1534837
A Parallel Execution Model of Logic Program Based on Dependency Relationship Graph
39501622
A logic programming approach to hybrid knowledge representation
A new hybrid system called Sphinx is presented which combines classification-based reasoning with logic programming technique. It consists of two major components: a terminological component and an assertional one. The former uses a frame-based knowledge representation scheme based on classification and the latter is a theorem prover based on Horn logic. The assertional component can also handle a first-order query language and wh-questions. A new knowledge base maintenance mechanism based on the negation as failure inference rule enables Sphinx to support incremental assertions and retractions. In addition, Sphinx provides explanation capability to help the user in developing and debugging knowledge bases.
32944410
SPHINX - A Hybrid Knowledge Representation System
3145493
KPSP: A Knowledge Programming System based on Prolog
29923826
A Relational Dataflow Database Machine Based on Hierarchical Ring Network
46360239
A New Algorithm for Subsumption Test
147958
Effective page refresh policies for Web crawlers
In this article, we study how we can maintain local copies of remote data sources "fresh," when the source data is updated autonomously and independently. In particular, we study the problem of Web crawlers that maintain local copies of remote Web pages for Web search engines. In this context, remote data sources (Websites) do not notify the copies (Web crawlers) of new changes, so we need to periodically poll the sources to maintain the copies up-to-date. Since polling the sources takes significant time and resources, it is very difficult to keep the copies completely up-to-date.This article proposes various refresh policies and studies their effectiveness. We first formalize the notion of "freshness" of copied data by defining two freshness metrics, and we propose a Poisson process as the change model of data sources. Based on this framework, we examine the effectiveness of the proposed refresh policies analytically and experimentally. We show that a Poisson process is a good model to describe the changes of Web pages and we also show that our proposed refresh policies improve the "freshness" of data very significantly. In certain cases, we got orders of magnitude improvement from existing policies.
5763081
A probabilistic approach to metasearching with adaptive probing
An ever-increasing amount of valuable information is stored in Web databases, "hidden" behind search interfaces. To save the user's effort in manually exploring each database, metasearchers automatically select the most relevant databases to a user's query. In this paper, we focus on one of the technical challenges in metasearching, namely database selection. Past research uses a precollected summary of each database to estimate its "relevancy" to the query, and in many cases make incorrect database selection. In this paper, we propose two techniques: probabilistic relevancy modelling and adaptive probing. First, we model the relevancy of each database to a given query as a probabilistic distribution, derived by sampling that database. Using the probabilistic model, the user can explicitly specify a desired level of certainty for database selection. The adaptive probing technique decides which and how many databases to contact in order to satisfy the user's requirement. Our experiments on real hidden-Web databases indicate that our approach significantly improves the accuracy of database selection at the cost of a small number of database probing.
1720833
Automatic identification of user interest for personalized search
One hundred users, one hundred needs. As more and more topics are being discussed on the web and our vocabulary remains relatively stable, it is increasingly difficult to let the search engine know what we want. Coping with ambiguous queries has long been an important part of the research on Information Retrieval, but still remains a challenging task. Personalized search has recently got significant attention in addressing this challenge in the web search community, based on the premise that a user's general preference may help the search engine disambiguate the true intention of a query. However, studies have shown that users are reluctant to provide any explicit input on their personal preference. In this paper, we study how a search engine can learn a user's preference automatically based on her past click history and how it can use the user preference to personalize search results. Our experiments show that users' preferences can be learned accurately even from little click-history data and personalized search based on user preference yields significant improvements over the best existing ranking mechanism in the literature.
9362566
Estimating frequency of change
Many online data sources are updated autonomously and independently. In this article, we make the case for estimating the change frequency of data to improve Web crawlers, Web caches and to help data mining. We first identify various scenarios, where different applications have different requirements on the accuracy of the estimated frequency. Then we develop several "frequency estimators" for the identified scenarios, showing analytically and experimentally how precise they are. In many cases, our proposed estimators predict change frequencies much more accurately and improve the effectiveness of applications. For example, a Web crawler could achieve 35% improvement in "freshness" simply by adopting our proposed estimator.
513620
Synchronizing a database to improve freshness
In this paper we study how to refresh a local copy of an autonomous data source to maintain the copy up-to-date. As the size of the data grows, it becomes more difficult to maintain the copy \ fresh, “making it crucial to synchronize the copy effectively. We define two freshness metrics, change models of the underlying data, and synchronization policies. We analytically study how effective the various policies are. We also experimentally verify our analysis, based on data collected from 270 web sites for more than 4 months, and we show that our new policy improves the \ freshness” very significantly compared to current policies in use.