Cyc

Foxvog, Doug

doi:10.1007/978-90-481-8847-5_12

Cited by 18 publications

(5 citation statements)

References 2 publications

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“…Although recent work on question generation has generated semantic knowledge bases from text (Olney et al 2012), we chose to use an existing knowledge base. Although few such knowledge bases are currently available, the technology and the market for such knowledge bases are increasing steadily (Liu and Singh 2004; Foxvog 2010; “Wikidata”). Thus, we believe that it is only a matter of time before knowledge bases are available that are sufficient for high school basic sciences.…”

Section: Generating Questions From a Semantic Knowledge Basementioning

confidence: 99%

How do machine-generated questions compare to human-generated questions?

Zhang

VanLehn

2016

RPTEL

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Science instructors need questions for use in exams, homework assignments, class discussions, reviews, and other instructional activities. Textbooks never have enough questions, so instructors must find them from other sources or generate their own questions. In order to supply biology instructors with questions for college students in introductory biology classes, two algorithms were developed. One generates questions from a formal representation of photosynthesis knowledge. The other collects biology questions from the web. The questions generated by these two methods were compared to questions from biology textbooks. Human students rated questions for their relevance, fluency, ambiguity, pedagogy, and depth. Questions were also rated by the authors according to the topic of the questions. Although the exact pattern of results depends on analytic assumptions, it appears that there is little difference in the pedagogical benefits of each class, but the questions generated from the knowledge base may be shallower than questions written by professionals. This suggests that all three types of questions may work equally well for helping students to learn.

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Section: Generating Questions From a Semantic Knowledge Basementioning

confidence: 99%

How do machine-generated questions compare to human-generated questions?

Zhang

VanLehn

2016

RPTEL

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“…Next, we aim to encode all the above information in the form of ontological statements (RDF-style triples) of the form: class 1-property-class 2. All the names (of persons, places, manuscripts, events, and other objects) were submitted as queries to a semantic search engine, Virtuoso server, 35 to look them up in 35 http://virtuoso.openlinksw.com/ external ontologies, such as Freebase (Bollacker et al, 2008), DBPedia (Auer et al, 2007), and Opencyc (Foxvog, 2010;Lenat et al, 2010 36 ), and in the authority files, such as VIAF, 37 LCNS,38 GETTY, 39 GND, 40 and GeoNames. 41 Once found (as shown in the last column of Table 2: links to authority files), they were linked directly by using the extracted URI or by owl:sameAs relation for similar terms in different vocabularies.…”

Section: Resultsmentioning

confidence: 99%

Toward an Ontopedia for Historical Hebrew Manuscripts

Zhitomirsky‐Geffet

Prebor

2016

Front. Digit. Humanit.

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Historical handwritten Hebrew manuscripts are one of the most unique and authentic witnesses of Jewish culture and thought that survived through the centuries. In order to enable a systematic research of the knowledge embedded in the manuscripts, there is a need for a formal conceptual data model with a high level of semantic granularity, an ontology. We propose to build a dynamic web-based framework that will allow scholars to create, enrich, and consult an "ontopedia" (ontology-based encyclopedia) of Hebrew manuscripts. The framework is based on an ontology especially designed and implemented for this domain and goals. We view a manuscript as a "living entity" and propose to design a new ontological data model of the narrative for a manuscript, stages/milestones in its biography (creation, copying, and acquisition). A sequence of events and places constitutes a timeline of history against which manuscripts, people, and their relationships can be placed. A large-scale automated reasoning based on the ontology will also enable us to construct a semantically rich social network of people and manuscripts, and to compare the effect of time and place on the manuscripts' qualitative characteristics and quantitative distribution.

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“…We use an ontology to specify the types of entities and relations that may appear in the parse graphs and therefore define the scope of our study. This ontology can be manually constructed using an ontology editor (e.g., [41], [42], [43]), adapted from an existing ontology (e.g., [44], [45]), or automatically or semi-automatically learned from data (e.g., [46], [47], [48]).…”

Section: Ontologymentioning

confidence: 99%

Joint Video and Text Parsing for Understanding Events and Answering Queries

Meng

Lee

et al. 2014

IEEE MultiMedia

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We propose a framework for parsing video and text jointly for understanding events and answering user queries. Our framework produces a parse graph that represents the compositional structures of spatial information (objects and scenes), temporal information (actions and events) and causal information (causalities between events and fluents) in the video and text. The knowledge representation of our framework is based on a spatial-temporal-causal And-Or graph (S/T/C-AOG), which jointly models possible hierarchical compositions of objects, scenes and events as well as their interactions and mutual contexts, and specifies the prior probabilistic distribution of the parse graphs. We present a probabilistic generative model for joint parsing that captures the relations between the input video/text, their corresponding parse graphs and the joint parse graph. Based on the probabilistic model, we propose a joint parsing system consisting of three modules: video parsing, text parsing and joint inference. Video parsing and text parsing produce two parse graphs from the input video and text respectively. The joint inference module produces a joint parse graph by performing matching, deduction and revision on the video and text parse graphs. The proposed framework has the following objectives: Firstly, we aim at deep semantic parsing of video and text that goes beyond the traditional bag-of-words approaches; Secondly, we perform parsing and reasoning across the spatial, temporal and causal dimensions based on the joint S/T/C-AOG representation; Thirdly, we show that deep joint parsing facilitates subsequent applications such as generating narrative text descriptions and answering queries in the forms of who, what, when, where and why. We empirically evaluated our system based on comparison against groundtruth as well as accuracy of query answering and obtained satisfactory results.

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Cyc

Cited by 18 publications

References 2 publications

How do machine-generated questions compare to human-generated questions?

How do machine-generated questions compare to human-generated questions?

Toward an Ontopedia for Historical Hebrew Manuscripts

Joint Video and Text Parsing for Understanding Events and Answering Queries

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