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Extracting Common Sense Knowledge from Text for Robot Planning
Extracting Common Sense Knowledge from Text for Robot Planning Peter Kaiser1 Mike Lewis2 Ronald P. A. Petrick2 Tamim Asfour1 Mark Steedman2 Abstract— Autonomous robots often require domain knowl- edge to act intelligently in their environment. This is particu- larly true for robots that use automated planning techniques, which require symbolic representations of the operating en- vironment and the robot’s capabilities. However, the task of specifying domain knowledge by hand is tedious and prone to error. As a result, we aim to automate the process of acquiring general common sense knowledge of objects, relations, and actions, by extracting such information from large amounts of natural language text, written by humans for human readers. We present two methods for knowledge acquisition, requiring Fig. 1: The humanoid robots ARMAR-IIIa (left) and only limited human input, which focus on the inference of ARMAR-IIIb working in a kitchen environment ([5], [6]). spatial relations from text. Although our approach is applicable to a range of domains and information, we only consider one type of knowledge here, namely object locations in a kitchen environment. As a proof of concept, we test our approach using domain knowledge based on information gathered from an automated planner and show how the addition of common natural language texts. These methods will provide the set sense knowledge can improve the quality of the generated plans. of object and action types for the domain, as well as certain I. INTRODUCTION AND RELATED WORK relations between entities of these types, of the kind that are commonly used in planning. As an evaluation, we build Autonomous robots that use automated planning to make a domain for a robot working in a kitchen environment decisions about how to act in the world require symbolic (see Fig. -
Open Mind Common Sense: Knowledge Acquisition from the General Public
Open Mind Common Sense: Knowledge Acquisition from the General Public Push Singh, Grace Lim, Thomas Lin, Erik T. Mueller Travell Perkins, Mark Tompkins, Wan Li Zhu MIT Media Laboratory 20 Ames Street Cambridge, MA 02139 USA {push, glim, tlin, markt, wlz}@mit.edu, [email protected], [email protected] Abstract underpinnings for commonsense reasoning (Shanahan Open Mind Common Sense is a knowledge acquisition 1997), there has been far less work on finding ways to system designed to acquire commonsense knowledge from accumulate the knowledge to do so in practice. The most the general public over the web. We describe and evaluate well-known attempt has been the Cyc project (Lenat 1995) our first fielded system, which enabled the construction of which contains 1.5 million assertions built over 15 years a 400,000 assertion commonsense knowledge base. We at the cost of several tens of millions of dollars. then discuss how our second-generation system addresses Knowledge bases this large require a tremendous effort to weaknesses discovered in the first. The new system engineer. With the exception of Cyc, this problem of scale acquires facts, descriptions, and stories by allowing has made efforts to study and build commonsense participants to construct and fill in natural language knowledge bases nearly non-existent within the artificial templates. It employs word-sense disambiguation and intelligence community. methods of clarifying entered knowledge, analogical inference to provide feedback, and allows participants to validate knowledge and in turn each other. Turning to the general public 1 In this paper we explore a possible solution to this Introduction problem of scale, based on one critical observation: Every We would like to build software agents that can engage in ordinary person has common sense of the kind we want to commonsense reasoning about ordinary human affairs. -
Commonsense Knowledge Base Completion with Structural and Semantic Context
Commonsense Knowledge Base Completion with Structural and Semantic Context Chaitanya Malaviya}, Chandra Bhagavatula}, Antoine Bosselut}|, Yejin Choi}| }Allen Institute for Artificial Intelligence |University of Washington fchaitanyam,[email protected], fantoineb,[email protected] Abstract MotivatedByGoal prevent go to tooth Automatic KB completion for commonsense knowledge dentist decay eat graphs (e.g., ATOMIC and ConceptNet) poses unique chal- candy lenges compared to the much studied conventional knowl- HasPrerequisite edge bases (e.g., Freebase). Commonsense knowledge graphs Causes brush use free-form text to represent nodes, resulting in orders of your tooth HasFirstSubevent Causes magnitude more nodes compared to conventional KBs ( ∼18x tooth bacteria decay more nodes in ATOMIC compared to Freebase (FB15K- pick 237)). Importantly, this implies significantly sparser graph up your toothbrush structures — a major challenge for existing KB completion ReceivesAction methods that assume densely connected graphs over a rela- HasPrerequisite tively smaller set of nodes. good Causes breath NotDesires In this paper, we present novel KB completion models that treat by can address these challenges by exploiting the structural and dentist infection semantic context of nodes. Specifically, we investigate two person in cut key ideas: (1) learning from local graph structure, using graph convolutional networks and automatic graph densifi- cation and (2) transfer learning from pre-trained language Figure 1: Subgraph from ConceptNet illustrating semantic models to knowledge graphs for enhanced contextual rep- diversity of nodes. Dashed blue lines represent potential resentation of knowledge. We describe our method to in- edges to be added to the graph. corporate information from both these sources in a joint model and provide the first empirical results for KB com- pletion on ATOMIC and evaluation with ranking metrics on ConceptNet. -
3040 Clairemont Drive Apartments
DATE ISSUED: September 20, 2018 REPORT NO. PC-18-060 HEARING DATE: September 27, 2018 SUBJECT: 3040 CLAIREMONT DRIVE APARTMENTS. Process Four Decision PROJECT NUMBER: 410740 OWNER/APPLICANT: Sam and Sandra Dimenstein Owners/Scott Spencer and Associates Applicant SUMMARY Issue: Should the Planning Commission approve the construction of a 35,240-square-foot, three-story, 19-unit apartment building and a 1,500-square-foot commercial building located at 3040 Clairemont Drive in the Clairemont Mesa Community Planning area. Staff Recommendations: 1. Approve Site Development Permit No. 1835923, and 2. Approve Planned Development Permit No. 143717, and 3. Approve Easement Vacation No. 2189558. Community Planning Group Recommendation: On October 17, 2017 the Clairemont Community Planning Group voted 9-4-0 to recommend approval of the project with no conditions. Environmental Review: The project was determined to be categorically exempt from the California Environmental Quality Act (CEQA) pursuant to Section 15332 (In-Fill Development Projects). This project is not pending an appeal of the environmental determination. The environmental exemption determination for this project was made on April 19, 2018, and the opportunity to appeal that determination ended on May 3, 2018. Fiscal Impact Statement: None with this action. All costs associated with the processing of the project are paid from a deposit account maintained by the applicant. Code Enforcement Impact: None. Housing Impact Statement: The Clairemont Mesa Community Plan designates the 3.318- acre site for mixed use development. The project would comply with that designation and is proposing 19 apartment units within the three-story residential building and a one-story commercial building. -
Common Sense Reasoning with the Semantic Web
Common Sense Reasoning with the Semantic Web Christopher C. Johnson and Push Singh MIT Summer Research Program Massachusetts Institute of Technology, Cambridge, MA 02139 [email protected], [email protected] http://groups.csail.mit.edu/dig/2005/08/Johnson-CommonSense.pdf Abstract Current HTML content on the World Wide Web has no real meaning to the computers that display the content. Rather, the content is just fodder for the human eye. This is unfortunate as in fact Web documents describe real objects and concepts, and give particular relationships between them. The goal of the World Wide Web Consortium’s (W3C) Semantic Web initiative is to formalize web content into Resource Description Framework (RDF) ontologies so that computers may reason and make decisions about content across the Web. Current W3C work has so far been concerned with creating languages in which to express formal Web ontologies and tools, but has overlooked the value and importance of implementing common sense reasoning within the Semantic Web. As Web blogging and news postings become more prominent across the Web, there will be a vast source of natural language text not represented as RDF metadata. Common sense reasoning will be needed to take full advantage of this content. In this paper we will first describe our work in converting the common sense knowledge base, ConceptNet, to RDF format and running N3 rules through the forward chaining reasoner, CWM, to further produce new concepts in ConceptNet. We will then describe an example in using ConceptNet to recommend gift ideas by analyzing the contents of a weblog. -
Conceptnet 5.5: an Open Multilingual Graph of General Knowledge
Proceedings of the Thirty-First AAAI Conference on Artificial Intelligence (AAAI-17) ConceptNet 5.5: An Open Multilingual Graph of General Knowledge Robyn Speer Joshua Chin Catherine Havasi Luminoso Technologies, Inc. Union College Luminoso Technologies, Inc. 675 Massachusetts Avenue 807 Union St. 675 Massachusetts Avenue Cambridge, MA 02139 Schenectady, NY 12308 Cambridge, MA 02139 Abstract In this paper, we will concisely represent assertions such Machine learning about language can be improved by sup- as the above as triples of their start node, relation label, and plying it with specific knowledge and sources of external in- end node: the assertion that “a dog has a tail” can be repre- formation. We present here a new version of the linked open sented as (dog, HasA, tail). data resource ConceptNet that is particularly well suited to ConceptNet also represents links between knowledge re- be used with modern NLP techniques such as word embed- sources. In addition to its own knowledge about the English dings. term astronomy, for example, ConceptNet contains links to ConceptNet is a knowledge graph that connects words and URLs that define astronomy in WordNet, Wiktionary, Open- phrases of natural language with labeled edges. Its knowl- Cyc, and DBPedia. edge is collected from many sources that include expert- The graph-structured knowledge in ConceptNet can be created resources, crowd-sourcing, and games with a pur- particularly useful to NLP learning algorithms, particularly pose. It is designed to represent the general knowledge in- those based on word embeddings, such as (Mikolov et al. volved in understanding language, improving natural lan- 2013). We can use ConceptNet to build semantic spaces that guage applications by allowing the application to better un- are more effective than distributional semantics alone. -
1 Symbols (2286)
1 Symbols (2286) USV Symbol Macro(s) Description 0009 \textHT <control> 000A \textLF <control> 000D \textCR <control> 0022 ” \textquotedbl QUOTATION MARK 0023 # \texthash NUMBER SIGN \textnumbersign 0024 $ \textdollar DOLLAR SIGN 0025 % \textpercent PERCENT SIGN 0026 & \textampersand AMPERSAND 0027 ’ \textquotesingle APOSTROPHE 0028 ( \textparenleft LEFT PARENTHESIS 0029 ) \textparenright RIGHT PARENTHESIS 002A * \textasteriskcentered ASTERISK 002B + \textMVPlus PLUS SIGN 002C , \textMVComma COMMA 002D - \textMVMinus HYPHEN-MINUS 002E . \textMVPeriod FULL STOP 002F / \textMVDivision SOLIDUS 0030 0 \textMVZero DIGIT ZERO 0031 1 \textMVOne DIGIT ONE 0032 2 \textMVTwo DIGIT TWO 0033 3 \textMVThree DIGIT THREE 0034 4 \textMVFour DIGIT FOUR 0035 5 \textMVFive DIGIT FIVE 0036 6 \textMVSix DIGIT SIX 0037 7 \textMVSeven DIGIT SEVEN 0038 8 \textMVEight DIGIT EIGHT 0039 9 \textMVNine DIGIT NINE 003C < \textless LESS-THAN SIGN 003D = \textequals EQUALS SIGN 003E > \textgreater GREATER-THAN SIGN 0040 @ \textMVAt COMMERCIAL AT 005C \ \textbackslash REVERSE SOLIDUS 005E ^ \textasciicircum CIRCUMFLEX ACCENT 005F _ \textunderscore LOW LINE 0060 ‘ \textasciigrave GRAVE ACCENT 0067 g \textg LATIN SMALL LETTER G 007B { \textbraceleft LEFT CURLY BRACKET 007C | \textbar VERTICAL LINE 007D } \textbraceright RIGHT CURLY BRACKET 007E ~ \textasciitilde TILDE 00A0 \nobreakspace NO-BREAK SPACE 00A1 ¡ \textexclamdown INVERTED EXCLAMATION MARK 00A2 ¢ \textcent CENT SIGN 00A3 £ \textsterling POUND SIGN 00A4 ¤ \textcurrency CURRENCY SIGN 00A5 ¥ \textyen YEN SIGN 00A6 -
Analogyspace: Reducing the Dimensionality of Common Sense
Proceedings of the Twenty-Third AAAI Conference on Artificial Intelligence (2008) AnalogySpace: Reducing the Dimensionality of Common Sense Knowledge Robyn Speer Catherine Havasi Henry Lieberman CSAIL Laboratory for Linguistics and Computation Software Agents Group Massachusetts Institute of Technology Brandeis University MIT Media Lab Cambridge, MA 02139, USA Waltham, MA 02454, USA Cambridge, MA 02139, USA [email protected] [email protected] [email protected] Abstract to reduce the dimensionality of that matrix. This results in computing principal components which represent the most We are interested in the problem of reasoning over very large salient aspects of the knowledge, which can then be used to common sense knowledge bases. When such a knowledge base contains noisy and subjective data, it is important to organize it along the most semantically meaningful dimen- have a method for making rough conclusions based on simi- sions. The key idea is that semantic similarity can be deter- larities and tendencies, rather than absolute truth. We present mined using linear operations over the resulting vectors. AnalogySpace, which accomplishes this by forming the ana- logical closure of a semantic network through dimensionality What AnalogySpace Can Do reduction. It self-organizes concepts around dimensions that can be seen as making distinctions such as “good vs. bad” AnalogySpace provides a computationally efficient way to or “easy vs. hard”, and generalizes its knowledge by judging calculate a wide variety of semantically meaningful opera- where concepts lie along these dimensions. An evaluation tions: demonstrates that users often agree with the predicted knowl- AnalogySpace can generalize from sparsely-collected edge, and that its accuracy is an improvement over previous knowledge. -
Arxiv:2005.11787V2 [Cs.CL] 11 Oct 2020 ( ( Hw Estl ...Tetpso Knowledge of Types the W.R.T
Common Sense or World Knowledge? Investigating Adapter-Based Knowledge Injection into Pretrained Transformers Anne Lauscher♣ Olga Majewska♠ Leonardo F. R. Ribeiro♦ Iryna Gurevych♦ Nikolai Rozanov♠ Goran Glavasˇ♣ ♣Data and Web Science Group, University of Mannheim, Germany ♠Wluper, London, United Kingdom ♦Ubiquitous Knowledge Processing (UKP) Lab, TU Darmstadt, Germany {anne,goran}@informatik.uni-mannheim.de {olga,nikolai}@wluper.com www.ukp.tu-darmstadt.de Abstract (Mikolov et al., 2013; Pennington et al., 2014) – neural LMs still only “consume” the distributional Following the major success of neural lan- information from large corpora. Yet, a number of guage models (LMs) such as BERT or GPT-2 structured knowledge sources exist – knowledge on a variety of language understanding tasks, bases (KBs) (Suchanek et al., 2007; Auer et al., recent work focused on injecting (structured) knowledge from external resources into 2007) and lexico-semantic networks (Miller, these models. While on the one hand, joint 1995; Liu and Singh, 2004; Navigli and Ponzetto, pre-training (i.e., training from scratch, adding 2010) – encoding many types of knowledge that objectives based on external knowledge to the are underrepresented in text corpora. primary LM objective) may be prohibitively Starting from this observation, most recent computationally expensive, post-hoc fine- efforts focused on injecting factual (Zhang et al., tuning on external knowledge, on the other 2019; Liu et al., 2019a; Peters et al., 2019) and hand, may lead to the catastrophic forgetting of distributional knowledge. In this work, linguistic knowledge (Lauscher et al., 2019; we investigate models for complementing Peters et al., 2019) into pretrained LMs and the distributional knowledge of BERT with demonstrated the usefulness of such knowledge conceptual knowledge from ConceptNet in language understanding tasks (Wang et al., and its corresponding Open Mind Common 2018, 2019). -
Improving User Experience in Information Retrieval Using Semantic Web and Other Technologies Erfan Najmi Wayne State University
Wayne State University Wayne State University Dissertations 1-1-2016 Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies Erfan Najmi Wayne State University, Follow this and additional works at: https://digitalcommons.wayne.edu/oa_dissertations Part of the Computer Sciences Commons Recommended Citation Najmi, Erfan, "Improving User Experience In Information Retrieval Using Semantic Web And Other Technologies" (2016). Wayne State University Dissertations. 1654. https://digitalcommons.wayne.edu/oa_dissertations/1654 This Open Access Dissertation is brought to you for free and open access by DigitalCommons@WayneState. It has been accepted for inclusion in Wayne State University Dissertations by an authorized administrator of DigitalCommons@WayneState. IMPROVING USER EXPERIENCE IN INFORMATION RETRIEVAL USING SEMANTIC WEB AND OTHER TECHNOLOGIES by ERFAN NAJMI DISSERTATION Submitted to the Graduate School of Wayne State University, Detroit, Michigan in partial fulfillment of the requirements for the degree of DOCTOR OF PHILOSOPHY 2016 MAJOR: COMPUTER SCIENCE Approved By: Advisor Date ⃝c COPYRIGHT BY ERFAN NAJMI 2016 All Rights Reserved ACKNOWLEDGEMENTS I would like to express my heartfelt gratitude to my PhD advisor, Dr. Zaki Malik, for supporting me during these past years. I could not have asked for a better advisor and a friend, one that let me choose my path, help me along it and has always been there if I needed to talk to a friend. I really appreciate all the time he spent and all the patience he showed towards me. Secondly I would like to thank my committee members Dr. Fengwei Zhang, Dr. Alexander Kotov and Dr. Abdelmounaam Rezgui for the constructive feedback and help they provided. -
Farsbase: the Persian Knowledge Graph
Semantic Web 1 (0) 1–5 1 IOS Press 1 1 2 2 3 3 4 FarsBase: The Persian Knowledge Graph 4 5 5 a a a,* 6 Majid Asgari , Ali Hadian and Behrouz Minaei-Bidgoli 6 a 7 Department of Computer Engineering, Iran University of Science and Technology, Tehran, Iran 7 8 8 9 9 10 10 11 11 Abstract. Over the last decade, extensive research has been done on automatically constructing knowledge graphs from web 12 12 resources, resulting in a number of large-scale knowledge graphs such as YAGO, DBpedia, BabelNet, and Wikidata. Despite 13 13 that some of these knowledge graphs are multilingual, they contain few or no linked data in Persian, and do not support tools 14 for extracting Persian information sources. FarsBase is a multi-source knowledge graph especially designed for semantic search 14 15 engines in Persian language. FarsBase uses some hybrid and flexible techniques to extract and integrate knowledge from various 15 16 sources, such as Wikipedia, web tables and unstructured texts. It also supports an entity linking that allow integrating with 16 17 other knowledge bases. In order to maintain a high accuracy for triples, we adopt a low-cost mechanism for verifying candidate 17 18 knowledge by human experts, which are assisted by automated heuristics. FarsBase is being used as the semantic-search system 18 19 of a Persian search engine and efficiently answers hundreds of semantic queries per day. 19 20 20 Keywords: Semantic Web, Linked Date, Persian, Knowledge Graph 21 21 22 22 23 23 24 24 1. -
Multi-Task Learning for Commonsense Reasoning (UNION)
ANA∗ at SemEval-2020 Task 4: mUlti-task learNIng for cOmmonsense reasoNing (UNION) Anandh Perumal, Chenyang Huang, Amine Trabelsi, Osmar R. Za¨ıane Alberta Machine Intelligence Institute, University of Alberta fanandhpe, chenyangh, atrabels,[email protected] Abstract In this paper, we describe our mUlti-task learNIng for cOmmonsense reasoNing (UNION) system submitted for Task C of the SemEval2020 Task 4, which is to generate a reason explaining why a given false statement is non-sensical. However, we found in the early experiments that simple adaptations such as fine-tuning GPT2 often yield dull and non-informative generations (e.g. simple negations). In order to generate more meaningful explanations, we propose UNION, a unified end-to-end framework, to utilize several existing commonsense datasets so that it allows a model to learn more dynamics under the scope of commonsense reasoning. In order to perform model selection efficiently, accurately and promptly, we also propose a couple of auxiliary automatic evaluation metrics so that we can extensively compare the models from different perspectives. Our submitted system not only results in a good performance in the proposed metrics but also outperforms its competitors with the highest achieved score of 2.10 for human evaluation while remaining a BLEU score of 15.7. Our code is made publicly availabled 1. 1 Introduction Common sense reasoning is one of the long-standing problems in natural language understanding. Previous work on modeling common sense knowledge deals mainly with indirect tasks such as co- reference resolution (Sakaguchi et al., 2019), or selecting the plausible situation based on the given subject or scenario (Zellers et al., 2018).