I-SEMANTICS '08: International Conference on Semantic Systems
Total Page:16
File Type:pdf, Size:1020Kb
Load more
Recommended publications
-
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. -
RDF Query Languages Need Support for Graph Properties
RDF Query Languages Need Support for Graph Properties Renzo Angles1, Claudio Gutierrez1, and Jonathan Hayes1,2 1 Dept. of Computer Science, Universidad de Chile 2 Dept. of Computer Science, Technische Universit¨at Darmstadt, Germany {rangles,cgutierr,jhayes}@dcc.uchile.cl Abstract. This short paper discusses the need to include into RDF query languages the ability to directly query graph properties from RDF data. We study the support that current RDF query languages give to these features, to conclude that they are currently not supported. We propose a set of basic graph properties that should be added to RDF query languages and provide evidence for this view. 1 Introduction One of the main features of the Resource Description Framework (RDF) is its ability to interconnect information resources, resulting in a graph-like structure for which connectivity is a central notion [GLMB98]. As we will argue, basic concepts of graph theory such as degree, path, and diameter play an important role for applications that involve RDF querying. Considering the fact that the data model influences the set of operations that should be provided by a query language [HBEV04], it follows the need for graph operations support in RDF query languages. For example, the query “all relatives of degree 1 of Alice”, submitted to a genealogy database, amounts to retrieving the nodes adjacent to a resource. The query “are suspects A and B related?”, submitted to a police database, asks for any path connecting these resources in the (RDF) graph that is stored in this database. The query “what is the Erd˝osnumber of Alberto Mendelzon”, submitted to (a RDF version of) DBLP, asks simply for the length of the shortest path between the nodes representing Erd˝osand Mendelzon. -
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. -
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. -
LNCS 3532, Pp
Activity Based Metadata for Semantic Desktop Search Paul Alexandru Chirita, Rita Gavriloaie, Stefania Ghita, Wolfgang Nejdl, and Raluca Paiu L3S Research Center / University of Hanover, Deutscher Pavillon, Expo Plaza 1, 30539 Hanover, Germany {chirita, gavriloaie, ghita, nejdl, paiu}@l3s.de Abstract. With increasing storage capacities on current PCs, searching the World Wide Web has ironically become more efficient than searching one’s own per- sonal computer. The recently introduced desktop search engines are a first step towards coping with this problem, but not yet a satisfying solution. The reason for that is that desktop search is actually quite different from its web counter- part. Documents on the desktop are not linked to each other in a way compara- ble to the web, which means that result ranking is poor or even inexistent, be- cause algorithms like PageRank cannot be used for desktop search. On the other hand, desktop search could potentially profit from a lot of implicit and explicit semantic information available in emails, folder hierarchies, browser cache con- texts and others. This paper investigates how to extract and store these activity based context information explicitly as RDF metadata and how to use them, as well as additional background information and ontologies, to enhance desktop search. 1 Introduction The capacity of our hard-disk drives has increased tremendously over the past decade, and so has the number of files we usually store on our computer. It is no wonder that sometimes we cannot find a document any more, even when we know we saved it somewhere. Ironically, in quite a few of these cases nowadays, the document we are looking for can be found faster on the World Wide Web than on our personal com- puter. -
Master Thesis Innovation Dynamics in Open Source Software
Master thesis Innovation dynamics in open source software Author: Name: Remco Bloemen Student number: 0109150 Email: [email protected] Telephone: +316 11 88 66 71 Supervisors and advisors: Name: prof. dr. Stefan Kuhlmann Email: [email protected] Telephone: +31 53 489 3353 Office: Ravelijn RA 4410 (STEPS) Name: dr. Chintan Amrit Email: [email protected] Telephone: +31 53 489 4064 Office: Ravelijn RA 3410 (IEBIS) Name: dr. Gonzalo Ord´o~nez{Matamoros Email: [email protected] Telephone: +31 53 489 3348 Office: Ravelijn RA 4333 (STEPS) 1 Abstract Open source software development is a major driver of software innovation, yet it has thus far received little attention from innovation research. One of the reasons is that conventional methods such as survey based studies or patent co-citation analysis do not work in the open source communities. In this thesis it will be shown that open source development is very accessible to study, due to its open nature, but it requires special tools. In particular, this thesis introduces the method of dependency graph analysis to study open source software devel- opment on the grandest scale. A proof of concept application of this method is done and has delivered many significant and interesting results. Contents 1 Open source software 6 1.1 The open source licenses . 8 1.2 Commercial involvement in open source . 9 1.3 Opens source development . 10 1.4 The intellectual property debates . 12 1.4.1 The software patent debate . 13 1.4.2 The open source blind spot . 15 1.5 Litterature search on network analysis in software development . -
Use Style: Paper Title
IJRECE VOL. 6 ISSUE 2 APR.-JUNE 2018 ISSN: 2393-9028 (PRINT) | ISSN: 2348-2281 (ONLINE) A Review on Reasoning System, Types and Tools and Need for Hybrid Reasoning Goutami M.K1, Mrs. Rashmi S R2, Bharath V3 1M.Tech, Computer Network Engineering, Dept. of Computer Science and Engineering, Dayananda Sagar college of Engineering, Bangalore. (Email: [email protected]) 2Asst. Professor, Dept. of Computer Science and Engineering, Dayananda Sagar college of Engineering , Bangalore. (Email: [email protected]) 3Dept. of Computer Science and Engineering, Dayananda Sagar college of Engineering, Bangalore Abstract— Expert system is a programming system which knowledge base then we require a programming system that utilizes the information of expert knowledge of the specific will facilitate us to access the knowledge for reasoning purpose domain to make decisions. These frameworks are intended to and helps in decision making and problem solving. Inference tackle complex issues by thinking through the learning process engine is an element of the system that applies logical rules to called as reasoning. The knowledge used for reasoning is the knowledge base to deduce new information. User interface represented mainly as if-then rules. Expert system forms a is one through which the user can communicate with the expert significant part in day-to-day life nowadays as it emulates the system. expert’s behavior in analyzing the information and concludes “A reasoning system is a software system that uses available knowledge, applies logical techniques to generate decision. This paper primarily centers on the reasoning system conclusions”. It uses logical techniques such as deduction which plays the fundamental part in field of artificial approach and induction approach. -
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). -
Instructions for the Preparation of a Camera-Ready Paper in MS Word
Review Semantic web; towards Ontology based Web Application Editor(s): Name Surname, University, Country Solicited review(s): Name Surname, University, Country Open review(s): Name Surname, University, Country Zeeshan Ahmed*, Saman Majeed Department of Bioinformatics, Biocenter, University of Wuerzburg, Am Hubland 97074, Wuerzburg Germany Abstract. In this review we address the importance of field i.e. Semantic Web; a mechanism of presenting information over the web in a format so that human being as well as machines can understand the semantic of context. Describing the techno- logical contributions of Semantic Web in detail, we present its main innovation i.e. Ontology, and development supporting technologies i.e. XML (eXtensible Mark-up Language), RDF (Resource Description Framework) and OWL (Web Ontology Language), offering different ways of more explicitly structuring and richly annotating Web pages. Furthermore, in this review paper, we discuss how ontology is contributing to semantic web development with the presentation of some Semantic Web application and conclude with some existing limitations need to be overcome. Keywords: Web, Ontology, Semantic Web 1. Introduction easy to go for scattered extensive information by looking into bookmarked web pages but quite diffi- Targeting the challenge of implementing a web cult to extract a piece of needed information. Al- based system capable of performing semantic based though some search engines and screen scrapers are search to extract desired information from attached invented, search