scholarly journals Exploring Knowledge Graphs in an Interpretable Composite Approach for Text Entailment

2019 ◽  
Vol 33 ◽  
pp. 7023-7030
Author(s):  
Vivian S. Silva ◽  
André Freitas ◽  
Siegfried Handschuh
Keyword(s):  
Question Answering ◽  
Text Summarization ◽  
World Knowledge ◽  
Semantic Relationship ◽  
Syntactic Variation ◽  
Semantic Relationships ◽  
Composite Approach ◽  
Textual Entailment ◽  
Knowledge Graphs ◽  
Recognizing Textual Entailment

Recognizing textual entailment is a key task for many semantic applications, such as Question Answering, Text Summarization, and Information Extraction, among others. Entailment scenarios can range from a simple syntactic variation to more complex semantic relationships between pieces of text, but most approaches try a one-size-fits-all solution that usually favors some scenario to the detriment of another. We propose a composite approach for recognizing text entailment which analyzes the entailment pair to decide whether it must be resolved syntactically or semantically. We also make the answer interpretable: whenever an entailment is solved semantically, we explore a knowledge base composed of structured lexical definitions to generate natural language humanlike justifications, explaining the semantic relationship holding between the pieces of text. Besides outperforming wellestablished entailment algorithms, our composite approach gives an important step towards Explainable AI, using world knowledge to make the semantic reasoning process explicit and understandable.

Semantic Methods for Textual Entailment

2012 ◽  
pp. 479-494
Author(s):  
Andrew Neel ◽  
Max H. Garzon
Keyword(s):  
Question Answering ◽  
Word Sense Disambiguation ◽  
Word Sense ◽  
World Knowledge ◽  
New Approach ◽  
Sense Disambiguation ◽  
Textual Entailment ◽  
Benchmark Datasets ◽  
Previous Solution ◽  
Recognizing Textual Entailment

The problem of recognizing textual entailment (RTE) has been recently addressed using syntactic and lexical models with some success. Here, a new approach is taken to apply world knowledge in much the same way as humans, but captured in large semantic graphs such as WordNet. We show that semantic graphs made of synsets and selected relationships between them enable fairly simple methods that provide very competitive performance. First, assuming a solution to word sense disambiguation, we report on the performance of these methods in four basic areas: information retrieval (IR), information extraction (IE), question answering (QA), and multi-document summarization (SUM), as described using benchmark datasets designed to test the entailment problem in the 2006 Recognizing Textual Entailment (RTE-2) challenge. We then show how the same methods yield a solution to word sense disambiguation, which combined with the previous solution, yields a fully automated solution with about the same performance. We then evaluate this solution on two subsequent RTE Challenge datasets. Finally, we evaluate the contribution of WordNet to provide world knowledge. We conclude that the protocol itself works well at solving entailment given a quality source of world knowledge, but WordNet is not able to provide enough information to resolve entailment with this inclusion protocol.

Recognizing textual entailment: Rational, evaluation and approaches

2009 ◽  
Vol 15 (4) ◽  
pp. i-xvii ◽  
Author(s):  
IDO DAGAN ◽  
BILL DOLAN ◽  
BERNARDO MAGNINI ◽  
DAN ROTH
Keyword(s):  
Natural Language ◽  
Question Answering ◽  
Natural Language Understanding ◽  
Language Understanding ◽  
Knowledge Representations ◽  
Semantic Relationships ◽  
Textual Entailment ◽  
Inference Methods ◽  
Recognizing Textual Entailment ◽  
Core Problem

AbstractThe goal of identifying textual entailment – whether one piece of text can be plausibly inferred from another – has emerged in recent years as a generic core problem in natural language understanding. Work in this area has been largely driven by the PASCAL Recognizing Textual Entailment (RTE) challenges, which are a series of annual competitive meetings. The current work exhibits strong ties to some earlier lines of research, particularly automatic acquisition of paraphrases and lexical semantic relationships and unsupervised inference in applications such as question answering, information extraction and summarization. It has also opened the way to newer lines of research on more involved inference methods, on knowledge representations needed to support this natural language understanding challenge and on the use of learning methods in this context. RTE has fostered an active and growing community of researchers focused on the problem of applied entailment. This special issue of the JNLE provides an opportunity to showcase some of the most important work in this emerging area.

scholarly journals KVQA: Knowledge-Aware Visual Question Answering

2019 ◽  
Vol 33 ◽  
pp. 8876-8884 ◽  
Author(s):  
Sanket Shah ◽  
Anand Mishra ◽  
Naganand Yadati ◽  
Partha Pratim Talukdar
Keyword(s):  
Language Processing ◽  
Question Answering ◽  
State Of The Art ◽  
World Knowledge ◽  
Named Entities ◽  
Commonsense Knowledge ◽  
White House ◽  
Visual Question Answering ◽  
Knowledge Graphs ◽  
Answering Questions

Visual Question Answering (VQA) has emerged as an important problem spanning Computer Vision, Natural Language Processing and Artificial Intelligence (AI). In conventional VQA, one may ask questions about an image which can be answered purely based on its content. For example, given an image with people in it, a typical VQA question may inquire about the number of people in the image. More recently, there is growing interest in answering questions which require commonsense knowledge involving common nouns (e.g., cats, dogs, microphones) present in the image. In spite of this progress, the important problem of answering questions requiring world knowledge about named entities (e.g., Barack Obama, White House, United Nations) in the image has not been addressed in prior research. We address this gap in this paper, and introduce KVQA – the first dataset for the task of (world) knowledge-aware VQA. KVQA consists of 183K question-answer pairs involving more than 18K named entities and 24K images. Questions in this dataset require multi-entity, multi-relation, and multi-hop reasoning over large Knowledge Graphs (KG) to arrive at an answer. To the best of our knowledge, KVQA is the largest dataset for exploring VQA over KG. Further, we also provide baseline performances using state-of-the-art methods on KVQA.

scholarly journals DEEP LEARNING FOR SEMANTIC MATCHING: A SURVEY

2021 ◽  
Vol 37 (4) ◽  
pp. 365-402
Author(s):  
Han Li ◽  
Yash Govind ◽  
Sidharth Mudgal ◽  
Theodoros Rekatsinas ◽  
AnHai Doan
Keyword(s):  
Deep Learning ◽  
Semantic Web ◽  
Question Answering ◽  
Ontology Matching ◽  
Coreference Resolution ◽  
Semantic Matching ◽  
Entity Linking ◽  
Matching Problem ◽  
Semantic Relationships ◽  
Textual Entailment

Semantic matching finds certain types of semantic relationships among schema/data constructs. Examples include entity matching, entity linking, coreference resolution, schema/ontology matching, semantic text similarity, textual entailment, question answering, tagging, etc. Semantic matching has received much attention in the database, AI, KDD, Web, and Semantic Web communities. Recently, many works have also applied deep learning (DL) to semantic matching. In this paper we survey this fast growing topic. We define the semantic matching problem, categorize its variations into a taxonomy, and describe important applications. We describe DL solutions for important variations of semantic matching. Finally, we discuss future R\&D directions.

scholarly journals A Survey of Techniques for Constructing Chinese Knowledge Graphs and Their Applications

2018 ◽  
Vol 10 (9) ◽  
pp. 3245 ◽  
Author(s):  
Tianxing Wu ◽  
Guilin Qi ◽  
Cheng Li ◽  
Meng Wang
Keyword(s):  
Artificial Intelligence ◽  
Question Answering ◽  
Knowledge Representation And Reasoning ◽  
Knowledge Graph ◽  
Development History ◽  
One Belt One Road ◽  
History Of ◽  
Knowledge Graphs ◽  
The One ◽  
The Impact

With the continuous development of intelligent technologies, knowledge graph, the backbone of artificial intelligence, has attracted much attention from both academic and industrial communities due to its powerful capability of knowledge representation and reasoning. In recent years, knowledge graph has been widely applied in different kinds of applications, such as semantic search, question answering, knowledge management and so on. Techniques for building Chinese knowledge graphs are also developing rapidly and different Chinese knowledge graphs have been constructed to support various applications. Under the background of the “One Belt One Road (OBOR)” initiative, cooperating with the countries along OBOR on studying knowledge graph techniques and applications will greatly promote the development of artificial intelligence. At the same time, the accumulated experience of China in developing knowledge graphs is also a good reference to develop non-English knowledge graphs. In this paper, we aim to introduce the techniques of constructing Chinese knowledge graphs and their applications, as well as analyse the impact of knowledge graph on OBOR. We first describe the background of OBOR, and then introduce the concept and development history of knowledge graph and typical Chinese knowledge graphs. Afterwards, we present the details of techniques for constructing Chinese knowledge graphs, and demonstrate several applications of Chinese knowledge graphs. Finally, we list some examples to explain the potential impacts of knowledge graph on OBOR.

Sign in / Sign up

Export Citation Format

Share Document