DOCSLIB.ORG

Realising the Potential of Algal Biomass Production Through Semantic Web and Linked Data

Total Page：16

File Type：pdf, Size：1020Kb

Download full-text PDF Read full-text
Realising the Potential of Algal Biomass Production Through Semantic Web and Linked Data LEAPS: Realising the Potential of Algal Biomass Production through Semantic Web and Linked data ∗ Monika Solanki Johannes Skarka Craig Chapman KBE Lab Karlsruhe Institute of KBE Lab Birmingham City University Technology (ITAS) Birmingham City University [email protected] [email protected] [email protected] ABSTRACT In order to derive fuels from biomass, algal operation plant Recently algal biomass has been identified as a potential sites are setup that facilitate biomass cultivation and con- source of large scale production of biofuels. Governments, version of the biomass into end use products, some of which environmental research councils and special interests groups are biofuels. Microalgal biomass production in Europe is are funding several efforts that investigate renewable energy seen as a promising option for biofuels production regarding production opportunities in this sector. However so far there energy security and sustainability. Since microalgae can be has been no systematic study that analyses algal biomass cultivated in photobioreactors on non-arable land this tech- potential especially in North-Western Europe. In this paper nology could significantly reduce the food vs. fuel dilemma. we present a spatial data integration and analysis frame- However, until now there has been no systematic analysis work whereby rich datasets from the algal biomass domain of the algae biomass potential for North-Western Europe. that include data about algal operation sites and CO2 source In [20], the authors assessed the resource potential for mi- sites amongst others are semantically enriched with ontolo- croalgal biomass but excluded all areas not between 37◦N gies specifically designed for the domain and made available and 37◦S, thus most of Europe. In their report the IEA as linked data. We then present a conceptual architecture Bioenergy Task 39 [5] point out that there is currently no and a prototype implementation of a GeoWeb service that comprehensive analysis on the resource potential of algal provides querying and analysing capabilities over the linked biomass available and emphasize the need for such a work. datasets. The initiatives of producing biofuels from algae has immense General Terms research and commercial potential. It is also quickly evi- Semantic Web, Linked data, Algal biomass, SPARQL, On- dent that because of extensive research being carried out, tologies the domain itself is a very rich source of information. Most of the knowledge is however largely buried in various for- mats of images, spreadsheets, proprietary data sources and 1. INTRODUCTION grey literature that are not readily machine accessible/in- The last few decades have seen a consistent rise in energy terpretable. A critical limitation that has been identified is and oil prices along with a significant depletion of fossil the lack of a knowledge level infrastructure that is equipped fuel resources. This has led to extensive research in the with the capabilities to provide semantic grounding to the search and production of naturally viable and sustainable datasets for algal biomass so that they can be interlinked, energy sources such as biofuels. Recently the idea that al- shared and reused within the biomass community. gae biomass based biofuels could serve as an alternative to fossil fuels has been embraced by councils across the globe. In this paper our objectives are two fold. Firstly, we moti- Major companies [4, 14], government bodies [19] and dedi- vate the use of Semantic Web [2] and linked data [3,9] tech- cated non-profit organisations such as ABO (Algal Biomass nologies to integrate, analyse and visualise various facets of Organisation) 1 and EABA(European Algal Biomass Asso- 2 data from the algal biomass domain. We believe Semantic ciation) have been pushing the case for research into clean Web and linked data have immense potential to contribute energy sources including algae biomass based biofuels. towards making the systematic analysis of algal biomass po- ∗Principal and corresponding author tential available to stakeholders within a unified framework. 1http://www.algalbiomass.org/ Secondly, we propose to contribute algal biomass knowledge 2http://www.eaba-association.eu/ to the Semantic Web and the growing Web of data by • laying out a set of ontological requirements for knowl- edge representation that support the publication of al- Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are gal biomass data. not made or distributed for profit or commercial advantage and that copies • elaborating on how algal biomass datasets are trans- bear this notice and the full citation on the first page. To copy otherwise, to republish, to post on servers or to redistribute to lists, requires prior specific formed to their corresponding RDF model representa- permission and/or a fee. I-SEMANTICS 2012, 8th Int. Conf. on Semantic tion. Systems, Sept. 5-7, 2012, Graz, Austria Copyright 2012 ACM 978-1-4503- 1112-0 ...$10.00. • interlinking the generated RDF datasets along spatial 117 dimensions with other datasets on the Web of data. • visualising the linked datasets via an end user LOD REST [6] Web service, LEAPS,(Linked Entities for Algal Plant Sites). The paper is structured as follows: Section 2 motivates the idea of using Semantic Web and linked data for al- gal biomass. Section 3 discusses related work. Section 4 presents the requirements for ontological representation of algal biomass knowledge. Section 5 provides an account of the raw datasets and illustrates our methodology for pro- ducing linked data. Section 6 presents the framework and conceptual architecture for LEAPS. Section 7 illustrates an Figure 1: The Algal biomass supply chain example of the queries used in LEAPS and finally Section 8 presents our conclusions. Stage 2 is concerned with the harvesting of algal biomass. Datasets and vocabularies related to harvesting strategies 2. SEMANTIC WEB AND LINKED DATA FOR and extraction techniques are the key semantic outputs of ALGAL BIOMASS this stage. Besides domain specific ontologies, task and ap- One of the key gaps that have been identified within the plication ontologies for the processes involved in this stage algal biomass domain is the lack of a semantically enriched will play a crucial role in modelling knowledge. infrastructure for sharing and reusing knowledge. Stage 3 involves the conversion of biomass to end use prod- An introspection of the algae-to-biofuels lifecycle reveals sev- ucts such as biofuels and other constituents. Marketing of eral layers where Semantic Web standards and linked data the products is also an integral part of this stage. Appli- technologies could be very successfully applied and immensely cation ontologies and product ontologies such as GoodRela- 5 benefit the community. Algal biomass data manifests itself tions will be crucial in describing the datasets for this stage. across several facets. At a very high level, the value chain 6 7 for algal biomass ranges from cultivation of algae to pro- Upper level ontologies such as SUMO and BFO can be duction of biofuels and other products from the cultivated employed as metadata specification at all the three stages. biomass [12]. Each of the core tasks of cultivation, harvest- Regulatory policies or domain specific rules can be specified ing, processing and fuel production further involves several either as part of the vocabulary description using standards 8 9 10 intermediate processes. Besides managing the process level such as OWL 2 RL or as rules using RuleML , SWRL 11 domain knowledge from cultivation to marketing of the end or RIF . We propose to address this as part of our future products, every stage in the algal supply chain is governed work as an extension to LEAPS. by regulatory policies and strategies laid out by local gov- ernment bodies and requirements defined by stakeholders. Linked data applications built to serve the stakeholders rely Each of the facets consumes and produces a large volume on an unambiguous and contextual representation of the do- of unstructured data and information that opens up a huge main knowledge in order to provide reliable and unambigu- potential for semantically grounded information extraction ous answers to data driven questions raised by stakeholders. and knowledge representation technologies to be successfully Below we enumerate a few examples of informal queries that applied. Figure 13 depicts a schematic representation of could be potentially raised against the knowledge in stage 1. the algal biofuel value chain stages and the contributions that Semantic Web and linked data could bring to each of • Which are the algal operation sites with CO sources the stages. As illustrated, at each stage of the value chain 2 that have CO emissions less than 130000 kgs, where datasets can be described using well established as well as 2 total costs of supplying CO is lower then 5000 GBP domain specific ontologies. Besides integrating stage spe- 2 per ton of CO , areal yield is greater than 30 tons per cific and intra-stage datasets, contextually relevant datasets 2 hectare and which are located within the NUTS re- from the LOD cloud4 can also be exploited for interlinking. gion \UKM61"? Supplement the data with supporting information about the region. Stage 1 encompasses the cultivation of algae. It involves • Which are the top ten algal operation sites with the setting up an algal cultivation site and incorporates datasets lowest impact on global warming potential? about location related geographical information about the sites, locations of sources of light, CO2, nutrients, water • For a given algal operation site which are the first five and labour. The linked data for datasets is described using 5http://purl.org/goodrelations/v1 domain specific and spatial ontologies. In this paper we 6http://www.ontologyportal.org/ showcase the publication of linked data for datasets from 7http://www.ifomis.org/bfo stage 1.
Load more

Recommended publications
  • From Cataloguing Cards to Semantic Web 1
    View metadata, citation and similar papers at core.ac.uk brought to you by CORE provided by Scientific Open-access Literature Archive and Repository Archeologia e Calcolatori 20, 2009, 111-128 REPRESENTING KNOWLEDGE IN ARCHAEOLOGY: FROM CATALOGUING CARDS TO SEMANTIC WEB 1. Introduction Representing knowledge is the basis of any catalogue. The Italian Ca- talogue was based on very valuable ideas, developed in the late 1880s, with the basic concept of putting objects in their context. Initially cataloguing was done manually, with typewritten cards. The advent of computers led to some early experimentations, and subsequently to the de�nition of a more formalized representation schema. The web produced a cultural revolution, and made the need for technological and semantic interoperability more evi- dent. The Semantic Web scenario promises to allow a sharing of knowledge, which will make the knowledge that remained unexpressed in the traditional environments available to any user, and allow objects to be placed in their cultural context. In this paper we will brie�y recall the principles of cataloguing and lessons learned by early computer experiences. Subsequently we will descri- be the object model for archaeological items, discussing its strong and weak points. In section 5 we present the web scenario and approaches to represent knowledge. 2. Cataloguing: history and principles The Italian Catalogue of cultural heritage has its roots in the experiences and concepts, developed in the late 1880s and early 1900s, by the famous art historian Adolfo Venturi, who was probably one of the �rst scholars to think explicitly in terms of having a frame of reference to describe works of art, emphasizing as the main issue the context in which the work had been produced.
    [Show full text]
  • Download Slides
    a platform for all that we know savas parastatidis http://savas.me savasp transition from web to apps increasing focus on information (& knowledge) rise of personal digital assistants importance of near-real time processing http://aptito.com/blog/wp-content/uploads/2012/05/smartphone-apps.jpg today... storing computing computers are huge amounts great tools for of data managing indexing example google and microsoft both have copies of the entire web (and more) for indexing purposes tomorrow... storing computing computers are huge amounts great tools for of data managing indexing acquisition discovery aggregation organization we would like computers to of the world’s information also help with the automatic correlation analysis and knowledge interpretation inference data information knowledge intelligence wisdom expert systems watson freebase wolframalpha rdbms google now web indexing data is symbols (bits, numbers, characters) information adds meaning to data through the introduction of relationship - it answers questions such as “who”, “what”, “where”, and “when” knowledge is a description of how the world works - it’s the application of data and information in order to answer “how” questions G. Bellinger, D. Castro, and A. Mills, “Data, Information, Knowledge, and Wisdom,” Inform. pp. 1–4, 2004 web – the data platform web – the information platform web – the knowledge platform foundation for new experiences “wisdom is not a product of schooling but of the lifelong attempt to acquire it” representative examples wolframalpha watson source:
    [Show full text]
  • Knowledge Extraction for Hybrid Question Answering
    KNOWLEDGEEXTRACTIONFORHYBRID QUESTIONANSWERING Von der Fakultät für Mathematik und Informatik der Universität Leipzig angenommene DISSERTATION zur Erlangung des akademischen Grades Doctor rerum naturalium (Dr. rer. nat.) im Fachgebiet Informatik vorgelegt von Ricardo Usbeck, M.Sc. geboren am 01.04.1988 in Halle (Saale), Deutschland Die Annahme der Dissertation wurde empfohlen von: 1. Professor Dr. Klaus-Peter Fähnrich (Leipzig) 2. Professor Dr. Philipp Cimiano (Bielefeld) Die Verleihung des akademischen Grades erfolgt mit Bestehen der Verteidigung am 17. Mai 2017 mit dem Gesamtprädikat magna cum laude. Leipzig, den 17. Mai 2017 bibliographic data title: Knowledge Extraction for Hybrid Question Answering author: Ricardo Usbeck statistical information: 10 chapters, 169 pages, 28 figures, 32 tables, 8 listings, 5 algorithms, 178 literature references, 1 appendix part supervisors: Prof. Dr.-Ing. habil. Klaus-Peter Fähnrich Dr. Axel-Cyrille Ngonga Ngomo institution: Leipzig University, Faculty for Mathematics and Computer Science time frame: January 2013 - March 2016 ABSTRACT Over the last decades, several billion Web pages have been made available on the Web. The growing amount of Web data provides the world’s largest collection of knowledge.1 Most of this full-text data like blogs, news or encyclopaedic informa- tion is textual in nature. However, the increasing amount of structured respectively semantic data2 available on the Web fosters new search paradigms. These novel paradigms ease the development of natural language interfaces which enable end- users to easily access and benefit from large amounts of data without the need to understand the underlying structures or algorithms. Building a natural language Question Answering (QA) system over heteroge- neous, Web-based knowledge sources requires various building blocks.
    [Show full text]
  • Datatone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3
    DataTone: Managing Ambiguity in Natural Language Interfaces for Data Visualization Tong Gao1, Mira Dontcheva2, Eytan Adar1, Zhicheng Liu2, Karrie Karahalios3 1University of Michigan, 2Adobe Research 3University of Illinois, Ann Arbor, MI San Francisco, CA Urbana Champaign, IL fgaotong,[email protected] fmirad,[email protected] [email protected] ABSTRACT to be both flexible and easy to use. General purpose spread- Answering questions with data is a difficult and time- sheet tools, such as Microsoft Excel, focus largely on offer- consuming process. Visual dashboards and templates make ing rich data transformation operations. Visualizations are it easy to get started, but asking more sophisticated questions merely output to the calculations in the spreadsheet. Asking often requires learning a tool designed for expert analysts. a “visual question” requires users to translate their questions Natural language interaction allows users to ask questions di- into operations on the spreadsheet rather than operations on rectly in complex programs without having to learn how to the visualization. In contrast, visual analysis tools, such as use an interface. However, natural language is often ambigu- Tableau,1 creates visualizations automatically based on vari- ous. In this work we propose a mixed-initiative approach to ables of interest, allowing users to ask questions interactively managing ambiguity in natural language interfaces for data through the visualizations. However, because these tools are visualization. We model ambiguity throughout the process of often intended for domain specialists, they have complex in- turning a natural language query into a visualization and use terfaces and a steep learning curve. algorithmic disambiguation coupled with interactive ambigu- Natural language interaction offers a compelling complement ity widgets.
    [Show full text]
  • Directions in AI Research and Applications at Siemens Corporate
    AI Magazine Volume 11 Number 1 (1991)(1990) (© AAAI) Research in Progress of linguistic phenomena. The com- Directions in AI Research putational work concerns questions of adequate processing models and algorithms, as embodied in the and Applications at actual interfaces being developed. These topics are explored in the Siemens Corporate Research framework of three projects: The nat- ural language consulting dialogue and Development project (Wisber) takes up research- oriented topics (descriptive grammar formalisms and linguistically adequate grammar specification, handling of Wolfram Buettner, Klaus Estenfeld, discourse, and so on), the data-access Hans Haugeneder, and Peter Struss project (Sepp) focuses on the practi- cal application of state-of-the-art technology, and the work on gram- mar-development environments ■ Many barriers exist today that prevent particular, Prolog extensions; and (4) (Ape) centers on the problem of ade- effective industrial exploitation of current design and analysis of neural networks. quate tools for specifying linguistic and future AI research. These barriers can The lab’s 26 researchers are orga- knowledge sources and efficient pro- only be removed by people who are work- nized into four groups corresponding cessing methods. ing at the scientific forefront in AI and to these areas. Together, they provide Wisber is jointly funded by the know potential industrial needs. Siemens with innovative software The Knowledge Processing Laboratory’s German government and several research and development concentrates in technologies, appropriate applications, industrial and academic partners. the following areas: (1) natural language prototypical implementations of AI The goal of the project is to develop interfaces to knowledge-based systems and systems, and evaluations of new a knowledge-based advice-giving databases; (2) theoretical and experimen- techniques and trends.
    [Show full text]
  • A Framework for Ontology-Based Library Data Generation, Access and Exploitation
    Universidad Politécnica de Madrid Departamento de Inteligencia Artificial DOCTORADO EN INTELIGENCIA ARTIFICIAL A framework for ontology-based library data generation, access and exploitation Doctoral Dissertation of: Daniel Vila-Suero Advisors: Prof. Asunción Gómez-Pérez Dr. Jorge Gracia 2 i To Adelina, Gustavo, Pablo and Amélie Madrid, July 2016 ii Abstract Historically, libraries have been responsible for storing, preserving, cata- loguing and making available to the public large collections of information re- sources. In order to classify and organize these collections, the library commu- nity has developed several standards for the production, storage and communica- tion of data describing different aspects of library knowledge assets. However, as we will argue in this thesis, most of the current practices and standards available are limited in their ability to integrate library data within the largest information network ever created: the World Wide Web (WWW). This thesis aims at providing theoretical foundations and technical solutions to tackle some of the challenges in bridging the gap between these two areas: library science and technologies, and the Web of Data. The investigation of these aspects has been tackled with a combination of theoretical, technological and empirical approaches. Moreover, the research presented in this thesis has been largely applied and deployed to sustain a large online data service of the National Library of Spain: datos.bne.es. Specifically, this thesis proposes and eval- uates several constructs, languages, models and methods with the objective of transforming and publishing library catalogue data using semantic technologies and ontologies. In this thesis, we introduce marimba-framework, an ontology- based library data framework, that encompasses these constructs, languages, mod- els and methods.
    [Show full text]
  • Ontologies to Interpret Remote Sensing Images: Why Do We Need Them?
    Ontologies to interpret remote sensing images : why do we need them? Damien Arvor, Mariana Belgiu, Zoe Falomir, Isabelle Mougenot, Laurent Durieux To cite this version: Damien Arvor, Mariana Belgiu, Zoe Falomir, Isabelle Mougenot, Laurent Durieux. Ontologies to inter- pret remote sensing images : why do we need them?. GIScience and Remote Sensing, Taylor & Francis: STM, Behavioural Science and Public Health Titles, 2019, pp.1-29. 10.1080/15481603.2019.1587890. halshs-02079438 HAL Id: halshs-02079438 https://halshs.archives-ouvertes.fr/halshs-02079438 Submitted on 26 Mar 2019 HAL is a multi-disciplinary open access L’archive ouverte pluridisciplinaire HAL, est archive for the deposit and dissemination of sci- destinée au dépôt et à la diffusion de documents entific research documents, whether they are pub- scientifiques de niveau recherche, publiés ou non, lished or not. The documents may come from émanant des établissements d’enseignement et de teaching and research institutions in France or recherche français ou étrangers, des laboratoires abroad, or from public or private research centers. publics ou privés. GIScience & Remote Sensing ISSN: 1548-1603 (Print) 1943-7226 (Online) Journal homepage: https://www.tandfonline.com/loi/tgrs20 Ontologies to interpret remote sensing images: why do we need them? Damien Arvor, Mariana Belgiu, Zoe Falomir, Isabelle Mougenot & Laurent Durieux To cite this article: Damien Arvor, Mariana Belgiu, Zoe Falomir, Isabelle Mougenot & Laurent Durieux (2019): Ontologies to interpret remote sensing images: why do we need them?, GIScience & Remote Sensing To link to this article: https://doi.org/10.1080/15481603.2019.1587890 © 2019 The Author(s). Published by Informa UK Limited, trading as Taylor & Francis Group.
    [Show full text]
  • Question Answering
    Question Answering What is Ques+on Answering? Dan Jurafsky Ques%on Answering One of the oldest NLP tasks (punched card systems in 1961) Simmons, Klein, McConlogue. 1964. Indexing and ( ( Dependency Logic for Answering English Quesons. !"#$%&' )&*#'%+,-.'$/#0$ American Documentaon 15:30, 196-204 What do worms eat? Worms eat grass Horses with worms eat grass worms horses worms eat with eat eat grass worms grass what Birds eat worms Grass is eaten by worms birds worms eat eat 2 worms grass Dan Jurafsky Ques%on Answering: IBM’s Watson • Won Jeopardy on February 16, 2011! WILLIAM WILKINSON’S “AN ACCOUNT OF THE PRINCIPALITIES OF WALLACHIA AND MOLDOVIA” Bram Stoker INSPIRED THIS AUTHOR’S MOST FAMOUS NOVEL 3 Dan Jurafsky Apple’s Siri 4 Dan Jurafsky Wolfram Alpha 5 Dan Jurafsky Types of Ques%ons in Modern Systems • Factoid ques+ons • Who wrote “The Universal Declara4on of Human Rights”? • How many calories are there in two slices of apple pie? • What is the average age of the onset of au4sm? • Where is Apple Computer based? • Complex (narrave) ques+ons: • In children with an acute febrile illness, what is the efficacy of acetaminophen in reducing fever? • What do scholars think about Jefferson’s posi4on on dealing with pirates? 6 Dan Jurafsky Commercial systems: mainly factoid quesons Where is the Louvre Museum located? In Paris, France What’s the abbreviaon for limited L.P. partnership? What are the names of Odin’s ravens? Huginn and Muninn What currency is used in China? The yuan What kind of nuts are used in marzipan? almonds What instrument does
    [Show full text]
  • Ques+On Answering
    Queson Answering Debapriyo Majumdar Information Retrieval – Spring 2015 Indian Statistical Institute Kolkata Adapted from slides by Dan Jurafsky (Stanford) and Tao Yang (UCSB) Queson Answering One of the oldest NLP tasks (punched card systems in 1961) Simmons, Klein, McConlogue. 1964. Indexing and Dependency Logic for Answering English Ques%on: Poten%al-Answers: Questions. American Documentation 15:30, 196-204 What do worms eat? Worms eat grass Horses with worms eat grass worms horses worms eat with eat eat grass worms grass what Birds eat worms Grass is eaten by worms birds worms eat eat worms grass 2 Ques%on Answering: IBM’s Watson § Won Jeopardy on February 16, 2011! WILLIAM WILKINSON’S “AN ACCOUNT OF THE PRINCIPALITIES OF WALLACHIA AND MOLDOVIA” Bram Stoker INSPIRED THIS AUTHOR’S MOST FAMOUS NOVEL 3 Apple’s Siri § A seemingly “limited” set of few possible questions § Answers based on contextual parameters 4 Wolfram Alpha, Google 5 Wolfram Alpha But in this case, Google returns a standard list of document links 6 Types of Ques%ons in Modern Systems § Factoid questions – Answers are short – The question can be rephrased as “fill in the blanks” question Examples: – Who directed the movie Titanic? – How many calories are there in two slices of apple pie? – Where is Louvre museum located? § Complex (narrative) questions: – What precautionary measures should we take to be safe from swine flu? – What do scholars think about Jefferson’s position on dealing with pirates? 7 Paradigms for QA § IR-based approaches – TREC QA Track (AskMSR, ISI,
    [Show full text]
  • The Negative Impact of A-Box Materialization on Rdf2vec Knowledge Graph Embeddings
    More is not Always Better: The Negative Impact of A-box Materialization on RDF2vec Knowledge Graph Embeddings Andreea Ianaa, Heiko Paulheima aData and Web Science Group, University of Mannheim, Germany Abstract RDF2vec is an embedding technique for representing knowledge graph entities in a continuous vector space. In this paper, we investigate the effect of materializing implicit A-box axioms induced by subproperties, as well as symmetric and transitive properties. While it might be a reasonable assumption that such a materialization before computing embeddings might lead to better embeddings, we conduct a set of experiments on DBpedia which demonstrate that the materialization actually has a negative effect on the performance of RDF2vec. In our analysis, we argue that despite the huge body of work devotedon completing missing information in knowledge graphs, such missing implicit information is actually a signal, not a defect, and we show examples illustrating that assumption. Keywords RDF2Vec, Embedding, Reasoning, Knowledge Graph Completion, A-box Materialization 1. Introduction A straightforward assumption is that completing miss- ing knowledge in a knowledge graph before computing RDFvec [1] was originally conceived for exploiting knowl- node representations will lead to better results. However, edge graphs in data mining. Since most popular data in this paper, we show that the opposite actually holds: mining tools require a feature vector representation of completing the knowledge graph before computing an records, various techniques have been proposed for cre- RDF2vec embedding actually leads to worse results in ating vector space representations from subgraphs, in- downstream tasks. cluding adding datatype properties as features or creat- ing binary features for types [2].
    [Show full text]
  • Question Answering
    Queson Answering Evangelos Kanoulas [email protected] Question answering EVI Siri Google (Amazon) (Apple) Question answering Question answering h7p://youtu.be/WFR3lOm_xhE?t=20s Connecons to Related Fields • Informaon retrieval • Natural language processing • Databases • Machine learning • Ar%ficial intelligence Queson Answering Types of Ques%ons in Modern Systems • Factoid quesons – Who wrote “The Universal Declaraon of Human Rights”? – How many calories are there in two slices of apple pie? – What is the average age of the onset of au%sm? – Where is Apple Computer based? • Complex (narrave) ques%ons: – In children with an acute febrile illness, what is the efficacy of acetaminophen in reducing fever? – What do scholars think about Jefferson’s posi%on on dealing with pirates? Commercial systems: mainly factoid quesons Where is the Louvre Museum In Paris, France located? What’s the abbreviaon for L.P. limited partnership? What are the names of Odin’s Huginn and Muninn ravens? What currency is used in China? The yuan What kind of nuts are used in almonds marzipan? What instrument does Max drums Roach play? What is the telephone number 650-723-2300 Paradigms for QA • IR-based approaches – TREC; Google • Knowledge-based approaches – Apple Siri; Wolfram Alpha; Amazon Evi • Hybrid approaches – IBM Watson Many ques%ons can already be answered by web search • a IR-based Ques%on Answering • a IR-based Factoid QA Document DocumentDocumentDocument Document Document Indexing Answer Passage Question Retrieval Processing Docume Document Query Document Document
    [Show full text]
  • Knowledge Vault: a Web-Scale Approach to Probabilistic Knowledge Fusion
    Knowledge Vault: A Web-Scale Approach to Probabilistic Knowledge Fusion ∗ Xin Luna Dong , Evgeniy Gabrilovich, Geremy Heitz, Wilko Horn, Ni Lao, y Kevin Murphy , Thomas Strohmann, Shaohua Sun, Wei Zhang Google, 1600 Amphitheatre Parkway, Mountain View, CA 94043 {lunadong|gabr|geremy|wilko|nlao|kpmurphy|tstrohmann|sunsh|weizh}@google.com ABSTRACT In recent years, several large-scale knowledge bases (KBs) Recent years have witnessed a proliferation of large-scale have been constructed, including academic projects such as knowledge bases, including Wikipedia, Freebase, YAGO, Mi- YAGO [39], NELL [8], DBpedia [3], and Elementary/ Deep- Dive [32], as well as commercial projects, such as those by crosoft's Satori, and Google's Knowledge Graph. To in- 1 2 3 crease the scale even further, we need to explore automatic Microsoft , Google , Facebook , Walmart [9], and others. methods for constructing knowledge bases. Previous ap- (See Section 7 for a detailed discussion of related work.) proaches have primarily focused on text-based extraction, These knowledge repositories store millions of facts about which can be very noisy. Here we introduce Knowledge the world, such as information about people, places and Vault, a Web-scale probabilistic knowledge base that com- things (generically referred to as entities). bines extractions from Web content (obtained via analysis of Despite their seemingly large size, these repositories are text, tabular data, page structure, and human annotations) still far from complete. For example, consider Freebase, the with prior knowledge derived from existing knowledge repos- largest open-source knowledge base [4]. 71% of people in Freebase have no known place of birth, and 75% have no itories.
    [Show full text]