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Extracting and Delivering Stories from Heterogeneous Information Sources

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    Slide 1:Extracting and Delivering Stories from Heterogeneous Information Sources

    V.S. Subrahmanian, M. Fayzullin University of Maryland M. Albanese, C. Cesarano, A. Picariello Univ. of Napoli, Italy

    Slide 2:Talk Outline

    Motivating examples STORY Architecture Theoretical Model Algorithms OptStory DynStory GenStory Experimental results

    Slide 3:Motivating example: Pakistani Nuclear Scientists

    Nuclear proliferation is the issue of the day Complex web of Nuclear scientists Personnel at weapons locations Arms dealers Customs officials Shipping companies Front companies Manufacturers Nuclear monitors may want the story on any person or place or event to decide if further investigation is warranted. Huge amounts of data need to be processed and filtered so that only the relevant data is shown to the analyst.

    Slide 4:Motivating example: US Immigration

    Customs official sees a traveller. Wants the quick story on him Where does he work? Who does he work for? What is his area of expertise? Any warrants? Is he on a watch list? Who are his associates anyone suspicious? Just the right data should be presented to him.

    Slide 5:A motivating example: Pompeii

    Pompeii is a spectacular archaeological site. Visitor experience can be greatly improved by: Automatically notifying visitors of interesting phenomena without posting extra signs Allowing visitors to explore the stories of various monuments, paintings, sculptures, etc. in Pompeii. Allowing visitors to explore the stories of the characters, events and places depicted in these monuments, paintings, sculptures, etc. Visitors interests vary so information about exhibits must adapt in real time to their interests to enhance the experience of the visitor.

    Slide 6:Pompeii Visitors

    Visitor arrives at ticket counter and buys ticket.

    Slide 7:Pompeii Visitors

    Visitor arrives at ticket counter and buys ticket. ANALOG: Soldier in Baghdad sets out on a mission.

    Slide 8:Pompeii Visitors

    Ticket agent asks if they would like to use the story facility and if they would like to use their cell phone and/ or PDA to get stories of interest to them.

    Slide 9:Pompeii Visitors

    Ticket agent asks if they would like to use the story facility and if they would like to use their cell phone and/ or PDA to get stories of interest to them. ANALOG: Soldier in Baghdad chooses to receive stories on his radio or PDA.

    Slide 10:Pompeii Visitors

    As visitor walks through Pompeii, STORY identifies where he is and predicts where he might go in the future (probabilistically). Ex. if he is at location L, it might predict that he will go to the House of the Vetti.

    Slide 11:Pompeii Visitors

    As visitor walks through Pompeii, STORY identifies where he is and predicts where he might go in the future (probabilistically). Ex. if he is at location L, it might predict that he will go to the House of the Vetti. ANALOG: As soldier drives through Baghdad, STORY identifies where he is and correlates where he will go with his route plan.

    Slide 12:Pompeii Visitors

    Based on this prediction of where he might go in future, it identifies potential stories he might be interested in and downloads parts of these stories to his PDA/cell. E.g. It might download stories about Pentheus. See items You are here (Triclinium in the House of the Vetti)

    Slide 13:Pompeii Visitors

    Based on this prediction of where he might go in future, it identifies potential stories he might be interested in and downloads parts of these stories to his PDA/cell. E.g. It might download stories about Pentheus. See items You are here (Triclinium in the House of the Vetti) ANALOG: STORY finds stories satisfying the soldiers conditions of interest and downloads them to his PDA or to the nearest radio broadcast location.

    Slide 14:Pompeii Visitors

    The visitor chooses which story he is interested in. STORY dynamically generates the story and delivers it to the users PDA/cell phone, e.g. user might choose story of Pentheus.

    Slide 15:Pompeii Visitors

    The visitor chooses which story he is interested in. STORY dynamically generates the story and delivers it to the users PDA/cell phone, e.g. user might choose story of Pentheus. ANALOG: STORY delivers the story to the soldier. He can then further interact with the story if needed using voice and cursor prompts.

    Slide 16:Pompeii Visitors

    The user can choose to explore the story in greater detail (e.g. if he is seeing the story of Pentheus, he can also explore the story of Agave).

    Slide 17:Stories depend upon context

    The concept of story is dramatically different for the examples mentioned earlier. Pompeii Visitor cares about mythological, historical, artistic facts. Soldier in Baghdad cares about security and mission related facts. Who are the people around me and not who is depicted on the walls. Nuclear analyst cares about the nuclear networks who is selling what to whom? Who is moving the money? What front companies are involved? What goes into a story depends not only on basic facts about entity of interest but also on the application domain and specific items of interest to the user.

    Slide 18:STORY Architecture

    Slide 19:RDF Triples

    Consist of 3 parts An entity An attribute A value STORY also allows time-stamped values. attributes to have set-valued types. Example: Attribute: mother, Value: Agave Attribute: cartag, Value: AMD 124 Attribute: employers, Value = {ibm, hp }

    Slide 20:RDF Triples

    Consist of 3 parts An entity An attribute A value STORY also allows time-stamped values. attributes to have set-valued types. Time Varying Attribute (TVA) Example: attribute: job Value = { (cardinal, 1500,1509), (pope,1510,1545) } Example: Attribute: worked-for Value = {(ibm,1990,1998), (hp,1999,2004)}

    Slide 21:Story Schema

    A story schema is a pair (E,A) Examples Set of entities in Pompeii: Set of all objects in Pompei Set of all objects and events depicted Any entities related to the previous categories. Set of all people/organizations associated with Iraqi cars Set of all car ids Set of owners of such cars Set of people associated with such owners via one or many links. Unlike DBs, no need to declare schema in advance.

    Slide 22:Story Instance

    Not all attribute values needed for all entities. An instance w.r.t. story schema (E,A) is a partial mapping Input: an entity of E and an attribute of A Output: a value v in dom(A) if A is an ordinary attribute, or a timevalue if A is a TVA

    Slide 23:Extracting RDF from text

    Text needs to be parsed in order to understand its structure before extracting RDF triples Context free grammars to parse the text A set of template-based rules to extract triples from parsed text Rule can be derived from examples

    Slide 24:Generating rules from examples

    Rome is the capital of Italy Syntactic parsing Manually mark nodes corresponding to entities, attributes and values. Add alternatives for constant tokens (e.g. of | in) Validate and define extraction patterns (see next slide)

    Slide 25:Generating rules from examples

    Each extraction patterns define which marked node acts as the entity, which one as the attribute and which one as the value.

    Slide 26:Generating rules from examples

    The same node may act as the entity w.r.t. an extraction pattern, and as the value w.r.t. another extraction pattern.

    Slide 27:Triples extraction

    Each sentence is parsed, generating one or more parse trees. Each parse tree is matched against the parse tree that represents an extraction rule using a tree matching algorithm. If the match succeeds, the pieces of information corresponding to the marked template nodes are extracted and triples are built according to the extraction patterns. Probabilistic tree matching Algorithms in progress

    Slide 28:Example Iran is one of the most dangerous enemies of the United States

    Slide 29:Example Iran is one of the most dangerous enemies of the United States

    Allows 4 different interpretations, corresponding to different parse trees. All of the 4 parsing trees match the template 2 of them allow us to extract the triple: E=the most dangerous enemies of the United States A=one V=Iran 2 of them allow to extract the triple: E=the United States A=one of the most dangerous enemies V=Iran

    Slide 30:Example Hu Jintao is the most popular leader in China

    Slide 31:Example Hu Jintao is the most popular leader in China

    Allows 2 different interpretations, corresponding to different parse trees. The first parse tree doesnt match the template The second parse tree matches the template and allows us to extract the triple: E=China A=the most popular leader V=Hu Jintao

    Slide 32:How the system works

    The story application developer first specifies a set of data sources that are to be accessed, e.g. www a relational database an object oriented database database of web documents a set of URLs Some combination of the above. The STORY crawler extracts a full instance. Set of triples obtained from all sources specified by the user. Full instances dont resolve inconsistencies, generalize data, etc. Stories are then created on demand using the full instance and using appropriate conflict resolution, generalization, and other modules.

    Slide 33:XML sources

    Consider an XML node N= ? name,value,{c1,cn}> where {c1,cn}are children nodes Assuming that N is a root node in an XML document, and nodes may act both as entities and the attributes. e is an entity A is an attribute

    Slide 34:GetXMLAttr(N,e,A)

    GetXMLAttr(N,e,A) begin \\ Result := ? If N.value=e or N.name=e then for each child c of N such that c.name=A do Result := Result U {c.value } end for else for each child c of N do Result := Result U GetXMLAttr(c,e,A) end for end if return Result end

    Slide 35:CPR

    There are good stories and bad stories The STORY architecture supports the goals of succinctness and exploration and creates stories with respect to three important parameters: the priority of the story content, the continuity of the story, the non-repetition of facts covered by the story We want to deliver the most important facts to the intended audience. So far, we have focused primarily on priority and non-repetition, worrying less about continuity.

    Slide 36:CPR examples

    In the story of Pentheus, it makes more sense to first say that his parents were Cadmus and Agave, then say he reigned as King of Thebes, and then explain why he was killed. This rendering of the story is in chronological order, ensuring a kind of temporal continuity. Other measures of continuity are also possible within the STORY framework. A repetition function may evaluates how much repetition there is in a given story. For example, in the case of Pentheus, we may extract the fact that Agave is a parent of Pentheus, and that Agave is the mother of Penthus. Including both these facts in a story is repetitive as the latter fact subsumes the former.

    Slide 37:Story evaluation function

    eval(S)=?. ?(s)+?. ?(s) - ?. ?(s) ?, ?, ? are arbitrary functions from the set of all possible stories S about some entities to [0,1] ? describes whether high priority facts are included in the story. For example, the fact that Pentheus' mother was Agave is more important than the length of Pentheus' big toe. ? describes how continuous the story is. This means that a story should not jump wildly from one fact to another. ? describes repetition. clearly, stories that repeat the same or similar facts over and over again leave much to be desired.

    Slide 38:CPR functions

    There are many ways of defining how continuous a story is, how repetitive a story is, etc. Our story creation algorithms can work with any continuity, priority and repetition functions whatsoever.

    Slide 39:Attribute Hierarchy

    The attributes of interest are arranged in an attribute hierarchy where attributes can be labeled with priorities. The story application developer can browse and edit this hierarchy (for example if he wishes to add new attributes). He can add priorities to selected items in the hierarchy (all sub elements of a given element in the hierarchy will inherit the priority value for the parent unless otherwise stated).

    Slide 41:Conflict Management

    As multiple data sources may be used to extract attributes, conflicts might occur. For example, one source may say that Pentheus mother is Agave, while another may say it is Hera. STORY allows conflict resolution with an application specific method. Conflicts do not always need to be resolved. Sometimes, you just report the existence of a conflict, and specify what should be reported.

    Slide 42:Conflict Management Policy

    Temporal Conflict Resolution Suppose different data sources provide different values v1, , vn. Suppose value vi was inserted into the data source at time ti. In this case, we pick the value vi such that ti = max{ t1,t2, ,tn}. If multiple exist, one is selected randomly. Source based conflict resolution. The developer of a story may assign a credibility ci to each source si that provides a value vi for attribute A of entity e. This strategy picks value vi such that ci = max {c1,, cn}. If multiple exist, one is selected randomly. Voting based conflict resolution. Each value vi returned by at least one data source has a vote that represents the number of sources that return value vi. In this case, this conflict resolution strategy returns the value with the highest vote. If multiple vi's have the same highest vote, one is picked randomly and returned.

    Slide 43:Generalization Module

    Goal: to generalize multiple RDF triples into one. For example, if we know that Pentheus's father is Cadmus, and his mother is Agave, we may want to generalize this to say that Pentheus's parents are Cadmus and Agave. If Pentheus was king of one town for some period, king of another town for another period of time, and so on, we may merely want to say that Pentheus was king of many places. The Generalization Module looks at the RDF-triples stored in the RDF database and augments it with triples that include generalization attributes that succinctly summarize a set of less general (i.e. more specific) attributes.

    Slide 44:Generalized Story Schema

    A generalized story schema consists of a regular story schema, a function that associates an equivalence relation with each attribute domain and a function that associates a generalization function with each attribute domain. An equivalence relation on the domain dom(A) of attribute $A$ specifies when certain values in the domain are considered equivalent. For example, we may consider string values king and monarch to be equivalent in dom(occupation). For a time varying attribute we may consider (king,L,U) and monarch,L',U' to be equivalent independently of whether L=L and U=U' is true or not. Our system uses WordNet and some heuristics to infer equivalence relationships between terms. Generalization currently being plugged into the system.

    Slide 45:STORY creation

    Construct a story of length k or less from the RDF database. examining all triples in the RDF entity of interest, including triples extracted from the data sources by the attribute extractor as well as triples created by the generalization module. It then finds the k triples that optimize an objective function. The objective function must be monotonic in priority of the triples and monotonic w.r.t. the continuity function selected by the STORY application developer, and anti-monotonic in the amount of repetition between tuples.

    Slide 46:Closed Instance

    We first compute the full instance associated with our source access table. We then split this instance into equivalence classes using equivalence relation. Suppose the equivalence classes thus generated are X1, , Xn. For each equivalence class Xi we compute the generalization vi using the generalization function associated with attribute A. We insert the tuple (e,A, vi) into the full instance. This process is repeated for all entities e and all attributes A After all tuples of the form shown above inserted into the full instance, it becomes the closed instance.

    Slide 47:Story Computation Problem

    Given a closed instance I, a positive integer k, and an entity e as input, find a story of size ? k that maximizes the value of a given evaluation function eval. In this case, the found story is called on Optimal Story. Theorem: Finding an optimal story is NP-hard (even after the full instance is created).

    Slide 48:Story Algorithms

    OptSTORY algorithm: finds the story that optimizes the objective function. This algorithm has the disadvantage of being very slow. Multiple alternative BestSTORY algorithms DynStory(S) uses a dynamic programming approach GenStory(S) which is based on genetic programming. DynStory and GenStory find suboptimal stories, but do so very fast.

    Slide 49:GPS Support Subsystem Current implementation

    Outdoor positioning at Pompeii implemented using DGPS Mobile devices are equipped with IEEE 802.11b wireless Ethernet to allow internet connection

    Slide 50:GIS Support Subsystem Outdoor and indoor positioning

    Outdoor positioning GPS has been successfully adopted in a lot of applications Indoor positioning GPS receivers are blind in indoor spaces Different kinds of positioning systems will be used Infrared or ultrasound sensors Radio Frequency sensors WLAN-based positioning We have methods to optimally position a set of sensors to monitor the site, but the system is not yet implemented.

    Slide 51:STORY presentation

    Our STORY architecture applies to several different hardware options our current implementation works for both PDAs and laptops. Multiple languages we currently support English, Spanish and Italian. Multiple output rendering via a graphical user interface or via speech

    Methods to merge multiple such sentences into one are being implemented.

    Slide 54:STORY Experiments

    Parameters to be evaluated Value of the facts included into the stories Quality of the prose (does it read nicely) Experiments plan 61 students enrolled as reviewers 51 non experts (no a priori knowledge about the subjects of the stories) 10 experts (a priori knowledge) Facts and prose evaluated for Different algorithms Different rendering techniques Different CPR parameters settings Different lengths of the stories

    Slide 55:Value of the facts vs. length of the story: Trends

    Slide 56:Value of the facts vs. length of the story: Considerations

    Highest Priorities: GenSTORY (version 1: using original sentences from sources if available instead of only using templates) wins Runner up is DynSTORY (version 1) Even if we ignore how the stories are rendered, GenSTORY still wins. Including the original sentences in the story adds more information content than rendering the same fact through a template.

    Slide 57:Quality of the prose vs. length of the story: Trends

    Slide 58:Quality of the prose vs. length of the story: Considerations

    The quality of the prose is high and seems independent of the algorithm used Quality of prose decreases as the story length increases (not surprising). Including sentences from text sources into stories improves story quality.

    Slide 59:Value of the facts and quality of the prose: Summary

    Slide 60:Value of the facts vs. CPR parameters: Trends

    Slide 61:Value of the facts vs. CPR parameters: Considerations

    Best value of facts is obtained when the priority is set to a high value Users are more interested in priority than in continuity and repetition Repetition is to avoid when the length of the story is very short For low values of L the best results are obtained when R is set to a high value