Hierarchical refinement and representation of the causal relation

Terminology ◽  
2002 ◽  
Vol 8 (1) ◽  
pp. 91-111 ◽  
Author(s):  
Caroline Barrière
Keyword(s):  
Knowledge Base ◽  
Causal Relation ◽  
Semantic Relation ◽  
Automatic Extraction ◽  
Conceptual Graphs ◽  
General Study ◽  
Manual Search ◽  
Degree Of Certainty ◽  
Representation Formalism

This research looks at the complexity inherent in the causal relation and the implications for its representation in a Terminological Knowledge Base (TKB). Supported by a more general study of semantic relation hierarchies, a hierarchical refinement of the causal relation is proposed. It results from a manual search of a corpus which shows that it efficiently captures and formalizes variations expressed in text. The feasibility of determining such categorization during automatic extraction from corpora is also explored. Conceptual graphs are used as a representation formalism to which we have added certainty information to capture the degree of certainty surrounding the interaction between two terms involved in a causal relation.

scholarly journals Expert Information Automatic Extraction for IOT Knowledge Base

2019 ◽  
Vol 147 ◽  
pp. 288-294 ◽  
Author(s):  
Lu Yi ◽  
Rao Yuan ◽  
Sun Long ◽  
Li Xue
Keyword(s):  
Knowledge Base ◽  
Automatic Extraction ◽  
Expert Information

Automatic Extraction of Function Knowledge From Text

2015 ◽  
Author(s):  
Hyunmin Cheong ◽  
Wei Li ◽  
Adrian Cheung ◽  
Andy Nogueira ◽  
Francesco Iorio
Keyword(s):  
Natural Language ◽  
Knowledge Base ◽  
Energy Flow ◽  
Automatic Extraction ◽  
Natural Language Text ◽  
Functional Basis ◽  
Language Text

This paper presents a method to automatically extract function knowledge from natural language text. Our method uses syntactic rules to extract subject-verb-object triplets from parsed text. We then leverage the Functional Basis taxonomy, WordNet, and word2vec to classify the triplets as artifact-function-energy flow knowledge. For evaluation, we compare the function definitions associated with 30 most frequent artifacts compiled in a human-constructed knowledge base, Oregon State University’s Design Repository (DR), to those extracted using our method from 4953 Wikipedia pages classified under the category “Machines”. Our method found function definitions for 66% of the test artifacts. For those artifacts found, our method identified 50% of the function definitions compiled in DR. In addition, 75% of the most frequent function definitions found by our method were also defined in DR. The results demonstrate the promising potential of our method in automatic extraction of function knowledge.

scholarly journals Exploiting Semantic Role Resources for Preposition Disambiguation

2009 ◽  
Vol 35 (2) ◽  
pp. 151-184 ◽  
Author(s):  
Tom O'Hara ◽  
Janyce Wiebe
Keyword(s):  
Knowledge Base ◽  
Word Sense Disambiguation ◽  
Knowledge Bases ◽  
Conceptual Graphs ◽  
Semantic Role ◽  
Word Sense ◽  
Prepositional Phrases ◽  
Sense Disambiguation ◽  
New Type ◽  
The Common

This article describes how semantic role resources can be exploited for preposition disambiguation. The main resources include the semantic role annotations provided by the Penn Treebank and FrameNet tagged corpora. The resources also include the assertions contained in the Factotum knowledge base, as well as information from Cyc and Conceptual Graphs. A common inventory is derived from these in support of definition analysis, which is the motivation for this work. The disambiguation concentrates on relations indicated by prepositional phrases, and is framed as word-sense disambiguation for the preposition in question. A new type of feature for word-sense disambiguation is introduced, using WordNet hypernyms as collocations rather than just words. Various experiments over the Penn Treebank and FrameNet data are presented, including prepositions classified separately versus together, and illustrating the effects of filtering. Similar experimentation is done over the Factotum data, including a method for inferring likely preposition usage from corpora, as knowledge bases do not generally indicate how relationships are expressed in English (in contrast to the explicit annotations on this in the Penn Treebank and FrameNet). Other experiments are included with the FrameNet data mapped into the common relation inventory developed for definition analysis, illustrating how preposition disambiguation might be applied in lexical acquisition.

scholarly journals Integrating problem solving and knowledge construction through dual mapping

2012 ◽  
pp. 248-257
Keyword(s):  
Medical Education ◽  
Problem Solving ◽  
Learning Environment ◽  
Knowledge Base ◽  
Study Design ◽  
Cognitive Processes ◽  
Knowledge Construction ◽  
General Study ◽  
Important Approach ◽  
Dual Mapping

Learning through problem solving has been regarded as an important approach to constructivist learning. However, how practice and knowledge reciprocate each other has not been sufficiently examined and remains implicit. Although problem-based learning is increasingly used in medical education and other domains, there is a concern about its weakness in general study design in relation to its impact on learners’ knowledge base. Considering the complex cognitive processes involved in learning through problem solving, this study proposes a dual mapping learning environment, serving as a visual affordance for improving problem solving and underlying knowledge construction processes as well as the transformation between the two.

scholarly journals Hypergraph Convolutional Network for Multi-Hop Knowledge Base Question Answering (Student Abstract)

2020 ◽  
Vol 34 (10) ◽  
pp. 13801-13802
Author(s):  
Jiale Han ◽  
Bo Cheng ◽  
Xu Wang
Keyword(s):  
Knowledge Base ◽  
Question Answering ◽  
Semantic Information ◽  
Semantic Relation ◽  
Experimental Results ◽  
Data Correlation ◽  
Convolutional Network ◽  
Related Data ◽  
Convolutional Networks ◽  
High Level

Graph convolutional networks (GCN) have been applied in knowledge base question answering (KBQA) task. However, the pairwise connection between nodes of GCN limits the representation capability of high-order data correlation. Furthermore, most previous work does not fully utilize the semantic relation information, which is vital to reasoning. In this paper, we propose a novel multi-hop KBQA model based on hypergraph convolutional network. By constructing a hypergraph, the form of pairwise connection between nodes and nodes is converted to the high-level connection between nodes and edges, which effectively encodes complex related data. To better exploit the semantic information of relations, we apply co-attention method to learn similarity between relation and query, and assign weights to different relations. Experimental results demonstrate the effectivity of the model.

Investigating the causal relation in informative texts

Terminology ◽  
2001 ◽  
Vol 7 (2) ◽  
pp. 135-154 ◽  
Author(s):  
Caroline Barrière
Keyword(s):  
Causal Relation ◽  
Domain Model ◽  
Automatic Extraction ◽  
Causal Relations ◽  
Usage Frequency ◽  
Dynamic Dimension

Our work investigates the causal relation as it is expressed in informative texts. We view causal relations as important because of the dynamic dimension they bring to a domain model. Thorough study of a corpus leads us to distinguish two prominent classes of indicators of the causal relation: conjunctional phrases, and verbs. This paper identifies multiple knowledge-rich patterns within each class and studies their usage, frequency and noise. Results from this manual investigation informs a discussion on the feasibility of automatic extraction of the different forms of expression of the causal relation.

scholarly journals European clinical guidelines for Tourette syndrome and other tic disorders—version 2.0. Part II: psychological interventions

2021 ◽  
Author(s):  
Per Andrén ◽  
Ewgeni Jakubovski ◽  
Tara L. Murphy ◽  
Katrin Woitecki ◽  
Zsanett Tarnok ◽  
...  
Keyword(s):  
Tourette Syndrome ◽  
Clinical Guidelines ◽  
Literature Search ◽  
Tic Disorders ◽  
Psychological Interventions ◽  
Reversal Training ◽  
Systematic Literature Search ◽  
Manual Search ◽  
Habit Reversal Training ◽  
Degree Of Certainty

AbstractPart II of the European clinical guidelines for Tourette syndrome and other tic disorders (ECAP journal, 2011) provides updated information and recommendations for psychological interventions for individuals with tic disorders, created by a working group of the European Society for the Study of Tourette Syndrome (ESSTS). A systematic literature search was conducted to obtain original studies of psychological interventions for tic disorders, published since the initial European clinical guidelines were issued. Relevant studies were identified using computerized searches of the MEDLINE and PsycINFO databases for the years 2011–2019 and a manual search for the years 2019–2021. Based on clinical consensus, psychoeducation is recommended as an initial intervention regardless of symptom severity. According to a systematic literature search, most evidence was found for Habit Reversal Training (HRT), primarily the expanded package Comprehensive Behavioral Intervention for Tics (CBIT). Evidence was also found for Exposure and Response Prevention (ERP), but to a lesser degree of certainty than HRT/CBIT due to fewer studies. Currently, cognitive interventions and third-wave interventions are not recommended as stand-alone treatments for tic disorders. Several novel treatment delivery formats are currently being evaluated, of which videoconference delivery of HRT/CBIT has the most evidence to date. To summarize, when psychoeducation alone is insufficient, both HRT/CBIT and ERP are recommended as first-line interventions for tic disorders. As part of the development of the clinical guidelines, a survey is reported from ESSTS members and other tic disorder experts on preference, use and availability of psychological interventions for tic disorders.

scholarly journals The role of semantics in mining frequent patterns from knowledge bases in description logics with rules

2010 ◽  
Vol 10 (3) ◽  
pp. 251-289 ◽  
Author(s):  
JOANNA JÓZEFOWSKA ◽  
AGNIESZKA ŁAWRYNOWICZ ◽  
TOMASZ ŁUKASZEWSKI
Keyword(s):  
Data Mining ◽  
Semantic Web ◽  
Knowledge Base ◽  
Description Logics ◽  
Pattern Discovery ◽  
Knowledge Bases ◽  
Frequent Pattern ◽  
Frequent Patterns ◽  
Representation Formalism

AbstractWe propose a new method for mining frequent patterns in a language that combines both Semantic Web ontologies and rules. In particular, we consider the setting of using a language that combines description logics (DLs) with DL-safe rules. This setting is important for the practical application of data mining to the Semantic Web. We focus on the relation of the semantics of the representation formalism to the task of frequent pattern discovery, and for the core of our method, we propose an algorithm that exploits the semantics of the combined knowledge base. We have developed a proof-of-concept data mining implementation of this. Using this we have empirically shown that using the combined knowledge base to perform semantic tests can make data mining faster by pruning useless candidate patterns before their evaluation. We have also shown that the quality of the set of patterns produced may be improved: the patterns are more compact, and there are fewer patterns. We conclude that exploiting the semantics of a chosen representation formalism is key to the design and application of (onto-)relational frequent pattern discovery methods.

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