Knowledge-Based Matching of $n$-ary Tuples

Extending DLR with Labelled Tuples, Projections, Functional Dependencies and Objectification (full version)

We introduce an extension of the n-ary description logic DLR to deal with attribute-labelled tuples (generalising the positional notation), with arbitrary projections of relations (inclusion dependencies), generic functional dependencies and with global and local objectification (reifying relations or their projections). We show how a simple syntactic condition on the appearance of projections and functional dependencies in a knowledge base makes the language decidable without increasing the computational complexity of the basic DLR language.

Text2NKG：面向n元关系知识图谱构建的细粒度n元关系抽取

Text2NKG: Fine-Grained N-ary Relation Extraction for N-ary relational Knowledge Graph Construction

针对现有n元关系知识图谱构建粗粒度、单一模式的局限，提出带异序与输出合并的跨度元组分类框架Text2NKG，支持四种典型NKG模式，在细粒度n元关系抽取基准上F1值达SOTA，代码与数据已公开。

A Decidable Very Expressive Description Logic for Databases (Extended Version)

We introduce $\mathcal{DLR}^+$, an extension of the n-ary propositionally closed description logic $\mathcal{DLR}$ to deal with attribute-labelled tuples (generalising the positional notation), projections of relations, and global and local objectification of relations, able to express inclusion, functional, key, and external uniqueness dependencies. The logic is equipped with both TBox and ABox axioms. We show how a simple syntactic restriction on the appearance of projections sharing common attributes in a $\mathcal{DLR}^+$ knowledge base makes reasoning in the language decidable with the sa…

Road-network-based Rapid Geolocalization

It has always been a research hotspot to use geographic information to assist the navigation of unmanned aerial vehicles. In this paper, a road-network-based localization method is proposed. We match roads in the measurement images to the reference road vector map, and realize successful localization on areas as large as a whole city. The road network matching problem is treated as a point cloud registration problem under two-dimensional projective transformation, and solved under a hypothesise-and-test framework. To deal with the projective point cloud registration problem, a global projectiv…

Domain Knowledge Integration By Gradient Matching For Sample-Efficient Reinforcement Learning

Model-free deep reinforcement learning (RL) agents can learn an effective policy directly from repeated interactions with a black-box environment. However in practice, the algorithms often require large amounts of training experience to learn and generalize well. In addition, classic model-free learning ignores the domain information contained in the state transition tuples. Model-based RL, on the other hand, attempts to learn a model of the environment from experience and is substantially more sample efficient, but suffers from significantly large asymptotic bias owing to the imperfect dynami…

Unsupervised Matching of Data and Text

Entity resolution is a widely studied problem with several proposals to match records across relations. Matching textual content is a widespread task in many applications, such as question answering and search. While recent methods achieve promising results for these two tasks, there is no clear solution for the more general problem of matching textual content and structured data. We introduce a framework that supports this new task in an unsupervised setting for any pair of corpora, being relational tables or text documents. Our method builds a fine-grained graph over the content of the corpo…