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Collective Annotation of Wikipedia Entities in Web Text
"... To take the first step beyond keyword-based search toward entity-based search, suitable token spans (“spots”) on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in Wikipedia. They are ..."
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Cited by 105 (9 self)
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To take the first step beyond keyword-based search toward entity-based search, suitable token spans (“spots”) on documents must be identified as references to real-world entities from an entity catalog. Several systems have been proposed to link spots on Web pages to entities in Wikipedia. They are largely based on local compatibility between the text around the spot and textual metadata associated with the entity. Two recent systems exploit inter-label dependencies, but in limited ways. We propose a general collective disambiguation approach. Our premise is that coherent documents refer to entities from one or a few related topics or domains. We give formulations for the trade-off between local spot-to-entity compatibility and measures of global coherence between entities. Optimizing the overall entity assignment is NP-hard. We investigate practical solutions based on local hill-climbing, rounding integer linear programs, and pre-clustering entities followed by local optimization within clusters. In experiments involving over a hundred manuallyannotated Web pages and tens of thousands of spots, our approaches significantly outperform recently-proposed algorithms.
Sofie: A self-organizing framework for information extraction
, 2008
"... ABSTRACT This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the existing knowledge and on the new knowledge in order to disambiguat ..."
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Cited by 79 (20 self)
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ABSTRACT This paper presents SOFIE, a system for automated ontology extension. SOFIE can parse natural language documents, extract ontological facts from them and link the facts into an ontology. SOFIE uses logical reasoning on the existing knowledge and on the new knowledge in order to disambiguate words to their most probable meaning, to reason on the meaning of text patterns and to take into account world knowledge axioms. This allows SOFIE to check the plausibility of hypotheses and to avoid inconsistencies with the ontology. The framework of SOFIE unites the paradigms of pattern matching, word sense disambiguation and ontological reasoning in one unified model. Our experiments show that SOFIE delivers high-quality output, even from unstructured Internet documents.
Annotating and Searching Web Tables Using Entities, Types and Relationships
"... Tables are a universal idiom to present relational data. Billions of tables on Web pages express entity references, attributes and relationships. This representation of relational world knowledge is usually considerably better than completely unstructured, free-format text. At the same time, unlike ..."
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Cited by 55 (2 self)
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Tables are a universal idiom to present relational data. Billions of tables on Web pages express entity references, attributes and relationships. This representation of relational world knowledge is usually considerably better than completely unstructured, free-format text. At the same time, unlike manually-created knowledge bases, relational information mined from “organic ” Web tables need not be constrained by availability of precious editorial time. Unfortunately, in the absence of any formal, uniform schema imposed on Web tables, Web search cannot take advantage of these high-quality sources of relational information. In this paper we propose new machine learning techniques to annotate table cells with entities that they likely mention, table columns with types from which entities are drawn for cells in the column, and relations that pairs of table columns seek to express. We propose a new graphical model for making all these labeling decisions for each table simultaneously, rather than make separate local decisions for entities, types and relations. Experiments using the YAGO catalog, DB-Pedia, tables from Wikipedia, and over 25 million HTML tables from a 500 million page Web crawl uniformly show the superiority of our approach. We also evaluate the impact of better annotations on a prototype relational Web search tool. We demonstrate clear benefits of our annotations beyond indexing tables in a purely textual manner. 1.
Answering table augmentation queries from unstructured lists on the web
- PVLDB
"... We present the design of a system for assembling a table from a few example rows by harnessing the huge corpus of information-rich but unstructured lists on the web. We developed a totally unsupervised end to end approach which given the sample query rows — (a) retrieves HTML lists relevant to the q ..."
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Cited by 31 (6 self)
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We present the design of a system for assembling a table from a few example rows by harnessing the huge corpus of information-rich but unstructured lists on the web. We developed a totally unsupervised end to end approach which given the sample query rows — (a) retrieves HTML lists relevant to the query from a pre-indexed crawl of web lists, (b) segments the list records and maps the segments to the query schema using a statistical model, (c) consolidates the results from multiple lists into a unified merged table, (d) and presents to the user the consolidated records ranked by their estimated membership in the target relation. The key challenges in this task include construction of new rows from very few examples, and an abundance of noisy and irrelevant lists that swamp the consolidation and ranking of rows. We propose modifications to statistical record segmen-tation models, and present novel consolidation and ranking techniques that can process input tables of arbitrary schema without requiring any human supervision. Experiments with Wikipedia target tables and 16 million unstructured lists show that even with just three sample rows, our system is very effective at recreating Wikipedia tables, with a mean runtime of around 20s. 1.
Language-model-based ranking for queries on RDF-graphs
, 2009
"... The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large “knowledge repositories” such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and r ..."
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Cited by 31 (11 self)
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The success of knowledge-sharing communities like Wikipedia and the advances in automatic information extraction from textual and Web sources have made it possible to build large “knowledge repositories” such as DBpedia, Freebase, and YAGO. These collections can be viewed as graphs of entities and relationships (ER graphs) and can be represented as a set of subject-property-object (SPO) triples in the Semantic-Web data model RDF. Queries can be expressed in the W3C-endorsed SPARQL language or by similarly designed graph-pattern search. However, exact-match query semantics often fall short of satisfying the users ’ needs by returning too many or too few results. Therefore, IR-style ranking models are crucially needed. In this paper, we propose a language-model-based approach to ranking the results of exact, relaxed and keyword-augmented graphpattern queries over RDF graphs such as ER graphs. Our method estimates a query model and a set of result-graph models and ranks results based on their Kullback-Leibler divergence with respect to the query model. We demonstrate the effectiveness of our ranking model by a comprehensive user study.
From Information to Knowledge: Harvesting Entities and Relationships from Web Sources
"... There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-l ..."
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Cited by 26 (7 self)
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There are major trends to advance the functionality of search engines to a more expressive semantic level. This is enabled by the advent of knowledge-sharing communities such as Wikipedia and the progress in automatically extracting entities and relationships from semistructured as well as natural-language Web sources. Recent endeavors of this kind include DBpedia, EntityCube, KnowItAll, ReadTheWeb, and our own YAGO-NAGA project (and others). The goal is to automatically construct and maintain a comprehensive knowledge base of facts about named entities, their semantic classes, and their mutual relations as well as temporal contexts, with high precision and high recall. This tutorial discusses state-ofthe-art methods, research opportunities, and open challenges along this avenue of knowledge harvesting.
Efficiently incorporating user feedback into information extraction and integration programs
, 2009
"... Many applications increasingly employ information extrac-tion and integration (IE/II) programs to infer structures from unstructured data. Automatic IE/II are inherently imprecise. Hence such programs often make many IE/II mistakes, and thus can significantly benefit from user feed-back. Today, howe ..."
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Cited by 25 (4 self)
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Many applications increasingly employ information extrac-tion and integration (IE/II) programs to infer structures from unstructured data. Automatic IE/II are inherently imprecise. Hence such programs often make many IE/II mistakes, and thus can significantly benefit from user feed-back. Today, however, there is no good way to automatically provide and process such feedback. When finding an IE/II mistake, users often must alert the developer team (e.g., via email or Web form) about the mistake, and then wait for the team to manually examine the program internals to lo-cate and fix the mistake, a slow, error-prone, and frustrating process. In this paper we propose a solution for users to directly provide feedback and for IE/II programs to automatically process such feedback. In our solution a developer U uses hlog, a declarative IE/II language, to write an IE/II pro-gram P. Next, U writes declarative user feedback rules that specify which parts of P ’s data (e.g., input, intermediate, or output data) users can edit, and via which user interfaces. Next, the so-augmented program P is executed, then en-ters a loop of waiting for and incorporating user feedback. Given user feedback F on a data portion of P, we show how to automatically propagate F to the rest of P, and to seam-lessly combine F with prior user feedback. We describe the syntax and semantics of hlog, a baseline execution strategy, and then various optimization techniques. Finally, we de-scribe experiments with real-world data that demonstrate the promise of our solution.
Web Data Extraction, Applications and Techniques: A Survey
, 2010
"... The World Wide Web contains a huge amount of unstructured and semi-structured information, that is exponentially increasing with the coming of the Web 2.0, thanks to User-Generated Con-tents (UGC). In this paper we intend to briefly survey the fields of application, in particular enterprise and soci ..."
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Cited by 18 (10 self)
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The World Wide Web contains a huge amount of unstructured and semi-structured information, that is exponentially increasing with the coming of the Web 2.0, thanks to User-Generated Con-tents (UGC). In this paper we intend to briefly survey the fields of application, in particular enterprise and social applications, and techniques used to approach and solve the problem of the extraction of information from Web sources: during last years many approaches were developed, some inherited from past studies on Information Extraction (IE) systems, many others studied ad hoc to solve specific problems.
Elementary: Large-scale Knowledge-base Construction via Machine Learning and Statistical Inference
"... Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge ..."
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Cited by 17 (5 self)
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Researchers have approached knowledge-base construction (KBC) with a wide range of data resources and techniques. We present Elementary, a prototype KBC system that is able to combine diverse resources and different KBC techniques via machine learning and statistical inference to construct knowledge bases. Using Elementary, we have implemented a solution to the TAC-KBP challenge with quality comparable to the state of the art, as well as an end-to-end online demonstration that automatically and continuously enriches Wikipedia with structured data by reading millions of webpages on a daily basis. We describe several challenges and our solutions in designing, implementing, and deploying Elementary. In particular, we first describe the conceptual framework and architecture of Elementary, and then discuss how we address scalability challenges to enable Web-scale deployment. First, to take advantage of diverse data resources and proven techniques, Elementary employs Markov logic, a succinct yet expressive language to specify probabilistic graphical models. Elementary accepts both domain-knowledge rules and classical machine-learning models such as conditional random fields, thereby integrating different data resources and KBC techniques in a principled manner. Second, to support large-scale KBC with terabytes of data and millions of entities, Elementary
Collective Graph Identification
"... Data describing networks (communication networks, transaction networks, disease transmission networks, collaboration networks, etc.) is becoming increasingly ubiquitous. While this observational data is useful, it often only hints at the actual underlying social or technological structures which giv ..."
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Cited by 15 (4 self)
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Data describing networks (communication networks, transaction networks, disease transmission networks, collaboration networks, etc.) is becoming increasingly ubiquitous. While this observational data is useful, it often only hints at the actual underlying social or technological structures which give rise to the interactions. For example, an email communication network provides useful insight but is not the same as the“real”social network among individuals. In this paper, we introduce the problem of graph identification, i.e., the discovery of the true graph structure underlying an observed network. We cast the problem as a probabilistic inference task, in which we must infer the nodes, edges, and node labels of a hidden graph, based on evidence provided by the observed network. This in turn corresponds to the problems of performing entity resolution, link prediction, and node labeling to infer the hidden graph. While each of these problems have been studied separately, they have never been considered together as a coherent task. We presentasimpleyetnovelapproachtoaddressallthreeproblems simultaneously. Our approach, called C 3, consists of Coupled Collective Classifiers that are iteratively applied to propagate information among solutions to the problems. We empirically demonstrate that C 3 is superior, in terms of both predictive accuracy and runtime, to state-of-the-art probabilistic approaches on three real-world problems.
