasis nützliche Textausschnitte vorgeschlagen werden. Diese Dissertation leistet drei Hauptbeiträge. Die ersten zwei Beiträge sind im Gebiet des Auffindens von Bildern von benannten Entitäten mit hoher Genauigkeit, hoher Trefferquote, und hoher visueller Vielfalt. Das Hauptaugenmerk liegt auf den weniger populären Entitäten bei denen die Bildersuchmaschinen normalerweise keine guten Ergebnisse liefern. Unsere Verfahren benutzen Hintergrundwissen über die Entität, zum Beispiel ontologische Fakten oder eine Kurzbeschreibung, so wie ein visuell-basiertes Bilderähnlichkeitsmaß um die Bilder nach Rang zu ordnen und um eine Menge von Bilderkandidaten zu diversifizieren. Der dritte Beitrag ist ein Ansatz um Textinhalte, die sich auf eine gegebene Entität beziehen, zu extrahieren. Der Ansatz nutzt ein auf einem Sprachmodell basierendes Ähnlichkeitsmaß zwischen einer Kurzbeschreibung der Entität und den Textquellen und löst zudem ein Optimierungsproblem mit Budgetrestriktion, das keine Annahmen an die Textstruktur macht. Darüber hinaus ist der Ansatz in der Lage Tonaufnahmen, welche in Beziehung zu einer Entität stehen, zuverlässig aus Nachrichten-Podcasts zu extrahieren. Dafür werden zeitliche Abgrenzungen aus den normalerweise sehr verrauschten Audiotranskriptionen hergeleitet

Entity-Oriented Search

This open access book covers all facets of entity-oriented search—where “search” can be interpreted in the broadest sense of information access—from a unified point of view, and provides a coherent and comprehensive overview of the state of the art. It represents the first synthesis of research in this broad and rapidly developing area. Selected topics are discussed in-depth, the goal being to establish fundamental techniques and methods as a basis for future research and development. Additional topics are treated at a survey level only, containing numerous pointers to the relevant literature. A roadmap for future research, based on open issues and challenges identified along the way, rounds out the book. The book is divided into three main parts, sandwiched between introductory and concluding chapters. The first two chapters introduce readers to the basic concepts, provide an overview of entity-oriented search tasks, and present the various types and sources of data that will be used throughout the book. Part I deals with the core task of entity ranking: given a textual query, possibly enriched with additional elements or structural hints, return a ranked list of entities. This core task is examined in a number of different variants, using both structured and unstructured data collections, and numerous query formulations. In turn, Part II is devoted to the role of entities in bridging unstructured and structured data. Part III explores how entities can enable search engines to understand the concepts, meaning, and intent behind the query that the user enters into the search box, and how they can provide rich and focused responses (as opposed to merely a list of documents)—a process known as semantic search. The final chapter concludes the book by discussing the limitations of current approaches, and suggesting directions for future research. Researchers and graduate students are the primary target audience of this book. A general background in information retrieval is sufficient to follow the material, including an understanding of basic probability and statistics concepts as well as a basic knowledge of machine learning concepts and supervised learning algorithms