Citations: Probabilistic Datalog: Implementing logical information retrieval for advanced applications

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Fuhr, N. (2000). Probabilistic Datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110. 12

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Categorizing Web Documents in Hierarchical Catalogues - Frommholz (2001) (2 citations) (Correct)

....local implication as a single event and assume their independence. This way, we can use probabilistic Datalog (pDatalog # ) for the calculation. Datalog is a function free Horn clause predicate logic. pDatalog # enhances Datalog with probability theory, assuming the independence of events (see ([5] for further details on pDatalog # ) With HySpirit , there exists an implementation of pDatalog # . A program that calculates # ## # written in pDatalog # is shown in figure 5. Here, the probabilistic hierarchy graph from figure 4 is implemented with # ### # ####### and # ### # ....

....rule describes how # ## # is calculated. # implies # globally if # implies # locally or # implies a category # globally and # implies # locally. The input globalimply(c1,c6)would yield # ## # # # # # = 0.13095. This value is computed by pDatalog # in the following way ([5]) http: www.hyspirit.com 1 0.3 localimply o(c1,c2) 0.5 localimply u(c2,c1) 2 0.3 localimply o(c2,c5) 0.5 localimply u(c5,c2) 3 0.3 localimply o(c6,c5) 0.5 localimply u(c5,c6) 4 0.3 localimply o(c1,c3) 0.5 localimply u(c3,c1) 5 0.3 localimply o(c3,c6) 0.5 localimply u(c6,c3) 6 ....

Norbert Fuhr. Probabilistic datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

Efficient algorithms for ranking documents represented as.. - Losada, Barreiro (2000) (Correct)

....issue stands on the fact that document representations are more expressive than classical ones and, thus, a system with such an expressive capability seems to have chance to improve performance. In fact, more expressive document representations have been recently claimed for advanced applications [9] and for image retrieval [10] Representing a document with a DNF formula gives us the possibility of expressing several views of the document. For instance, the title and the abstract could be the basis for building two different clauses of the document s representation. This representation would ....

N. Fuhr. Probabilistic Datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

The MIND Architecture for Heterogeneous Multimedia - Federated Digital Libraries Self-citation (Fuhr) (Correct)

Learning probabilistic Datalog rules for information.. - Nottelmann, Fuhr (2001) Self-citation (Fuhr) (Correct)

....and imprecision in IR, a logic that allows for uncertain reasoning should be used. In [16] a probabilistic approach is discussed for this purpose, thus document retrieval can be based on the estimation of the probability Pr(q # d) This probabilistic approach is combined with Datalog in [3]. Datalog is a (function free) variant of Horn predicate logic which is widely used for deductive databases. For probabilistic Datalog, probabilistic weights may be attached to both facts and rules. As rule weights can only be used with certain restrictions, 17] describes another approach for ....

....and b i (the subgoals of the body ) denote literals with variables and constants as arguments. A rule can be seen as clause h,b 1 , b n . A fact f is a rule with only constants in the head and an empty body (the # can be omitted in this case) In probabilistic Datalog (see [3]) every fact or rule has a probabilistic weight attached, prepended to the fact or rule. Evaluation is based on the notion of event keys and event expressions. Facts and instantiated rules are basic events, each of them has assigned an event key. Each derived fact is associated with an event ....

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N. Fuhr. Probabilistic Datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

MIND: An architecture for multimedia information retrieval.. - Nottelmann, Fuhr (2001) (4 citations) Self-citation (Fuhr) (Correct)

....data type of an attribute defines the media type, the domain and the (vague) predicates. As MIND considers heterogeneous libraries with different schemas, one major task is to transform the user query into its library specific counterpart ( proprietary query ) We will use probabilistic Datalog [6] for describing uncertain mappings between schemas. e.g. a user query condition for the Dublin Core (DC) 3] attribute creator will be transformed into three proprietary query conditions referring to the MARC 21 [7] attributes field 100 , field 700 and field 710 , respectively. The ....

N. Fuhr. Probabilistic datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

Models in Information Retrieval - Fuhr Self-citation (Fuhr) (Correct)

....A probabilistic weight of 1 can be omitted. Furthermore, ground facts must be unique, i.e. #g # PE # # # g # # PE # g = g # implies that # = # # . Evaluation of Datalog# programs Here we only give a brief explanation of the evaluation process for DatalogP programs (for the details, see [9]) As described above, each fact derived by an IDB predicate or a query is accompanied by an event expression that describes the derivation of this fact from the underlying EDB facts. In order to compute the probability for an event expression, we use the socalled inclusion exclusion (or sieve) ....

Norbert Fuhr. Probabilistic datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

Applying the Annotation View on Messages - For Discussion Search (Correct)

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Fuhr, N. (2000). Probabilistic Datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110. 12

Uncertainty and Partial Non-Uniform Assumptions in Parametric .. - Loyer, Straccia (2002) (Correct)

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Norbert Fuhr. Probabilistic datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

sPLMap: A probabilistic approach to schema matching - Henrik Nottelmann And (2005) (Correct)

Default Knowledge in Logic Programs with Uncertainty - Yann Loyer And (2003) (1 citation) (Correct)

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N. Fuhr. Probabilistic Datalog: Implementing logical information retrieval for advanced applications. J. of the American Society for Information Science, 51(2):95--110, 2000.

An Enhanced Model for Searching in Semantic Portals - Zhang, Yu, Zhou, Lin, Yang (2005) (1 citation) (Correct)

Implementing Document Ranking within a Logical Framework - David Losada Ailab (2000) (Correct)

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N. Fuhr. Probabilistic Datalog: Implementing logical information retrieval for advanced applications. Journal of the American Society for Information Science, 51(2):95--110, 2000.

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