LANCE: A Generic Benchmark Generator for Linked Data

نویسندگان

  • Tzanina Saveta
  • Evangelia Daskalaki
  • Giorgos Flouris
  • Irini Fundulaki
  • Axel-Cyrille Ngonga Ngomo
چکیده

Identifying duplicate instances in the Data Web is most commonly performed (semi-)automatically using instance matching frameworks. However, current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this demo paper, we present Lance, a domain-independent instance matching benchmark generator for Linked Data. Lance is the first benchmark generator for Linked Data to support semantics-aware test cases that take into account complex OWL constructs in addition to the standard test cases related to structure and value transformations. Lance supports the definition of matching tasks with varying degrees of difficulty and produces a weighted gold standard, which allows a more fine-grained analysis of the performance of instance matching tools. It can accept as input any linked dataset and its accompanying schema to produce a target dataset implementing test cases of varying levels of difficulty. In this demo, we will present the benchmark generation process underlying Lance as well as the user interface designed to support Lance users.

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

منابع مشابه

LANCE: Piercing to the Heart of Instance Matching Tools

One of the main challenges in the Data Web is the identification of instances that refer to the same real-world entity. Choosing the right framework for this purpose remains tedious, as current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this paper, we present Lance,...

متن کامل

How Well Does Your Instance Matching System Perform? Experimental Evaluation with LANCE

Identifying duplicate instances in the Data Web is most commonly performed (semi-)automatically using instance matching frameworks. However, current instance matching benchmarks fail to provide end users and developers with the necessary insights pertaining to how current frameworks behave when dealing with real data. In this paper, we present the results of the evaluation of instance matching ...

متن کامل

Benchmarking Linked Open Data Management Systems

Objective, well-designed and good quality benchmarks are important to fairly compare the performance of software products and uncover useful insights related to their strengths as well as their limitations. They encourage the advancement of technology by providing both academy and industry with clear targets for performance and functionality. The Linked Data Benchmark Council (LDBC) aims to cre...

متن کامل

HOBBIT link discovery benchmarks at ontology matching 2017

We address the problem of benchmarking ontology matching and link discovery frameworks at large scale. In particular, we aim to ensure that the benchmarks generate comparable results for the various systems and approaches. Our solution lies in implementing our benchmarks into the HOBBIT benchmarking platform, which provide means for the unified benchmarking of Big Linked Data solutions. The HOB...

متن کامل

Benchmarking Link Discovery Systems for Geo-Spatial Data

Linking geo-spatial entities is targeted only by a limited number of link discovery benchmarks. Linking spatial resources requires techniques that differ from the classical, mostly string-based approaches. In particular, considering the topology of the spatial resources and the topological relations between them is of central importance to systems that manage spatial data. Due to the large amou...

متن کامل

ذخیره در منابع من


  با ذخیره ی این منبع در منابع من، دسترسی به آن را برای استفاده های بعدی آسان تر کنید

برای دانلود متن کامل این مقاله و بیش از 32 میلیون مقاله دیگر ابتدا ثبت نام کنید

ثبت نام

اگر عضو سایت هستید لطفا وارد حساب کاربری خود شوید

عنوان ژورنال:

دوره   شماره 

صفحات  -

تاریخ انتشار 2015