User:Tim Lebo

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Contents 1 Research 2 Advanced Semantic Web (Fall 2008) 2.1 Overview of participation 2.2 Presentation attendance 2.3 Papers presented 2.4 Questions posed 2.5 Course wiki contributions

Research

A description of my research can be found at http://data-gov.tw.rpi.edu/wiki/Tim_Lebo

Advanced Semantic Web (Fall 2008)

Overview of participation

Of the 42 paper presentations, Tim Lebo presented 3, attended 25, and did not attend 3.

While attending CSCI 6966 Advanced Semantic Web (Fall 2008), Tim Lebo gave four presentations:

• a summary of his current "Accountable Visualization" research,
• an overview of two conference papers, and
• an overview of one journal article.

Tim Lebo made 47 posts containing comments and questions regarding presented papers. Many of these posts pose multiple questions.

Tim Lebo also contributed to the organization of the course wiki. The Course wiki contributions section lists these contributions.

Presentation attendance

Presentations Tim Lebo attended	Speaker	Date
Joshua Taylor Journal Paper Presentation 1	Joshua A. Taylor	24542831 July 2007
Joshua Taylor Slate Introduction	Joshua A. Taylor	24547144 September 2008
Community-basedMapping Shangguan 0911	Zhenning Shangguan	245472111 September 2008
Semantic Grounding Joshua Shinavier 20089011	Joshua Shinavier	245472111 September 2008
Joshua Taylor 20080918 Presentation	Joshua A. Taylor	245472818 September 2008
Tw:Brahms Medha Presentation 0918	Medha Atre	245472818 September 2008
Gregory Todd Williams SPARQL BGP Optimization Presentation	Gregory Todd Williams	245473525 September 2008
Asma Ounnas Semantic Social Networks Presentation	Asma Ounnas	245473525 September 2008
Alvaro Graves SemRank	Alvaro Graves	24547422 October 2008
Joshua Shinavier Networked Graphs	Joshua Shinavier	24547422 October 2008
Jesse Weaver RDF Management Approaches	Jesse Weaver	24547422 October 2008
James Michaelis Provenance Model	James Michaelis	24547422 October 2008
Summary Abox Ankesh	Ankesh Khandelwal	24547499 October 2008
Joshua Taylor presents Discovering Simple Mappings Between Relational Database Schemas and Ontologies	Joshua A. Taylor	24547499 October 2008
Gregory Todd Williams Graph Summaries	Gregory Todd Williams	24547499 October 2008
Shangguan MetaQuery Presentation	Zhenning Shangguan	245475616 October 2008
Medha GRIN Presentation	Medha Atre	245475616 October 2008
Joshua Shinavier Laying the foundations for a World Wide Argument Web	Joshua Shinavier	24547776 November 2008
Journal Ankesh	Ankesh Khandelwal	24547776 November 2008
Journal XixiLuo	Xixi Luo	24547776 November 2008
Jesse Weaver Presents Named Graphs	Jesse Weaver	245478413 November 2008
GTW Time in RDF	Gregory Todd Williams	245478413 November 2008
Shangguan Journal CNLPresentation	Zhenning Shangguan	245479120 November 2008
Jiao Journal Presentation	Jiao Tao	245479120 November 2008
Medha Journal Presentation	Medha Atre	245479120 November 2008

(Made pseudo-disjoint classes Presentation attended by Tim Lebo and Presentation not attended by Tim Lebo)

Presentations Tim Lebo did not attend	Speaker	Date
NSPARQL Jesse Weaver 20080911	Jesse Weaver	245472111 September 2008
Ankesh Sep11	Ankesh Khandelwal	245472111 September 2008
Debbie Rank Typed Graph Walks Presentation	Debbie Heisler	245476323 October 2008

Tim left early on 11 September for a business trip and was at VisWeek for the week of 23 October.

Papers presented

Tim Lebo gave the following paper presentations for this course:

Presentation Page	Title of paper	Paper has author
Abel2007enabling presented by Tim Lebo 25 sept 2008	Enabling Advanced and Context-Dependent Access Control in RDF Stores	Fabian Abel Juri Luca De Coi Nicola Henze Arne Wolf Koesling Daniel Krause Daniel Olmedilla
Theoharis2008graph presented by Tim Lebo 4 dec 2008	On Graph Features of Semantic Web Schemas	Yannis Theoharis Yannis Tzitzikas Dimitris Kotzinos Vassilis Christophides
Zhao2004using presented by Tim Lebo 9 oct 2008	Using Semantic Web Technologies for Representing E-science Provenance	Jun Zhao Chris Wroe Carole A. Goble Robert Stevens Dennis Quan Mark Greenwood

Questions posed

Tim Lebo posted the following in preparation for attending paper presentations for this course:

About	Text
Anyanwu2005semrank	The authors propose a few interesting metrics for ranking a set of semantic paths. A semantic path is an instance of a Semantic Association, which is a sequence of (RDF) properties. In the Background/Motivation section, the authors illustrate and define three example Property Sequences: rho-pathAssociation, rho-joinAssociation, and rho-isoAssociation. Are there compelling reasons for recognizing these types of semantic paths, i.e., are these structures applicable in real-world analysis? Are there other Property Sequences that are typical in the Semantic Association research area? Although these examples are helpful in understanding the purpose of analyzing semantic paths, it is unclear how these identified three relate to the metrics introduced in the remaining sections of the paper. Is there a deeper relationship between these three Property Sequences and the metrics proposed?
Anyanwu2005semrank	The information content I(ps) is the addition of three terms: a ROC-aware min, a ROC-aware avg, and a non-ROC-aware max. The refraction count RC(PS) relies on the Semantic Summary, which in turn relies on the ROCs. The S-Match(property,keyword) metric relies on a property hierarchy. The final SEMRANK metric involves a (modulated) composition of five terms, four of which rely on a schema to be defined. Is this reliance a strength or a weakness? How do the metrics change as the presence, completeness, and quality of the schema changes?
Carroll2005named	When comparing TriX to RDF/XML, the authors state, "The URI at which an RDF/XML document is published is used for three different purposes: as a retrieval address, with an operational semantics; as a means of identifying the document; and as a means of identifying the graph described by the document. There is potential for confusion between these three uses." The URIRef for a named graph performs only the latter function (identifying the graph). When encountering a URIref in a set of triples (e.g., ":G1 pr:disallowedUsage pr:Marketing ." in section 5.3), how does a crawler know that the URIRef identifies a named graph? When a crawler determines that a URIRef names a named graph (e.g., 'ng') , how does it obtain the rdfgraph(ng)? Would the process require anything different from the proposed methods for obtaining descriptions for a "plain old" rdfs:Resource?
Cattuto2008semantic
Cattuto2008semantic	The introduction motivates the investigation of relatedness measures: "We believe that a deeper insight into the semantic properties of relatedness measures is an important prerequisite for the design of ontology learning procedures..." Although relatedness measures may be necessary for a "Ontology Learning" capability, they are arguably insufficient on their own. What other components are required to achieve Ontology Learning? Is this work already being done?
Cattuto2008semantic	The paper compares five relatedness metrics. We could sit down and make up ten more. What does a "good" metric look like? How do we avoid a subjective evaluation of a metric's results? (e.g.,"An interesting observation is also that java and python could be considered as siblings in some suitable concept hierarchy") If we had the perfect metric (m*), what would we do with it?
Cattuto2008semantic	Could you explain the difference between one-mode, two-mode, and three-mode analysis? What challenges are introduced when attempting three-mode analysis?
Cattuto2008semantic	Notation nit: <math>R^T</math> and <math>R^n</math> seem to be used synonymously -- what is the distinction? Shouldn't <math>R^T</math> be <math>R^
Cattuto2008semantic	I don't understand the statement "The reason for giving weight zero between a node and itself is that we want two tags to be considered related when they occur in a similar context, and not when they occur together. What is the difference between "similar context" and "occur together"? I would think that they are the same. Isn't "occurrence" the only "context" that the Folksonomy formalism provides?
Cattuto2008semantic	The description of FolkRank mentions "random surfer vector" but does not introduce the term or it's purpose. Could you describe the random surfer vector?
Eiter2008combining	The paper describes a "conservative extension" to the combination of DL's first-order semantics and logic programming's answer set semantics, where knowledge can be transferred between a DL knowledge base and a logic programming program. When describing how dl-programs can express the closed-world assumption "on top" of an external DL knowledge base, negation is asserted despite its DL provability. Could an inconsistency arise when the CWA assertions are shared with the DL knowledge base? I'm not familiar with logic programming and answer sets -- and reading the paper left me interested, impressed, and confused. Could you explain the gist of logic programming and how answer set semantics differs from description logic semantics? It sounds pretty cool.
Fokoue2006summary	The summary of an Abox takes advantage of redundant assertions w.r.t consistency checking by collapsing individuals that are members of the same concept sets and are /not/ explicitly asserted to be different from each other. The authors note, "Any explicit assertions that two individuals are different from each other are maintained in the summary Abox." Would an exhaustive enumeration of "differentFrom" for each instance in the Abox render the summary method useless?
Gil2007towards	The results of the simulation compare the Mean Squared Error, k-sum, and Edit Distance to a 'baseline', which is "a ... search engine ... that ranks search results by topic and popularity, ... without taking trust into account." A lot of assumptions are made to model the trust users have for associations and resources, and the simulation used 1,000 queries generated from 1,000 generated resources, 10,000 generated associations, and 1,000 generated users. Although the paper is trying to compare their trust-based ordering against 'traditional' search engines, are they not just comparing their technique to random performance instead?
Grau2007history	The authors open the abstract by stating, "The development of ontologies involves continuous but relatively small modifications." A reasonable first step to reduce the ontology development cycle is to tackle the problem addressed in the paper: classify ontology O^2 by reusing the "evidences" from the classification of O^1, the set of added axioms, and the set of removed axioms. Their "module" technique can then be applied at each committed change. This addresses the relatively small modifications aspect of ontology development, but what about the continuous aspect? Could "modules" be determined using a history longer than only the previous step?
Gutierrez2007introducing	How does the work presented in this paper differ from OWL Time? Could OWL Time be used to extend the temporal labels used by Gutierrez et al.?
Gutierrez2007introducing	According to Footnote 3, the authors "chose not to ((use the standard reification vocabulary of RDF)) to stress the fact that the notions presented in this paper are independent of any view one may have about the concept of reification in RDF." Yet the "temporal label" is constructed in the exact same way the standard reification vocabulary would do it (:tsubj rdfs:subPropertyOf rdf:subject . :tpred rdfs:subPropertyOf rdf:predicate . :tobj rdfs:subPropertyOf rdf:object.). How can the authors reproduce the standard reification approach while pretending to stay at arms length from the standard?
	In Definition 3, a mapping m is a 5-tuple <id,u,v,t,f>, where u is an element of the relational schema, v is an element of the ontology, t is a relationship (e.g., equivalence or subsumption), and f is a confidence measure. Phase II of the matching process views each element of the relational schema and ontology as a "virtual document" that is compared to the other elements' "virtual documents" using the the TF/IDF cosine measure. In a mapping m, id, u, v, and f are providing in this process. How is t (equivalence/subsumption) determined for a given mapping? Does the use the subsequent third Phase, Validity Mapping Consistency, indicate a lack of confidence in the previous stage's ability to match? What else "doesn't make sense" in the matching that hasn't been filtered out?
Janik2005brahms	How many implementations of the depth-first search and bi-directional breadth-first exhaustive search algorithms were used in the evaluation? Three languages were used (C++ (BRAHMS), C (Redland), and Java (Jena and Sesame)) among the four triple stores. If multiple implementations were used, what assurances were made that they performed similarly before incorporating the triple store?
Janik2005brahms	Did the authors repeat the load and execution tests to demonstrate reproducability? How much variability in the results could we expect?
Lin2008discovering	The authors make a very good point regarding the "ill defined" nature of using probabilistic measures for a deterministic graph structure. The two Random Experiments that they propose are unique, intuitive, and probabilistically sound methods for obtaining probabilistic measures. But from the time that they propose the method, they do not discuss the methods' computational expense until the last paragraph of the paper, "an important future direction is to improve the scalability of the system. What is most expensive is the computation of feature values, since it requires the system to count a potentially large number of paths." How long should the Random Experiments be reasonably run to obtain characterize the input while trading off the time required to do so?
Moreau2007open	It is clear that the intent of this paper is to introduce the start of a common model for provenance and NOT to motivate the use of provenance systems. The need for a common model for any mutual interest is self-evident. However, some motivation for the use of provenance would be helpful. What applications require any provenance representation, what are the benefits for its use, and how many provenance models are in use today (e.g., how many systems need to reconcile with the common model)?
Noy2008collecting	The ability to provide a "One-stop shopping for ontology resources" remains overdue. Why do you think it has taken so long to establish this type of capability and why is it not the norm? It seems that after a lot of hard work by a team, ontologies are dead-on-arrival when they hit the web. What is preventing the Web philosophy from nurturing continued use and development of existing ontologies?
Noy2008collecting	Is reification used to represent the Mapping <C_s,C_t,R,M>? If so, have your experiences with reification been positive or negative? If not, how did reification not scratch the itch for your application?
Noy2008collecting	The authors state "One mapping can depend on another: 'If X is Y, then A is B'." How is this dependency mapping modeled?
Noy2008collecting	(diagram nit) Fig. 2 is great for illustrating the connectivity between ontologies. Making the nodes' size proportional to the size of the ontologies they represent might be more communicative, since the cardinality between two ontologies could usefully be considered with respect to the sizes of each ontology.
Noy2008collecting	Claims of "complete domain-independence" are often overstatements. To what extent is this technology domain-independent, and in what areas is it not?
Perez2008nsparql	The "path-like" nature resembles Fresnel Selector Language (FSL) http://www.w3.org/2005/04/fresnel-info/fsl/. Where does FSL fit within the family of nSPARQL and the others discussed in the Related Work section?
Perez2008nsparql	(just a comment) The "axis-like" nature, along with the "forward-backward" directionality, resembles Ted Nelson's zzStructure http://www.nongnu.org/gzz/gi/gi.html. The zzStructure incorporates a layout that facilitates visual navigation. It would be interesting to compare this work from a different discipline in a different era (he was decades ago).
Perez2008nsparql	The authors note "the occurrence of variables in the predicate position of triple patterns is forbidden in nSPARQL." This limitation resembles another that was described in the DARQ paper: "The matching compares the predicate in a triple pattern with the predicate defined for a capability and evaluated the constraint for subject and object. Because matching is based on predicates, DARQ currently only supports queries with bound predicates." Is there something fundamental about the need to assume a bound predicate? If so, does this inhibit the ability to make advancements throughout the field?
Perez2008nsparql	Do you think the nested-regular-expression construct proposed in nSPARQL will make it into future SPARQL recommendations?
Quilitz2008querying	The authors state, "There is no other need for cooperation except of the support of the SPARQL protocol." Yet later, "To find the relevant information sources for the different triples in a query and to decompose the query into sub-queries the query engine needs information about the data sources." DARQ relies on the service descriptions to determine how to decompose the query. How do the service descriptions not require cooperation to obtain the capabilities <math>C_D</math> of a data source D? "We deliberately use only these simple statistics because we expect every data source to be able to provide them, or at least rough estimations."
Quilitz2008querying	Aren't Limitations subsumed by Constraints? That is, can't all Limitations be expressed as Constraints?
Quilitz2008querying	How does the subject (object) being bound effect the subject (object) selectivity <math>ssel_D(p)</math> (<math>osel_D(p)</math)? How does this value relate to it's alternative when the subject (object) is not bound?
Quilitz2008querying	What are the Service Descriptions for the dbpedia endpoints? This is a critical component to understanding /why/ the optimization performed better. "Our evaluations show that even with a very limited amount of statistical information it is possible to generate query plans that perform relatively well." What statistical information was used?
Quilitz2008querying	It is not clear how the dbpedia data were split across the two servers. How were they split? For example, Q1 used sources S4 and S5. If both S4 and S5 were on the first server, the second was idle.
Quilitz2008querying	Why are their /five/ logical endpoints on two servers? How does the setup of two Sun-Fire-880 machines "make sure that the endpoints are not a bottleneck"?
Quilitz2008querying	What is the computational complexity of the planning and optimization? Execution timings can be a misleading indicator of the efficiency.
Rahwan2007laying	The authors chose to model their argument ontology in RDFS. Although this makes sense because of RDF's suitability for distributed augmentation of existing resources, how are the semantics of RDFS beneficial in this application? The authors restrict themselves to RDFS while wishing for disjointness. Why, in 2007, would an RDFS ontology developer choose not to use OWL? "Translating the ontology to more expressive Semantic Web ontology languages such as OWL can also enable ontological reasoning over argument structures, for example, to automatically classify arguments, or to identify semantic similarities among arguments." Could you suggest any examples for how (and why) arguments could be classified or similarities could be determined? The interfaces shown indicate a large jump from today's blogging to tomorrow WWAF. What kind of users would use the system the paper proposes? How much "argument theory" would they be expected to know? If a novice to "argument theory" used the system, how likely would their products align with the reasoning the system can provide? The system proposed involves adding additional explicit structure to the "blogging" of today. How far would a user need to decompose the free text, and how often would that granularity need to change as the argument evolves? At what point does the structure inhibit the thought process of the user?
Schenk2008networked	The extension of named graphs that the authors describe looks like it will be very useful in Semantic Web applications. A couple of pragmatic questions. Do the QNames in the rdf:Literal values of g:definedBy inherit the prefix bindings defined in the Networked Graph file? Tucking query expressions into literals seems to be a recurring theme (e.g., Fresnel does it for SPARQL and FSL expressions). However, my experiences with managing query expressions within literals has been clunky at best. Has anyone proposed an analogous rdf:Property that indicates a URI that should be dereferenced to obtain the query expression? If so, where. If not, is it needed? It seems that this would fit well within a development environment.
Schenk2008networked	In the Related Work section, the authors compare Networked Graphs to ActiveXML: "The semantics of ActiveXML documents is also defined using a fixpoint, but unlike NGs, ActiveXML documents are infinite in general." What does "infinite in general" mean and why is it not desirable?
Schenk2008networked	In the Related Work section, the authors compare Networked Graphs to NRL: "NEPOMUK Representation Language NRL ... allows for various ways to specify 'views', which are defined with a procedural semantics in contrast to the declarative semantics of NGs." What is the distinction between procedural semantics and declarative semantics?
Schmidt2008experimental	The only place the ratios of usr, sys, and total response times are mentioned is in the discussion for Q1 ("Return the year of publication of 'Journal 1 (1940)'", where the authors state, "The gap between total and usr+sys for 25M indicates that much time is spent in waiting for data being read from or written to disk". The choice to use different vertical scales in Figures 1-3 leads to an investigation of these ratios while obscuring a natural consideration of the more important issue: the relative response times between triple store approaches. Regardless, the ratio usr/sys falls within one of three categories: minority/majority, all/none, and none/none -- and the ratio's category transitions from none/none, to all/none, to minority/majority as the data size increases within a condition. What does the ratio of usr and sys times indicate with respect to the performance of the query execution?
Schueler2008querying	When describing their design choices, the authors state, "For compliance with existing applications that access the repository in a common way (e.g. using SPARQL queries), we do not modify existing user data." The preservation of user data is clearly a important. But they disregard RDF reification as an option, saying "This requirement does not allow us to use mechanisms like RDF reification, which decompose existing triples and fully change the representation model." In what way would RDF reification (the use of rdf:Statement) decompose and fully change the representation model?
Schwitter2008controlled	The Controlled Natural Language presented in this paper seems to be useful for pedagogical purposes, but little more. Although hiding a more obscure syntax /may/ increase the approachability and/or "explainability", it seems that a novice would still require a background understanding of OWL Lite^minus to do anything useful. The novice is at risk for establishing a greater expectation for the language based on the assumption that the English-like sentences are English; these expectations will be unfulfilled by the restricted ("focused"?) capabilities of a logic-based system. On the other hand, the verbosity that provides approachability for the novice would hinder use by "knowledge engineers" who prefer a more concise syntax. However, these issues are most concerning only when authoring the TBox and ABox. When posing questions to the system, the interaction seems more natural. This is done by generating a satisfying model with SATCHMO, which includes all entailed statements. What is a "satisfying model", how does it differ from other kinds of models, and how is it generated?
Szomszor2008semantic	The authors claim that "Tagging ... (is a) knowledge management mechanism that users find easy to use and understand." What scientific, non anecdotal, evidence do we have that can convince us of this? Do /all/ users find tagging to be easy to use and understand? Are there certain contexts in which tagging is easy to use and understand, but not others?
Szomszor2008semantic	The authors claim that their "results show that far richer interest profiles can be generated." What meaningful metric are they using to measure 'richer', such that we can know that their tag-combining approach provides 'far richer' results? One suggestion would be to offer the service described in the paper and gather metrics for how many del.icio.us and flickr users request the service, suggest the service, refer back to the results, etc.
Udrea2007grin	It would seem that the method used to determine cluster centers would drastically influence query performance. The selected clustering algorithm (e.g., PAM) and the inter-cluster metric (e.g., single, complete, or average link) would both be factors of performance. The authors do not commit to an inter-cluster distance metric d_c and devote one sentence discussing the results of the comparison: They all "performed" the same within 5%. Why do you suppose GRIN's index creation times, index sizes, and query times were invariant to the inter-cluster metric used?

Course wiki contributions

• Created the Template:CSCI 6966 Advanced Semantic Web (Fall 2008) - Lesson index template to provide overview and navigational capabilities.
  • Contributed to the wording, selected attributes, and ordering of CSCI 6966 Advanced Semantic Web (Fall 2008) All Presentations.
  • Created the CSCI 6966 Advanced Semantic Web (Fall 2008) announcements page.
  • Moved syllabus section to CSCI 6966 Advanced Semantic Web (Fall 2008) syllabus
• Illustrated how we could use the wiki to explain/describe the topics that we discuss (see Named graph and Reification). This is especially useful when we discuss multiple papers that discuss similar notions (see s).
  • Paper, Property:Discusses, Simmhan2005survey, Provenance system

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