Fuzzy  Web  Knowledge  Aggregation,  Representation,  and  Reasoning  for  Online  Privacy  and  Reputation  Management  

Edy  Portmann1  &  Witold  Pedrycz2  1Department  of  EECS,  University  of  California,  Berkeley,  California,  USA  

2Department  of  ECE,  University  of  Alberta,  Edmonton,  Canada  and      Systems  Research  Institute,  Polish  Academy  of  Sciences,  Warsaw,  Poland

Abstract      A  social  Semantic  Web  empowers  its  users  to  have  access  to  col-­‐lective  Web  knowledge   in  a   simple  manner,   and   for   that   reason,   controlling  online  privacy  and  reputation  becomes   increasingly   important,  and  must  be  taken  seriously.  This  chapter  presents  Fuzzy  Cognitive  Maps  (FCM)  as  a  vehi-­‐cle  for  Web  knowledge  aggregation,  representation,  and  reasoning.  With  this  in  mind,  a  conceptual  framework  for  Web  knowledge  aggregation,  represen-­‐tation,   and   reasoning   is   introduced   along  with   a   use   case,   in  which   the   im-­‐portance  of  investigative  searching  for  online  privacy  and  reputation  is  high-­‐lighted.  Thereby  it  is  demonstrated  how  a  user  can  establish  a  positive  online  presence.  

Acknowledgments      We  are  grateful   to  our  colleagues  Lotfi  A.  Zadeh  and  Sergio  Guadar-­‐rama  for  their  valuable  thoughts.  Under  grant  number  PBFRP2-­‐138628,  the  Swiss  National  Science  Foundation  supported  preparations  of  this  chapter.    

1. Introduction    

As nowadays more and more humans are social media literate, we bear down on an interconnected information space. However, there are limits to social applica-tions such as blogging, social networks and wikis, concerning information integra-tion, dissemination, reuse, portability, searchability, automation and more chal-lenging tasks like querying. This is where semantic applications come into play, in which knowledge is expressed in a computer-understandable language. With the intention to enable computers to grasp meaning as humans do, the Semantic Web includes layers built on the current Web, which describes concepts and relation-ships, following logical rules [7]. The vision is to provide humans automated as-sistance in everyday tasks, for example through incorporating respective Semantic Web technologies into social applications.

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From the point of logic, however, it is known that no system could ever be both consistent and complete and that no sufficiently rich interpreted language can fully represent its own semantics [13, 40]. To make the matter worse, there even exists a class of NP-complete problems that basically cannot be solved efficiently by current computers [43]. In contrast, human beings seem to thrive on this; they live and make decisions without knowing all the facts. What is striking is that all these human activities, to a great extend, entail imprecision, uncertainty, and partial truth.  Characterized by Web users needs to have the power to define themselves online (and in life), and by the fact that evermore uncontrolled information is available, in a forthcoming social Semantic Web, a user should be endorsed to manage his online privacy and reputation. To do this reliably, the Semantic Web has to over-come its limitations, by dealing with approximations [47]. On this account, deriv-ing fuzzy meaning from the massive amount of online discussions, and gain a deeper understanding [32] is a key concern of media applications in a social Se-mantic Web. This Web is an enhanced Semantic Web that combines its vision with socially ac-cepted applications. Among other things, its applications admit facilitated infor-mation integration, dissemination, reuse, portability, searchability, automation and querying. Web users learn by conversations, and create new knowledge in the con-text of it. A social Semantic Web allows computers to help users create even more knowledge and also to learn from each other (more efficiently). On one hand, computers in the Semantic Web collect, store, remember, search, and combine us-ers Web data, and on the other hand, they draw logical conclusions from and make predictions about these data [15]. Generally speaking, techniques for a social Se-mantic Web should resemble organic processes more closely than traditional tech-niques, which are largely based on mathematical logic systems, and which strive for exactness and full truth. Hence, unlike these systems as todays Semantic Web is built on, social Semantic Web applications exploit the given tolerance of impre-cision, partial truth, and uncertainty [32]. Using Fuzzy Cognitive Maps (FCM) to address Web knowledge aggregation, rep-resentation, and reasoning, in this chapter we present an approximated, yet practi-cable and robust framework as solution to seize the presented requirements of online privacy and reputation management. The next section provides background knowledge around a social Semantic Web we strive for. Section 3 introduces the notion of using investigative searches for online privacy and reputation manage-ment. In section 4, we present the Web knowledge aggregation, representation, and reasoning framework. Based on FCM techniques, approximated ontologies can be derived that quasi represent the Semantic Web’s knowledge. Through in-teractive visualization, an aggregated FCM allows investigative seekers for online privacy and reputation browsing netlike knowledge structures, and support their reasoning. Finally, conclusion and future research directions are outlined in sec-tion 5.

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2. Towards  a  social  Semantic  Web  

This section illustrates the evolution of the Web to a social Semantic Web. Step-by-step we explain how to tackle today’s Semantic Web issues using FCMs; to that end, subsection 2.1 introduces into the Semantic Web; subsection 2.2 into Web agents; subsection 2.3 in FCMs; and subsection 2.4 brings together the pre-vious parts.  

2.1. The  Semantic  Web  

The Semantic Web is “an extension of the current Web in which information is given well-defined meaning, better enabling computer and people to work in co-operation.” [7]. The prefix semantics thereby suggests computer understanding of the meaning of the human-readable Web content. Yet, to teach computers mean-ing, the difficulty is defining relations from the syntactic structure of a human lan-guage to an appropriate model [38]. To tackle this issue, typically ontologies are used. In conventional sense, these ontologies explore the nature of being, exist-ence, or reality, as well as the basic categories of being and their relations. They cover topics like what entities exist or can be said to exist, and how such entities can be grouped, related within a hierarchy, and subdivided according to similari-ties and differences. In the Semantic Web “ontologies are conceptual models that capture and make explicit the vocabulary used in a domain or in a semantic application, thereby guaranteeing the absence of ambiguities.” [9], and Gruber adds, that “an ontology is readable for both humans and machines. Together with a syntax and semantics, it provides the language by which knowledge-based systems can interoperate, e.g. exchanging assertions, queries, and answers. The ontology determines what “ex-ists” for a system.” [14]. More formally, an ontology 𝑂 ∈ 𝐷 (i.e., domain) con-tains different concepts 𝐶, the properties 𝑃 of the concepts 𝐶, as well as the rela-tionships 𝑅 that exist between the concepts 𝐶. In such an ontology, the meaning of a symbol is expressed in a specific language 𝐿 [10]. The outcome is an index of the types of things that are assumed to exist in 𝐷 from the perspective of a user who uses 𝐿 for the purpose of talking about 𝐷. The types of 𝑂 represent the predi-cates, word senses, or concept and relation of 𝐿 when used to discuss topics in 𝐷. It is a description (i.e., a formal specification of a program) of the domain, as it ex-ists for a user or an agent. In the Semantic Web several distinct ontology lan-guages emerged; examples are RDF1, RDFS2, FOAF3, XTM4, DAML+OIL5, and OWL6.

                                                                                                               1  http://www.w3.org/RDF/  2  http://www.w3.org/TR/rdf-­‐schema/  3  http://www.foaf-­‐project.org/  

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In order to integrate data, information, and knowledge from several ontologies, agents are needed that merge the individual ontologies to a new ontology 𝑂!"# [11]. In this sense, data (showing at the lowest level of abstraction), information (i.e., intermediated level), and knowledge (i.e., highest level among all three) can be shared as well as reused across applications, enterprises, and community boundaries [35]. The next subsection looks at Web agents more precisely, which are used to collect data, information, and knowledge from Semantic Web sources.

2.2. Semantic  Web  Agents  

In the last years, the volume of the Web increased, what led eventually to modern Web search engines prevalence, due in no small parts to Web agents [4]. The Web remains intelligible to its human users because it is steadily analyzed and moni-tored by these automatic agents. Reactive agents perform unsophisticated behavior patterns, whereas cognitive agents have considerable better-elaborated behavior patterns (e.g., the ability to pursue goals, plan actions and to negotiate with other agents to reach objectives, etc.). The reactive agents strength, however, is their ca-pability of protruding an emergent global behavior beyond the behavior of indi-vidual agents [11]. Today, Web crawlers are basically used to create copies of visited Web pages for later processing by a search engine that index the downloaded pages to provide users fast searches. A crawler takes as input a set of starting pages that are down-loaded, parsed and scanned for new links. The links pointing to pages that have not yet been downloaded are superimposed to a central queue of URLs for a later retrieval. Following a set of rules, the crawlers select a new page from the queue to download and to process, is repeated until reaching a (potential) stop criterion. In order to prepare an ontology, a Web crawler needs to be extended and should not only collect URLs but also other data used. The notion of crawlers can be em-ployed to aggregate existing ontologies in the Semantic Web; examples are the DAML Crawler7, the RDF Crawler8 or FOAF Scutter9, and the OCRA Crawler10. These crawlers imply an add-on to traditional reactive Web agents with cognitive behavior. The aggregated ontologies now need to be aligned following a process, where cor-respondences between ontologies are determined. Given a set of 𝑛 crawled ontol-

                                                                                                                                                                                                                                                             4  http  http://www.isotopicmaps.org/sam/sam-­‐xtm//  5  http://www.w3.org/TR/daml+oil-­‐reference  6  http://www.w3.org/TR/owl2-­‐primer/  7  http://www.daml.org/crawler/  8  http://ontobroker.semanticweb.org/rdfcrawl/index.html  9  http://code.google.com/p/slug-­‐semweb-­‐crawler/  10  http://www.mindswap.org/~golbeck/downloads/ocra.shtml  

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ogies 𝑂 = 𝑂!, . . . ,𝑂! possibly stored at different locations on the Web and in different formats are matched against a new ontology 𝑂!"#. In doing so wrappers 𝑊 = 𝑊!, . . . ,𝑊! help to transform the different formats of the ontologies to the new global ontology 𝑂!"# (see fig. 1).

Fig. 1: Ontology Alignment Using Semantic Web Agents

In order to answer imprecision, uncertainty, partial truth, and approximation, the ontology alignment process should go back to the use of inexact solutions to com-putationally hard tasks (see next subsection).

2.3. Fuzzy  Cognitive  Maps  

In a distributed and open system as the Semantic Web, it is impossible to avoid heterogeneity of concepts [11]. So what is needed are means automatically re-balance these issues to align ontologies in an organic manner. This means should be powerful enough to negotiate meaning in a real-life approximated context, somehow similar as humans do in everyday communications [34]. For this pur-pose, fuzzy logic ���seems eminently suitable, because it mimics human behavior [44], resulting in an enhanced human-computer relationship (i.e., the computer can respond to user in a human way). A combination of fuzzy logic with cognitive maps yields structures that resemble neural networks (i.e., imitating human neu-rons). Incidentally, the credit for the creation of cognitive maps is often given to Tolman [41], although the concept dates back to 1913 [42]. To represent social-scientific knowledge, in the seventies Axelrod used an interconnected diagraph [3]. Intro-duced by Kosko [19], primarily with semi-quantitative attributes [2], a Fuzzy Cognitive Map (FCM) is a directed graph with concepts 𝐶 as nodes and causalities as edges, representing causal relationship between concepts 𝐶. Unlike ordinary neural networks, however, FCMs boast high transparency [25]. For an overview of the concept, we may refer to [12, 28]. Using fuzzy logic’s epistemic facet [45] al-lows defining fuzzy ontologies 𝑂 with expressions from natural language 𝐿 [32]. When the nodes (i.e., concepts) are fuzzy sets then they are called fuzzy nodes.

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The causalities represent the relationships 𝑅 with properties 𝑃 (see subsection 2.1). However, FCMs that restrict causalities (i.e., edge weights 𝑤) to the set −1,0,1 are called simple (see fig. 2); thereby the value of -1 represents full

negative, +1 full positive, and 0 denotes neutral relation [2, 24]. Note that using 𝛼-cuts [48], complex FCMs can be transformed to simple FCMs.

Fig. 2: A Simple Fuzzy Cognitive Map

The next subsection highlights how FCMs can be used in order to make the Se-mantic Web more social.

2.4. From  a  Semantic  Web  to  a  social  Semantic  Web  

This subsection is about the Web evolution to a more social Semantic Web, which combines the Semantic Web vision with socially accepted applications. In order to enable computers better meet human users (e.g., through adaptive interfaces [32, 36]), a social Semantic Web draws on techniques that resemble organic processes more closely than traditional ones. To adaptively address individual human user, an intelligent interface exploits carefully the human-inherent tolerance of impreci-sion, partial truth, and uncertainty [46].

Fig. 3: Fuzzy Cognitive Maps to Bridge the Gap Between Human and Computers

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In the social Semantic Web, FCMs can be used to bridge the gap between a hu-man-centered Web and the technique-centered Semantic Web (see fig. 3): Firstly, based on expert’s opinion (see, e.g., [26, 27]) that is ready and stored in Semantic Web ontologies (see, e.g., [11]), they are very simple, thus leading to a quicker so-cial acceptance than more complicated systems. Secondly, in an information inte-gration process the different concepts of the Semantic Web ontologies only need to be crawled and wrapped to FCMs, and eventually matched against another FCM. Thereby the combined 𝐹𝐶𝑀!"# naturally averages the FCMs and their cor-responding causal descriptions as well as much of their dynamics [20]. Thirdly, as a technique that exposes hidden patterns, when the data happens to be unsuper-vised, FCMs become reasonable practicable [24]. Through an interactive visuali-zation of the aggregated Web knowledge as FCMs in a human-computer interac-tion process, a Web user can investigate hidden knowledge patterns [17, 32] eventually allowing reasoning for human and computers (i.e., there is already software that automatically supports human reasoning process such as EYE11, Je-na12, OpenCyC13, Pyke14, etc.). Allowing information integration, dissemination, reuse, portability, searchability, automation, and querying, these are all required features of an online privacy and reputation management application. Most of these application start with a Web (reputation) search engine that can benefit from FCMs. The next section introduc-es into online privacy and reputation management, and underlying idea of investi-gative search.

3. Investigative  Search  for  Online  Reputation  Management  

This section introduces FCMs in online privacy and reputation management; sub-section 3.1 presents online privacy and reputation management that is becoming increasingly important in a social Semantic Web; and subsection 3.2 introduces the notion behind investigative Web search for online privacy and reputation man-agement.

3.1. Online  Privacy  and  Reputation  Management  

Since all organisms seek sustenance and propagation, searches deeply affect our lives [21]. With the advancement of computers to consumer products and the evo-lution of the Internet as mass medium [22], searching the Web evolved into a daily                                                                                                                11  http://eulersharp.sourceforge.net/2003/03swap/eye-­‐note.txt  12  http://jena.apache.org/  13  http://www.opencyc.org/  14  http://pyke.sourceforge.net/index.html  

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activity for everyone. Built on crawlers, the Web made searches a lot easier, and, in turn, users put a lot more of their life in the Web. Since today’s life is defined by search results, however, search engines results can include information that is irrelevant or out of context [6]. Modern Web search engines imperil users to quickly rank negative or slanderous results. If one does not protect, someone can post a comment, make a video, create a blog post, file a complaint, promote a competition, and develop a hate site or worse [33]. These search results can affect negatively a business or career, if the information is falla-cious or misleading (e.g., false accusations or exaggerated negative reviews, etc.). Because it is habitually obscure how many have seen that misinformation online or how many have passed along this information offline the extent of the damage can only be estimated. Web users have to realize that they must play their part in protecting their privacy [5]. Since current laws are insufficient to prevent damaging a users reputation or harvesting private information (e.g., without adequate protection, personal infor-mation could be sold by information traders), online privacy and reputation man-agement is critical. For this reason a monitoring of a users reputation is advisable, and should not be understood as impudent promotion [6]. Originating from the mindset that everyone should have the power to define himself in life (as well as online), this is rather a form of (self) defense and best practice. When someone is chasing a dream school, job or relationship, the first impression will probably be made online. The following subchapter introduces the concept of investigatory search as basis for identifying online privacy and reputation. Shaped according Web search en-gines principles, a reputation search engine can help monitor online reviews from all over the Web, and subsequently establish a positive Web presence [33]. Therewith one can straightforwardly detect personal information from websites that collecting some and help to protect privacy and identity. On this basis it is possible to set the records straight by pushing down false and misleading search results with positive material in one’s own control.

3.2. Investigative  Web  Search  Engines  

When a user enters a query, a Web search engine examines its Web crawler-produced index (see subsection 2.2) and provides a list of best-matching websites. The usefulness of a search engine depends on the relevance of the returned web-sites. Traditionally recall (i.e., the number of relevant objects that are retrieved from Web data) and precision (i.e., the number of retrieved objects that are rele-vant) are used as quality measurement of a search engine. Yet, Marchionini [21] makes a significant distinction between lookup searches (i.e., traditional view of searching the Web) and exploratory search (i.e., new view that also addresses more investigative searching the Web). This searching investigates more in recall (i.e., maximizing the number of possibly relevant objects that are retrieved) than

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precision (i.e., minimizing the number of possibly irrelevant objects that are re-trieved). In investigative search, different levels of interaction for automatic query reformu-lation are studied that leverage the traditional measurements of precision and re-call but apply them to the results of multiple iterations of user interaction, rather than to a single query response. Ingwersen defines this as “interactive communica-tion processes that occur during the retrieval of information by involving all the major participants in information retrieval […], i.e. the user, the intermediary, and the IR system.” [16]. In an investigative search it is crucial to study the inter-actions of Popper’s second and third world, to describe and explain them if they can and so to help in organizing knowledge (rather than data and information) for more effective use [30]. Most systems, however, point to data and information, giving physical access via representations to world one objects [16, 30]. Investiga-tive search covers a broad class of activities, such as exploring, evaluating, com-paring, and synthesizing. Seekers in an investigative search process generally combine querying and browsing strategies to foster investigation (and learning) [21]. Using FCMs it is possible to bring Popper’s three worlds together [30]. In the next section, FCMs are employed as help in investigative search for a Web users online privacy and reputation. Thereby aggregated FCMs act as approximated causal Web knowledge (i.e., top-level ontology) that can interactively visualized (e.g., for investigative search), and even support human user with reasoning.

4. Fuzzy  Cognitive  Maps  in  the  social  Semantic  Web  

This section presents the Web knowledge aggregation, representation, and reason-ing framework; with this in mind, subsection 4.1 introduces into the conceptual framework; subsection 4.2 illustrates the aggregation component; subsection 4.3 the representing component; and subsection 4.4 the reasoning component. Last but not least, a short use case for this framework is presented in subsection 4.5.

4.1. Fuzzy  Cognitive  Maps  for  Knowledge  Aggregation,  Representation,  and  Reasoning  

In the Semantic Web different actors have different interests and habits, use dif-ferent tools and knowledge, and most often, at different levels of detail. These many reasons for heterogeneity lead to diverse forms of knowledge, and, there-fore, should be considered in investigating online privacy and reputation. Accord-ing to Ingwersen, world knowledge consists of knowledge structures or cognitive structures, which are „determined by the individual and its social/collective expe-riences, education, etc.“ [16]. The connections and influences between individuals

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and social/organizational knowledge, goals and purpose, preferences, as well as expectations and experiences, are thus reflected in the underlying ontologies. Yet, Web crawlers can aggregate knowledge about a specific domain 𝐷. At the same time, these agents permit a wrapping of the relevant ontologies 𝑂! to FCMs, 𝐹𝐶𝑀!. Afterwards an alignment of different FCMs leads to the creation of a new FCM from possible overlapping submodels. Thereby the initial ontologies remain unaltered. The aligned 𝐹𝐶𝑀!"# (see fig. 4) is supposed to contain the knowledge of the respective ontologies (e.g., consequences of each ontology are consequenc-es of the alignment). Using FCMs, we can calculate an approximated FCM.

Fig. 4: Web Knowledge Aggregation, Representation, and Reasoning Framework

Figure 4 illustrates the Web knowledge aggregation, representation, and reasoning framework that can be used to monitor online privacy and reputation. Based on this framework, it is possible to manage online reputation and to establish a trans-parent Web presents [6] that gets someone noticed for the right reasons. There-with, using investigative search techniques, supported by FCMs, it is possible to detect personal information from websites the easy way, and help to protect priva-cy and identity. In the next subsections the three main components of this frame-work are specified; subsection 4.2 illustrates knowledge aggregation with FCMs, subsection 4.3 knowledge representation using FCMs, and subsection 4.4 support in reasoning based on FCMs.

4.2. Knowledge  Aggregation  using  Fuzzy  Cognitive  Maps  

A FCMs aggregation yields a new FCM model, 𝐹𝐶𝑀!"#, as a merge of submod-els. The development of a FCM often occurs within a group context. In the Se-mantic Web, ontologies are developed free and/or in a group context. The FCM

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underlying assumption is that combining incomplete, conflict opinions of different experts may cancel out the effect of oversight, ignorance, and prejudice [1]. The simplest method is to establish the candidate FCM by averaging the corresponding relationship values (i.e., weights 𝑤) across all the submodels. Using the FCMs matrix representation, this is carried out by calculating averages of the correspond-ing cells across all the submodels. A finite number of FCMs can be combined together to produce the joint effect of all underlying maps. Let 𝐸!,𝐸!,… ,𝐸! be the adjacency matrices of the maps with 𝐶!,𝐶!,… ,𝐶!, then the combined FCM is obtained by summing up all adjacency matrices 𝐸!,𝐸!,…𝐸!. The combined map’s adjacency matrix is labeled by 𝐸 = 𝐸!

!!!! , with 𝑝 as the number of adjacency matrices and 𝑛 as the number of

concepts (see section 2). According to Stach et al., this standard technique can be improved by adding credibility to each submodel [39]. Accordingly, credibility 𝑐𝑟 can be computed using in-sample error values (i.e., difference in the original data). The in-sample error assesses the quality of submodels; submodels with low in-sample error obtain higher credibility, and vice versa. Specifically, given 𝑆 sub-models and their in-sample values, the aggregation is realized using

𝑒!" =𝑐𝑟!×!

!!! 𝑒!"!

𝑐𝑟!!!!!

where 𝑐𝑟! is the credibility of 𝑠th submodel (i.e., calculated as 1-in-sample error of the submodel); 𝑒!"! the weight between concept 𝐶! and 𝐶! in the 𝑠th sub-model. Using FCMs, the next subsection illustrates the interactive visualization of the ag-gregated knowledge.

4.3. Knowledge  Representation  using  Fuzzy  Cognitive  Maps  

Through dynamic query interfaces [36] humans are able to adapt to computers and through machine learning techniques (i.e., neural networks) computers can (better) adapt to humans. To support everyday people in their search, dynamic query inter-faces should fit in to single users knowledge and to head of each user there. To do this, the computer should rely on machine learning to defer to (average) users, which uses his language in a natural way. Through dynamic interfaces that inte-grate digital content into a human’s live in seamless ways, a computer should even become adaptable to each individual user (see, e.g., [31]). With an automatically built-in ontology, computers for example may become (more) responsive to humans. The aggregated FCM model, (together with any un-derlying submodels crawled and wrapped) includes fuzzy meaning of human world knowledge that can be used to bring together human and computers [18]. FCMs are qualitative alternative to dynamic techniques that allows observing the gross behavior quickly and without expert’s help [1].

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Let us consider concepts 𝐶!,𝐶!,… ,𝐶! in a simple aggregated FCM model, 𝐹𝐶𝑀!"#. Suppose the graph is drawn using edge weight 𝑒!" ∈ 0,1,−1 . The ad-jacency matrix 𝐸 = 𝑒!" where e!" is the weight of the directed edge 𝐶!𝐶! (note that all matrices are always square matrices with diagonal entries as zero). If 𝐶! is a concepts of a FCM, then an instantaneous state vector 𝐴 = 𝑎!, 𝑎!,… , 𝑎! where 𝑎! ∈ 0,1 denotes an on-off position of the concepts at an instant. Hence, for 𝑖 = 1,2,… , 𝑛:

𝑎! =  0, 𝑖𝑓  𝑎!  𝑖𝑠  𝑜𝑓𝑓1, 𝑖𝑓  𝑎!  𝑖𝑠  𝑜𝑛    

Let 𝐶!𝐶!,𝐶!𝐶!,𝐶!𝐶!,… ,𝐶!𝐶! be the edges of the FCM 𝑖 ≠ 𝑗 , then the edges con-stitute a directed cycle. A FCM is cyclic if it possesses a directed cycle, and acy-clic otherwise; a FCM with cycles is said to have feedback and is called dynamic [17]. Now, 𝐶!𝐶!,𝐶!𝐶!,… ,𝐶!𝐶!!! is a cycle; if 𝐶! is on (i.e., causality flows through the edges of the cycle) and causes 𝐶!, the dynamic system is said to go round and round (i.e., true for any 𝐶!, with 𝑖 = 1,2,… , 𝑛). However, these FCMs (i.e., each single 𝐹𝐶𝑀! as well as 𝐹𝐶𝑀!"#) can be used as simple representation of knowledge. Users through a provided adaptive interface now can investigate this aggregated Web knowledge structures. Today, from their perspective, browsing and searching are the main interactions on the Web [18]. While browsers provide visual mechanisms for navigating the Web, in many cases search engines are the source where a Web navigation process starts. In future, a search engine should provide the possibilities of searching and browsing not only the retrieved Web documents, but also the underlying meaning. For instance, in addition to only browse from a found Web document via link to another docu-ment, a user’s search is extended by the possibility to follow a link into visualized knowledge structures (i.e., FCMs; see fig. 3). Therein the user navigates the net-like structures for some time, and then follows back a link to a different Web doc-ument, as [8] illustrate.

4.4. Knowledge-­‐based  Reasoning  using  Fuzzy  Cognitive  Maps  

In the Semantic Web exist numerous formal reasoner for (more or less simple) de-scription logic-based reasoning (see section 2). In the vision of a social Semantic Web, these reasoners should support user in their reasoning (e.g., in a investigative Web search for online privacy and reputation; see section 3). This support, how-ever, exhibits an initial form of users cognitive augmentation. Via FCMs it is possible to aggregate ontologies from collective knowledge sys-tems. According to Gruber are this “human-computer systems in which [comput-ers] enable the collection and harvesting of large amounts of human-generated knowledge.” [15]. In a cognition augmentation interaction process, FCMs can on one hand be used to illustrate coherencies, and on the other hand they can be used

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in an automatic reasoning. Here as well, there are first versions of fuzzy reasoning architectures that should help users in their reasoning process at a later time (see, e.g., [29, 37]). For example, such a reasoner can ascertain equilibriums in the FCM. An equilibri-um in this dynamical system is called the hidden pattern. If the equilibrium is a unique state vector, then it is called a fixed point, if the map settles down with a state vector repeating in the form 𝐴! → 𝐴! → ⋯ → 𝐴! → 𝐴! then the equilibrium is called a limited cycle. However, in the sense of human’s cognitive augmenta-tion we focus here more on the visualized knowledge aspects through FCMs. Compared to other systems, FCMs are relatively easy to use for representing structured knowledge, and the inference can be computed by numeric matrix oper-ation instead of explicit if-then rules [1]. FCMs are appropriate to explicit the knowledge and experience which has been aggregated.

Fig. 5: Interactive Knowledge Representation

In exploring the knowledge structures, additional functionalities (e.g., zoom, drag-and-drop, etc. [32]) to standard functionalities (i.e., clicking hyperlinks in brows-ing process; see previous subsection) are conceivable. Using such functions allows users to investigate efficiently online privacy and reputation related knowledge, information, and data (see fig. 5). So it is for example possible that with the zoom function a special ontology 𝑂!(= 𝐹𝐶𝑀!) can be zoomed in. After a closer investi-gation, a user can zoom out again to the aggregated ontology 𝑂!"#(= 𝐹𝐶𝑀!"#). In combination with a drag-and-drop function, this allows users a knowledge-based reasoning, which in future even can be supported by improved, user-adapted automatic reasoned [29, 37]. In conclusion, the next subsection illustrates the presented framework by means of a brief use case.

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4.5. Tim  Berners-­‐Lee  Use  Case    

Imagine that, to analyze his Web presence, Tim Berners-Lee wants to know all corresponding references. Hence, to spot them, a sound analysis should also in-clude weak signs (e.g., not directly name-checked by an underlying websites), and, in addition, the found issues should be somehow summarized (e.g., into relat-ed topics). Yet, a challenge for a search for new and unobserved Web information is that the relationship between terms and topics is often unknown in advance. To scan all his (more or less related) mentions, Tim first searches for his name. After hitting the search button, on the left side of his browser window, immediate-ly an ontology (i.e., the 𝐹𝐶𝑀!"# including all aggregated 𝐹𝐶𝑀!’s) pops up (see fig. 3). This 𝐹𝐶𝑀!"# captures the vagueness of aligned human concepts [33] (i.e., stored in all the individual underlying ontologies 𝐹𝐶𝑀!). Besides, on the right side of his browser, a list (e.g., ranked by relevance, trust, credibility, etc.) pops up that entails sorted Web hits (see also fig. 3). This way Tim can browse the 𝐹𝐶𝑀!"# (i.e., Popper’s second and third world; see section 3), and more importantly, also interactively discover underlying social media applications (i.e., Popper’s first world) at one go. The advantage is that Tim not only finds more relevant information concerning his search, but also more structured information (based on 𝐹𝐶𝑀!"# and the respective hit list). With a conventional Boolean search, he would only find information posi-tively containing his name. In contrast, our framework enables us, to find not only the search term but also related topics. Since the revealed browser window is in-teractive, Tim may also zoom in-and-out as well as drag-and-drop (see fig. 5) and, that way, browse the FCM in a straightforward manner (e.g., as described in [32, 33]). That permits him to do an integrated analysis of his Web presence (e.g., his online privacy and reputation), and to reason about his very own situation (e.g., supported by automatic reasoners as presented in section 2). As last point, the next section first provides a short summary, followed by a con-clusion and an outlook of further studies.

5. Conclusions  and  Outlook  

First we have introduced the intended social Semantic Web that empowers its us-ers to have collective Web knowledge at hand. It is composed of a human-centric social Web and the technic-centered Semantic Web. In the end, the Semantic Web becomes more social, or vice versa, social applications will be augmented with computer-understandable meaning of real-life concepts. In a social Semantic Web, controlling online privacy and reputation becomes increasingly important, and must be taken seriously. Investigative searches may reveal hidden knowledge of the underlying ontologies that can be used for managing online privacy and repu-tation efficiently. Based on FCMs, the Web aggregation, representation, and rea-soning framework, allows an information integration, searchability, automation

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and querying. In addition to conventional ordinary Web information found, the netlike knowledge structures of the aggregated FCMs can also be searched and browsed. To support human reasoning, it is furthermore possible to zoom-in-and-out, as well as drag-and-drop these FCM knowledge structures. Our interactive, visual, human-oriented approach to knowledge presentation should help an information seeker understand, or get to know, and (thus) express information needs, formulate queries straightforwardly, select from available in-formation, understand results by browsing not only the retrieved Web documents but also the semantic relations of the document’s content, and keep track of the progress of a search. The quality of the interface depends strongly on how Web users react to it. Its preferences may be given through different factors, such as speed of the underlying framework or familiarity and aesthetic realization of the inductive fuzzy cognitive maps, and so on. All these points are to be evaluated in a further step, using discount usability methods, as suggested by Nielson [23]. To this end, first prototypes for fuzzy Web knowledge aggregation, representation, and reasoning are at the early stages of development. We realize that although progress has been made in ontology alignment, it still may appear to be virtually impossible. Indeed, for finding correspondences be-tween concepts, it is necessary to approximate their meaning. The ultimate mean-ing of concepts is in the head of the people who developed those concepts and, originating from mathematical logic, we are unable to fully program computers to learn it par for par. However, the same remark leads to the conclusion that com-munication, even between people, is impossible [34]. Yet, we know that human beings achieve communication; they at least, succeed quite often in communi-cating and sometimes fail. Achieving this communication can be viewed as a con-tinuous task of negotiating the relations between fuzzy concepts (i.e., arguing about alignments, building new ones, questioning them, etc.). Therefore, aligning ontologies is an on-going work and considering it in its dynamics can make fur-ther substantial progress in the field.

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