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idMesh: Graph-Based Disambiguation of Linked Data
- In WWW
, 2009
"... passau.de We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities – repre-sented by globally identifiable declarative artifacts – self-organize in a dynamic and probabilistic manner. Our solution has the fol-lowing two desirable propertie ..."
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Cited by 22 (2 self)
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passau.de We tackle the problem of disambiguating entities on the Web. We propose a user-driven scheme where graphs of entities – repre-sented by globally identifiable declarative artifacts – self-organize in a dynamic and probabilistic manner. Our solution has the fol-lowing two desirable properties: i) it lets end-users freely define associations between arbitrary entities and ii) it probabilistically in-fers entity relationships based on uncertain links using constraint-satisfaction mechanisms. We outline the interface between our scheme and the current data Web, and show how higher-layer ap-plications can take advantage of our approach to enhance search and update of information relating to online entities. We describe a decentralized infrastructure supporting efficient and scalable entity disambiguation and demonstrate the practicability of our approach in a deployment over several hundreds of machines.
Efficient Entity Resolution for Large Heterogeneous Information Spaces
"... We have recently witnessed an enormous growth in the volume of structured and semi-structured data sets available on the Web. An important prerequisite for using and combining such data sets is the detection and merge of information that describes the same real-world entities, a task known as Entity ..."
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We have recently witnessed an enormous growth in the volume of structured and semi-structured data sets available on the Web. An important prerequisite for using and combining such data sets is the detection and merge of information that describes the same real-world entities, a task known as Entity Resolution. To make this quadratic task efficient, blocking techniques are typically employed. However, the high dynamics, loose schema binding, and heterogeneity of (semi-)structured data, impose new challenges to entity resolution. Existing blocking approaches become inapplicable because they rely on the homogeneity of the considered data and a-priory known schemata. In this paper, we introduce a novel approach for entity resolution, scaling it up for large, noisy, and heterogeneous information spaces. It combines an attribute-agnostic mechanism for building blocks with intelligent block processing techniques that boost blocks with high expected utility, propagate knowledge about identified matches, and preempt the resolution process when it gets too expensive. Our extensive evaluation on real-world, large, heterogeneous data sets verifies that the suggested approach is both effective and efficient.
Efficient Semantic-Aware Detection of Near Duplicate Resources
"... Abstract. Efficiently detecting near duplicate resources is an important task when integrating information from various sources and applications. Once detected, near duplicate resources can be grouped together, merged, or removed, in order to avoid repetition and redundancy, and to increase the dive ..."
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Abstract. Efficiently detecting near duplicate resources is an important task when integrating information from various sources and applications. Once detected, near duplicate resources can be grouped together, merged, or removed, in order to avoid repetition and redundancy, and to increase the diversity in the information provided to the user. In this paper, we introduce an approach for efficient semantic-aware near duplicate detection, by combining an indexing scheme for similarity search with the RDF representations of the resources. We provide a probabilistic analysis for the correctness of the suggested approach, which allows applications to configure it for satisfying their specific quality requirements. Our experimental evaluation on the RDF descriptions of real-world news articles from various news agencies demonstrates the efficiency and effectiveness of our approach. Key words: near duplicate detection, data integration 1
Finding Experts on the Semantic Desktop
"... Abstract. Expert retrieval has attracted deep attention because of the huge economical impact it can have on enterprises. The classical dataset on which to perform this task is company intranet (i.e., personal pages, e-mails, documents). We propose a new system for finding experts in the user’s desk ..."
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Abstract. Expert retrieval has attracted deep attention because of the huge economical impact it can have on enterprises. The classical dataset on which to perform this task is company intranet (i.e., personal pages, e-mails, documents). We propose a new system for finding experts in the user’s desktop content. Looking at private documents and e-mails of the user, the system builds expert profiles for all the people named in the desktop. This allows the search system to focus on the user’s topics of interest thus generating satisfactory results on topics well represented on the desktop. We show, with an artificial test collection, how the desktop content is appropriate for finding experts on the topic the user is interested in. 1
From Web Data to Entities and Back
"... Abstract. We present the Entity Name System (ENS), an enabling infrastructure, which can host descriptions of named entities and provide unique identifiers, on large-scale. In this way, it opens new perspectives to realize entity-oriented, rather than keyword-oriented, Web information systems. We de ..."
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Abstract. We present the Entity Name System (ENS), an enabling infrastructure, which can host descriptions of named entities and provide unique identifiers, on large-scale. In this way, it opens new perspectives to realize entity-oriented, rather than keyword-oriented, Web information systems. We describe the architecture and the functionality of the ENS, along with tools, which all contribute to realize the Web of entities.
Entity-Aware Query Processing for Heterogeneous Data with Uncertainty and Correlations
"... Many modern systems rely on rich heterogeneous data that has been integrated from a variety of different applications and sources. To successfully perform their tasks, these systems require to know which data refer to the same real-world entities, such as locations, people, or movies. My work focuse ..."
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Many modern systems rely on rich heterogeneous data that has been integrated from a variety of different applications and sources. To successfully perform their tasks, these systems require to know which data refer to the same real-world entities, such as locations, people, or movies. My work focuses on addressing this requirement through a new approach for entity-aware query processing over heterogeneous data. Data provided for integration is processed to generate the possible entities and linkages between these entities. This information is never merged with the original data, but used during query processing to provide entity-aware results that reflect the real-world entities existing in the current data. Special emphasis is given to the effective management of uncertainty and correlations that either exist in the original data, or are generated by data matching techniques. Advisor: Prof. Dr. Wolfgang Nejdl 1.
Leveraging PersonalMetadata for Desktop Search: The Beagle++ System
"... Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop searc ..."
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Search on PCs has become less efficient than searching the Web due to the increasing amount of stored data. In this paper we present an innovative Desktop search solution, which relies on extracted metadata, context information as well as additional background information for improving Desktop search results. We also present a practical application of this approach — the extensible Beagle++ toolbox. To prove the validity of our approach, we conducted a series of experiments. By comparing our results against the ones of a regular Desktop search solution — Beagle — we show an improved quality in search and overall performance.
User Data Discovery and Aggregation: the CS-UDD Algorithm
"... and other research outputs User data discovery and aggregation: the CS-UDD al-gorithm Journal Article ..."
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and other research outputs User data discovery and aggregation: the CS-UDD al-gorithm Journal Article
