39 research outputs found
The Challenge of Unifying Semantic and Syntactic Inference Restrictions
While syntactic inference restrictions don't play an important role for SAT, they are an essential reasoning technique for more expressive logics, such as first-order logic, or fragments thereof. In particular, they can result in short proofs or model representations. On the other hand, semantically guided inference systems enjoy important properties, such as the generation of solely non-redundant clauses. I discuss to what extend the two paradigms may be unifiable
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LexMa: Tabular data to knowledge graph matching using lexical techniques
With the fundamentals of lives dependent upon the extensive use of the internet-based searches for common life items, there is an ever-growing demand of the quick and meaningful search query systems. This has given the rise of the concept called Semantic Web. There are many challenges in developing the Semantic Web however one fundamental challenge is to design systems to enable the semantic access to the information in tabular data (e.g., Web tables). In this paper, we discuss one such system which has been developed for the automatic annotation of the tabular data using a knowledge graph. We call this system LexMa. Our system is based on lexical matching techniques. LexMa has participated in the Semantic Web Challenge on Tabular Data to Knowledge Graph Matching (SemTab 2020)
Efficient Sketching Algorithm for Sparse Binary Data
Recent advancement of the WWW, IOT, social network, e-commerce, etc. have
generated a large volume of data. These datasets are mostly represented by high
dimensional and sparse datasets. Many fundamental subroutines of common data
analytic tasks such as clustering, classification, ranking, nearest neighbour
search, etc. scale poorly with the dimension of the dataset. In this work, we
address this problem and propose a sketching (alternatively, dimensionality
reduction) algorithm -- \binsketch (Binary Data Sketch) -- for sparse binary
datasets. \binsketch preserves the binary version of the dataset after
sketching and maintains estimates for multiple similarity measures such as
Jaccard, Cosine, Inner-Product similarities, and Hamming distance, on the same
sketch. We present a theoretical analysis of our algorithm and complement it
with extensive experimentation on several real-world datasets. We compare the
performance of our algorithm with the state-of-the-art algorithms on the task
of mean-square-error and ranking. Our proposed algorithm offers a comparable
accuracy while suggesting a significant speedup in the dimensionality reduction
time, with respect to the other candidate algorithms. Our proposal is simple,
easy to implement, and therefore can be adopted in practice
280 Birds with One Stone: Inducing Multilingual Taxonomies from Wikipedia using Character-level Classification
We propose a simple, yet effective, approach towards inducing multilingual
taxonomies from Wikipedia. Given an English taxonomy, our approach leverages
the interlanguage links of Wikipedia followed by character-level classifiers to
induce high-precision, high-coverage taxonomies in other languages. Through
experiments, we demonstrate that our approach significantly outperforms the
state-of-the-art, heuristics-heavy approaches for six languages. As a
consequence of our work, we release presumably the largest and the most
accurate multilingual taxonomic resource spanning over 280 languages
The Challenge of Unifying Semantic and Syntactic Inference Restrictions
International audienceWhile syntactic inference restrictions don't play an important role for SAT, they are an essential reasoning technique for more expressive logics, such as first-order logic, or fragments thereof. In particular, they can result in short proofs or model representations. On the other hand, semantically guided inference systems enjoy important properties, such as the generation of solely non-redundant clauses. I discuss to what extend the two paradigms may be unifiable
Taxonomy Induction using Hypernym Subsequences
We propose a novel, semi-supervised approach towards domain taxonomy
induction from an input vocabulary of seed terms. Unlike all previous
approaches, which typically extract direct hypernym edges for terms, our
approach utilizes a novel probabilistic framework to extract hypernym
subsequences. Taxonomy induction from extracted subsequences is cast as an
instance of the minimumcost flow problem on a carefully designed directed
graph. Through experiments, we demonstrate that our approach outperforms
stateof- the-art taxonomy induction approaches across four languages.
Importantly, we also show that our approach is robust to the presence of noise
in the input vocabulary. To the best of our knowledge, no previous approaches
have been empirically proven to manifest noise-robustness in the input
vocabulary