Current web-scale knowledge bases (KBs) incorporate a substantial amount of information in a structured format. Availability of this readily machine-digestible data has made KBs a desirable resource for other applications. This has motivated many to explore learning on KBs. Graph embeddings and inference rule learning are examples such methods. This paper concerns the later, mostly because
-
-
Taming The Data Monster To Make Better Decisions
Daisy Zhe Wang Comment Closed Uncategorized
[Source: News from The Herbert Wertheim College of Engineering] In today’s increasingly connected world, the sheer volume of messages we receive on any subject, including speech, images, video and metadata, as well as text imbedded in images and videos, is mind-boggling. When something unexpected happens, government policy analysts need to inform and advise our nation’s
-
Mining Rules Incrementally over Large Knowledge Bases
Xiaofeng Zhou Comment Closed publications, research directions
Xiaofeng Zhou Multiple web-scale knowledge bases (e.g., Freebase, YAGO, NELL) have been constructed using semi-supervised or unsupervised information extraction techniques and many of them, despite their large sizes, are continuously growing. Much research effort has been put into mining inference rules from these knowledge bases. To address the task of rule mining over evolving web-scale knowledge bases,
-
Multimodal Learning for Web Information Extraction
Dihong Gong Comment Closed publications
We consider the problem of extracting text instances of predefined categories (e.g. city and person) from the Web. Instances of a category may be scattered across thousands of independent sources in many different formats with potential noises, which makes open-domain information extraction a challenging problem. Learning syntactic rules like “cities such as _” or “_
-
Archimedes: Efficient Query Processing over Probabilistic Knowledge Bases
Xiaofeng Zhou Comment Closed publications, research directions
We present the ARCHIMEDES system for efficient query processing over probabilistic knowledge bases. We design ARCHIMEDES for knowledge bases containing incomplete and uncertain information due to limitations of information sources and human knowledge. Answering queries over these knowledge bases requires efficient probabilistic inference. In this paper, we describe ARCHIMEDES’s efficient knowledge expansion and query-driven inference over UDA-GIST, an
-
Extracting Visual Knowledge from the Web with Multimodal Learning
Dihong Gong Comment Closed publications, research directions
We consider the problem of automatically extracting visual objects from web images. Despite the extraordinary advancement in deep learning, visual object detection remains a challenging task. To overcome the deficiency of pure visual techniques, we propose to make use of meta text surrounding images on the Web for enhanced detection accuracy. In this work we present
-
Interactive Inference for Information Extraction
sean Comment Closed Uncategorized
Introduction In order to keep up with the enormous pace of data being produced on the web, we need automatic methods for converting it into a more structured form for analyzing and querying. For text data this amounts to generating labels which describe the underlying semantic concept and is known as Information Extraction (IE). Examples
-
SigmaKB VLDB16 Demo System and TAC KBP Slot Filling Validation 2016
Miguel Rodriguez Comment Closed Uncategorized
Miguel Rodriguez The amount of information available on the web has motivated a number of efforts in creating large-scale knowledge bases (KBs), each with their own methods of automatically extracting relevant information from unstructured text. Despite sharing the same data model, each project is unique, displaying their own strengths and weaknesses related to the size
-
The ArchimedesOne Knowledge Base System
yang Comment Closed publications, research directions
Yang Chen, Xiaofeng Zhou Recent development in information extraction and data management systems arouses elevating efforts in constructing large knowledge bases (KBs). These knowledge bases store information in a structured format, facilitating efficient processing and querying. Examples of these knowledge bases include DBpedia, DeepDive, Freebase, Google Knowledge Graph, Knowledge Vault, NELL, OpenIE, ProBase, ProbKB, and
-
NIST 2015 Knowledge Base Population – Ensemble
Miguel Rodriguez Comment Closed NIST and open eval, research directions
Miguel Rodriguez The Text Analysis Conference (TAC) is a series of evaluation workshops organized by NIST to encourage research in Natural Language Processing and related applications. TAC is focused on Knowledge Base Population (KBP), automated systems that discover information about entities found in a large corpus and incorporate them into a knowledge base. The TAC-KBP evaluation is composed
Sponsors
