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Wrapper induction faces a dilemma: To reach web scale, it requires automatically generated examples, but to produce accurate results, these examples must have the quality of human annotations. We resolve this conflict with AMBER, a system for fully automated data extraction from result pages. In contrast to previous approaches, AMBER employs domain specific gazetteers to discern basic domain attributes on a page, and leverages repeated occurrences of similar attributes to group related attributes into records rather than relying on the noisy structure of the DOM. With this approach AMBER is able to identify records and their attributes with almost perfect accuracy (>98%) on a large sample of websites. To make such an approach feasible at scale, AMBER automatically learns domain gazetteers from a small seed set. In this demonstration, we show how AMBER uses the repeated structure of records on deep web result pages to learn such gazetteers. This is only possible with a highly accurate extraction system. Depending on its parametrization, this learning process runs either fully automatically or with human interaction. We show how AMBER bootstraps a gazetteer for UK locations in 4 iterations: From a small seed sample we achieve 94.4% accuracy in recognizing UK locations in the 4th iteration.
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Search engines are the sinews of the web. These sinews have become strained, however: Where the web's function once was a mix of library and yellow pages, it has become the central marketplace for information of almost any kind. We search more and more for objects with specific characteristics, a car with a certain mileage, an affordable apartment close to a good school, or the latest accessory for our phones. Search engines all too often fail to provide reasonable answers, making us sift through dozens of websites with thousands of offers--never to be sure a better offer isn't just around the corner. What search engines are missing is understanding of the objects and their attributes published on websites. Automatically identifying and extracting these objects is akin to alchemy: transforming unstructured web information into highly structured data with near perfect accuracy. With DIADEM we present a formula for this transformation, but at a price: DIADEM identifies and extracts data from a website with high accuracy. The price is that for this task we need to provide DIADEM with extensive knowledge about the ontology and phenomenology of the domain, i.e., about entities (and relations) and about the representation of these entities in the textual, structural, and visual language of a website of this domain. In this demonstration, we demonstrate with a first prototype of DIADEM that, in contrast to alchemists, DIADEM has developed a viable formula.
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Wrapper induction faces a dilemma: To reach web scale, it requires automatically generated examples, but to produce accurate results, these examples must have the quality of human annotations. We resolve this conflict with AMBER, a system for fully automated data extraction from result pages. In contrast to previous approaches, AMBER employs domain specific gazetteers to discern basic domain attributes on a page, and leverages repeated occurrences of similar attributes to group related attributes into records rather than relying on the noisy structure of the DOM. With this approach AMBER is able to identify records and their attributes with almost perfect accuracy (>98%) on a large sample of websites. To make such an approach feasible at scale, AMBER automatically learns domain gazetteers from a small seed set. In this demonstration, we show how AMBER uses the repeated structure of records on deep web result pages to learn such gazetteers. This is only possible with a highly accurate extraction system. Depending on its parametrization, this learning process runs either fully automatically or with human interaction. We show how AMBER bootstraps a gazetteer for UK locations in 4 iterations: From a small seed sample we achieve 94.4% accuracy in recognizing UK locations in the 4th iteration.
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Giorgio Orsi
Search engines are the sinews of the web. These sinews have become strained, however: Where the web's function once was a mix of library and yellow pages, it has become the central marketplace for information of almost any kind. We search more and more for objects with specific characteristics, a car with a certain mileage, an affordable apartment close to a good school, or the latest accessory for our phones. Search engines all too often fail to provide reasonable answers, making us sift through dozens of websites with thousands of offers--never to be sure a better offer isn't just around the corner. What search engines are missing is understanding of the objects and their attributes published on websites. Automatically identifying and extracting these objects is akin to alchemy: transforming unstructured web information into highly structured data with near perfect accuracy. With DIADEM we present a formula for this transformation, but at a price: DIADEM identifies and extracts data from a website with high accuracy. The price is that for this task we need to provide DIADEM with extensive knowledge about the ontology and phenomenology of the domain, i.e., about entities (and relations) and about the representation of these entities in the textual, structural, and visual language of a website of this domain. In this demonstration, we demonstrate with a first prototype of DIADEM that, in contrast to alchemists, DIADEM has developed a viable formula.
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Ontological queries are evaluated against a knowledge base consisting of an extensional database and an ontology (i.e., a set of logical assertions and constraints that derive new intensional knowledge from the extensional database), rather than directly on the extensional database. The evaluation and optimization of such queries is an intriguing new problem for database research. In this article, we discuss two important aspects of this problem: query rewriting and query optimization. Query rewriting consists of the compilation of an ontological query into an equivalent first-order query against the underlying extensional database. We present a novel query rewriting algorithm for rather general types of ontological constraints that is well suited for practical implementations. In particular, we show how a conjunctive query against a knowledge base, expressed using linear and sticky existential rules, that is, members of the recently introduced Datalog+/- family of ontology languages, can be compiled into a union of conjunctive queries (UCQ) against the underlying database. Ontological query optimization, in this context, attempts to improve this rewriting process soas to produce possibly small and cost-effective UCQ rewritings for an input query.
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ROSeAnn - Reconciling Opinions of Semantic Annotators. VLDB 2014 Conference. A growing number of resources are available for enriching documents with semantic annotations. While originally focused on a few standard classes of annotations, the ecosystem of annotators is now becoming increasingly diverse. Although annotators often have very different vocabularies, with both high-level and specialist concepts, they also have many semantic interconnections. We will show that both the overlap and the diversity in annotator vocabularies motivate the need for semantic annotation integration: middleware that produces a unified annotation on top of diverse semantic annotators. On the one hand, the diversity of vocabulary allows applications to benefit from the much richer vocabulary available in an integrated vocabulary. On the other hand, we present evidence that the most widely-used annotators on the web suffer from serious accuracy deficiencies: the overlap in vocabularies from individual annotators allows an integrated annotator to boost accuracy by exploiting inter-annotator agreement and disagreement. The integration of semantic annotations leads to new challenges, both compared to usual data integration scenarios and to standard aggregation of machine learning tools. We overview an approach to these challenges that performs ontology-aware aggregation. We introduce an approach that requires no training data, making use of ideas from database repair. We experimentally compare this with a supervised approach, which adapts maximal entropy Markov models to the setting of ontology-based annotations. We further experimentally compare both these approaches with respect to ontology-unaware supervised approaches, and to individual annotators.
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The Semantic Web effort has steadily been gaining traction in the recent years. In particular,Web search companies are recently realizing that their products need to evolve towards having richer semantic search capabilities. Description logics (DLs) have been adopted as the formal underpinnings for Semantic Web languages used in describing ontologies. Reasoning under uncertainty has recently taken a leading role in this arena, given the nature of data found on theWeb. In this paper, we present a probabilistic extension of the DL EL++ (which underlies the OWL2 EL profile) using Markov logic networks (MLNs) as probabilistic semantics. This extension is tightly coupled, meaning that probabilistic annotations in formulas can refer to objects in the ontology. We show that, even though the tightly coupled nature of our language means that many basic operations are data-intractable, we can leverage a sublanguage of MLNs that allows to rank the atomic consequences of an ontology relative to their probability values (called ranking queries) even when these values are not fully computed. We present an anytime algorithm to answer ranking queries, and provide an upper bound on the error that it incurs, as well as a criterion to decide when results are guaranteed to be correct.
Heuristic Ranking in Tightly Coupled Probabilistic Description Logics
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Web forms are the interfaces of the deep web. Though modern web browsers provide facilities to assist in form filling, this assistance is limited to prior form fillings or keyword matching. Automatic form understanding enables a broad range of applications, including crawlers, meta-search engines, and usability and accessibility support for enhanced web browsing. In this demonstration, we use a novel form understanding approach, OPAL, to assist in form filling even for complex, previously unknown forms. OPAL associates form labels to fields by analyzing structural properties in the HTML encoding and visual features of the page rendering. OPAL interprets this labeling and classifies the fields according to a given domain ontology. The combination of these two properties, allows OPAL to deal effectively with many forms outside of the grasp of existing form filling techniques. In the UK real estate domain, OPAL achieves >99% accuracy in form understanding.
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)
OPAL: a passe-partout for web forms - WWW 2012 (Demonstration)
Giorgio Orsi
UML Class Diagrams (UCDs) are the best known class-based formalism for conceptual modeling. They are used by software engineers to model the intensional structure of a system in terms of classes, attributes and operations, and to express constraints that must hold for every instance of the system. Reasoning over UCDs is of paramount importance in design, validation, maintenance and system analysis; however, for medium and large software projects, reasoning over UCDs may be impractical. Query answering, in particular, can be used to verify whether a (possibly incomplete) instance of the system modeled by the UCD, i.e., a snapshot, enjoys a certain property. In this work, we study the problem of querying UCD instances, and we relate it to query answering under guarded Datalog +/-, that is, a powerful Datalog-based language for ontological modeling. We present an expressive and meaningful class of UCDs, named UCDLog, under which conjunctive query answering is tractable in the size of the instances.
Querying UML Class Diagrams - FoSSaCS 2012
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Giorgio Orsi
Forms are our gates to the web. They enable us to access the deep content of web sites. Automatic form understanding unlocks this content for applications ranging from crawlers to meta-search engines and is essential for improving usability and accessibility of the web. Form understanding has received surprisingly little attention other than as component in specific applications such as crawlers. No comprehensive approach to form understanding exists and previous works disagree even in the definition of the problem. In this paper, we present OPAL, the first comprehensive approach to form understanding. We identify form labeling and form interpretation as the two main tasks involved in form understanding. On both problems OPAL pushes the state of the art: For form labeling, it combines signals from the text, structure, and visual rendering of a web page, yielding robust characterisations of common design patterns. In extensive experiments on the ICQ and TEL-8 benchmarks and a set of 200 modern web forms OPAL outperforms previous approaches by a significant margin. For form interpretation, we introduce a template language to describe frequent form patterns. These two parts of OPAL combined yield form understanding with near perfect accuracy (> 98%).
OPAL: automated form understanding for the deep web - WWW 2012
OPAL: automated form understanding for the deep web - WWW 2012
Giorgio Orsi
We present Nyaya , a flexible system for the management of Semantic-Web data which couples a general-purpose storage mechanism with efficient ontology reasoning and querying capabilities. Nyaya processes large Semantic-Web datasets, expressed in a variety of formalisms, by transforming them into a collection of Semantic Data Kiosks. Each kiosk exposes the native meta-data in a uniform fashion using Datalog± , a very general rule-based language for the representation of ontological constraints. The kiosks form a Semantic Data Market where the data in each kiosk can be uniformly accessed using conjunctive queries and where users can specify user-defined constraints over the data. Nyaya is easily extensible and robust to updates of both data and meta-data in the kiosk and can readily adapt to different logical organization of the persistent storage. The approach has been experimented using well-known benchmarks, and compared to state-of-the-art research prototypes and commercial systems.
Nyaya: Semantic data markets: a flexible environment for knowledge management...
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The Diadem Ontology
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Diadem 1.0
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OPAL Presentation
OPAL Presentation
Table Recognition
1.
The DIADEM Ontology
DIADEM 1.0 Yiyang Bao 2 , Xiaonan Guo 2 , Giorgio Orsi 1,2 , Christian Schallhart 2 , Cheng Wang 2 1 Institute for the Future of Computing University of Oxford 2 Department of Computer Science University of Oxford
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Linking the domain
ontology: OntoX
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Uncertainty, Vagueness and
Inconsistencies
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Thank you!
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