27 research outputs found
MinoanER: Schema-Agnostic, Non-Iterative, Massively Parallel Resolution of Web Entities
Entity Resolution (ER) aims to identify different descriptions in various
Knowledge Bases (KBs) that refer to the same entity. ER is challenged by the
Variety, Volume and Veracity of entity descriptions published in the Web of
Data. To address them, we propose the MinoanER framework that simultaneously
fulfills full automation, support of highly heterogeneous entities, and massive
parallelization of the ER process. MinoanER leverages a token-based similarity
of entities to define a new metric that derives the similarity of neighboring
entities from the most important relations, as they are indicated only by
statistics. A composite blocking method is employed to capture different
sources of matching evidence from the content, neighbors, or names of entities.
The search space of candidate pairs for comparison is compactly abstracted by a
novel disjunctive blocking graph and processed by a non-iterative, massively
parallel matching algorithm that consists of four generic, schema-agnostic
matching rules that are quite robust with respect to their internal
configuration. We demonstrate that the effectiveness of MinoanER is comparable
to existing ER tools over real KBs exhibiting low Variety, but it outperforms
them significantly when matching KBs with high Variety.Comment: Presented at EDBT 2001
Natural Language Interfaces to Data
Recent advances in NLU and NLP have resulted in renewed interest in natural
language interfaces to data, which provide an easy mechanism for non-technical
users to access and query the data. While early systems evolved from keyword
search and focused on simple factual queries, the complexity of both the input
sentences as well as the generated SQL queries has evolved over time. More
recently, there has also been a lot of focus on using conversational interfaces
for data analytics, empowering a line of non-technical users with quick
insights into the data. There are three main challenges in natural language
querying (NLQ): (1) identifying the entities involved in the user utterance,
(2) connecting the different entities in a meaningful way over the underlying
data source to interpret user intents, and (3) generating a structured query in
the form of SQL or SPARQL.
There are two main approaches for interpreting a user's NLQ. Rule-based
systems make use of semantic indices, ontologies, and KGs to identify the
entities in the query, understand the intended relationships between those
entities, and utilize grammars to generate the target queries. With the
advances in deep learning (DL)-based language models, there have been many
text-to-SQL approaches that try to interpret the query holistically using DL
models. Hybrid approaches that utilize both rule-based techniques as well as DL
models are also emerging by combining the strengths of both approaches.
Conversational interfaces are the next natural step to one-shot NLQ by
exploiting query context between multiple turns of conversation for
disambiguation. In this article, we review the background technologies that are
used in natural language interfaces, and survey the different approaches to
NLQ. We also describe conversational interfaces for data analytics and discuss
several benchmarks used for NLQ research and evaluation.Comment: The full version of this manuscript, as published by Foundations and
Trends in Databases, is available at http://dx.doi.org/10.1561/190000007
End-to-End Entity Resolution for Big Data: A Survey
One of the most important tasks for improving data quality and the
reliability of data analytics results is Entity Resolution (ER). ER aims to
identify different descriptions that refer to the same real-world entity, and
remains a challenging problem. While previous works have studied specific
aspects of ER (and mostly in traditional settings), in this survey, we provide
for the first time an end-to-end view of modern ER workflows, and of the novel
aspects of entity indexing and matching methods in order to cope with more than
one of the Big Data characteristics simultaneously. We present the basic
concepts, processing steps and execution strategies that have been proposed by
different communities, i.e., database, semantic Web and machine learning, in
order to cope with the loose structuredness, extreme diversity, high speed and
large scale of entity descriptions used by real-world applications. Finally, we
provide a synthetic discussion of the existing approaches, and conclude with a
detailed presentation of open research directions
Benchmarking Blocking Algorithms for Web Entities
An increasing number of entities are described by interlinked data rather
than documents on the Web. Entity Resolution (ER) aims to identify descriptions
of the same real-world entity within one or across knowledge bases in the Web
of data. To reduce the required number of pairwise comparisons among
descriptions, ER methods typically perform a pre-processing step, called
\emph{blocking}, which places similar entity descriptions into blocks and thus
only compare descriptions within the same block. We experimentally evaluate
several blocking methods proposed for the Web of data using real datasets,
whose characteristics significantly impact their effectiveness and efficiency.
The proposed experimental evaluation framework allows us to better understand
the characteristics of the missed matching entity descriptions and contrast
them with ground truth obtained from different kinds of relatedness links.Comment: accepted at IEEE Transactions on Big Data journa
Web-Scale Blocking, Iterative and Progressive Entity Resolution
International audienceEntity resolution aims to identify descriptions of the same entity within or across knowledge bases. In this work, we provide a comprehensive and cohesive overview of the key research results in the area of entity resolution. We are interested in frameworks addressing the new challenges in entity resolution posed by the Web of data in which real world entities are described by interlinked data rather than documents. Since such descriptions are usually partial, overlapping and sometimes evolving, entity resolution emerges as a central problem both to increase dataset linking, but also to search the Web of data for entities and their relations. We focus on Web-scale blocking, iterative and progressive solutions for entity resolution. Specifically, to reduce the required number of comparisons, blocking is performed to place similar descriptions into blocks and executes comparisons to identify matches only between descriptions within the same block. To minimize the number of missed matches, an iterative entity resolution process can exploit any intermediate results of blocking and matching, discovering new candidate description pairs for resolution. Finally, we overview works on progressive entity resolution, which attempt to discover as many matches as possible given limited computing budget, by estimating the matching likelihood of yet unresolved descriptions, based on the matches found so far
Entity Resolution in the Web of Data
International audienceIn recent years, several knowledge bases have been built to enable large-scale knowledge sharing, but also an entity-centric Web search, mixing both structured data and text querying. These knowledge bases offer machine-readable descriptions of real-world entities, e.g., persons, places, published on the Web as Linked Data. However, due to the different information extraction tools and curation policies employed by knowledge bases, multiple, complementary and sometimes conflicting descriptions of the same real-world entities may be provided. Entity resolution aims to identify different descriptions that refer to the same entity appearing either within or across knowledge bases.The objective of this book is to present the new entity resolution challenges stemming from the openness of the Web of data in describing entities by an unbounded number of knowledge bases, the semantic and structural diversity of the descriptions provided across domains even for the same real-world entities, as well as the autonomy of knowledge bases in terms of adopted processes for creating and curating entity descriptions. The scale, diversity, and graph structuring of entity descriptions in the Web of data essentially challenge how two descriptions can be effectively compared for similarity, but also how resolution algorithms can efficiently avoid examining pairwise all descriptions.The book covers a wide spectrum of entity resolution issues at the Web scale, including basic concepts and data structures, main resolution tasks and workflows, as well as state-of-the-art algorithmic techniques and experimental trade-offs
FairER : Entity Resolution with Fairness Constraints
There is an urgent call to detect and prevent "biased data"at the earliest possible stage of the data pipelines used to build automated decision-making systems. In this paper, we are focusing on controlling the data bias in entity resolution (ER) tasks aiming to discover and unify records/descriptions from different data sources that refer to the same real-world entity. We formally define the ER problem with fairness constraints ensuring that all groups of entities have similar chances to be resolved. Then, we introduce FairER, a greedy algorithm for solving this problem for fairness criteria based on equal matching decisions. Our experiments show that FairER achieves similar or higher accuracy against two baseline methods over 7 datasets, while guaranteeing minimal bias.acceptedVersionPeer reviewe